An ex ante impact assessment of a Striga control programme in East Africa

Transcription

1 An ex ante impact assessment of a Striga control programme in East Africa INTERIM REPORT Submitted to KILIMO TRUST Written by Dr. Laban MacOpiyo, Dr. Jeffrey Vitale, and Dr. John Sanders 0 P age

2 Contents EXECUTIVE SUMMARY INTRODUCTION Food Crisis Yield Gaps METHODOLOGY Quantification of the Striga Problem Introduction Distribution of the various Striga Species: Extent and Severity of Striga Infestation Striga s Impact on Rural Livelihoods Interventions Strategies for controlling Striga Traditional Practices: Manual Weeding and Fallow Trap Crops: Crop Rotation, Intercropping, and Push Pull Push pull Striga Control Crop Intensification: Soil Fertility Enhancement and herbicides Seed Coating: IR Maize Conventional Breeding and Striga Resistant/Tolerant cultivars Chemical Control Intercropping and Rotation of Cereals with Legumes Need for Policy Action PROFITABILITY AND UPTAKE Matching the technologies with the various systems Multi Criteria Evaluation (MCE) RETURNS TO INVESTMENTS IN STRIGA CONTROL METHODOLOGY: SECTOR IMPACTS: OPPORTUNITIES, EQUITY, AND CONSTRAINTS 8.0 THE WAY FORWARD: ACTORS INVESTMENTS, AND POLICY 1 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo, J. Vitale, and J. Sanders Consultants for Kilimo Trust. April, 2009.

3 EXECUTIVE SUMMARY This report describes an ex ante economic impact assessment study that was conducted November 2008 through February 2009 to generate information to help guide the development of a Striga control program in East Africa. INTRODUCTION 1) The challenge of food security continues to overwhelm the governments, citizens, as well as international donor agencies in Sub Saharan Africa. Over the past few decades, many SSA countries have gone from being net exporters of food to net importers. Pests constitute a significant threat to cereal production in SSA. One of the most pernicious problems is Striga, a parasitic weed that attaches itself to the roots of cereal plants. 2) Recent developments in crop science have created new hope in combating Striga. Researchers have discovered novel properties of legume fodder crops, such as desmodium, which are highly effective in controlling Striga damage and also provides producers with new sources of animal feed. Fodder crops can also be integrated into a push pull pest management system, a more comprehensive approach that controls other pests such as maize stem borers, while generating further benefits. IR maize is yet another emerging technology that controls Striga using an innovative approach that coats maize seeds with specially selected herbicides. By design, IR maize is resistant to the herbicide, which kills Striga plants under the soil surface before they can do any damage. 3) Along with new hope there is also a sense of urgency. Striga is a pernicious enemy that grows more damaging with time. Striga flowers produce hundreds of thousands of tiny seeds that spread quickly, infesting new areas. Each year, the opportunity cost of a do nothing, business as usual strategy grows larger. 4) A modeling framework was developed to predict the economic benefits of introducing Striga control measures in three East Africa countries, Kenya, Tanzania, and Uganda. The model was constructed using the results of field trial data from sixteen independent studies conducted over the past eight years in East and Central Africa. The field trial results were extrapolated to other Striga infested locations METHODOLOGY 5) Baseline Striga conditions were established using data from several sources. These sources included recently conducted field surveys, expert opinion, and previous studies that documented Striga conditions in the East Africa study region. This 2 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo, J. Vitale, and J. Sanders Consultants for Kilimo Trust. April, 2009.

4 baseline data generated information on Striga infestation levels throughout East Africa and their corresponding damage on maize and sorghum yields. Using GIS, the Striga infestation was geo referenced and mapped. Geostatistical interpolation technique called co kriging was then used to extrapolate determine the spatial distribution of Striga for locations where surveys had not been undertaken. The mapping of Striga infestation determined both the spatial extent and intensity of Striga, which are included in the baseline analysis. The maps then provided a clear description of where, and what the density of Striga infestation is in particular regions throughout East Africa. 6) Production losses due to Striga were determined by relating yield losses under to various Striga infestation levels within various ecological zones and extrapolating the losses to other locations based on levels of Striga infestations. Based on primary data, deterministic statistical models were developed and used to estimate what yield losses would be for each ecological environment given a specific level of severity of Striga infestation. 7) The benefits from introducing Striga control measures were calculated based on primary data collected by field crop scientists. Experiments, at both research stations and on farm trials, have documented the efficacy of Striga control measures. The push pull and IR maize technologies controls &efficacy measures have been tested at several locations in Western Kenya, including extensive studies using on farm trials. IR maize has been tested at experiment stations in Kenya, including some on farm trials. Other technologies such as legume intercrop/rotation, Striga resistant maize and sorghum cultivars have also been largely tested on select trial locations across East Africa. Based on this primary data, estimations on the effect on production of each of the various control measures such as push pull, legume rotation, IR maize technologies among other technologies were arrived at by comparing yield gain under each technology against production under a do nothing/control scenario (usually maize mono crop). The yield gain framework was used to estimate crop production gains due to Striga control across the region. The local level impacts by system within the various countries in which the interventions were judged to be relevant were then aggregated, using the geographic information systems (GIS) data layers derived in the characterization stage. 8) Each intervention by system combination was then assessed, using the economic surplus and agricultural sector models, in relation to the potential impacts that could arise as a result of resource expenditures on applying the specific technology. 9) Economic benefits were imputed by valuing production gains at the fair market value of each commodity. The Net present value of economic benefits, producer costs and donor investments were then deduced for a 20 year period from 2010 till Producer and donor benefits cost were also determined. 3 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo, J. Vitale, and J. Sanders Consultants for Kilimo Trust. April, 2009.

5 RESULTS Extent of Infestation: 10) The major Striga area in East Africa is found around the Lake zone (found along the lake Victoria basin in the three East Africa countries). This is a single continuous Striga band that has the largest Striga extent and infestation density. This region is the Western and Nyanza provinces of Kenya, the Mwanza Shinyanga Mara regions and coastal zone of Tanzania and the eastern region of Uganda. Striga is also to be found further inland in Tanzania, predominantly in the semi arid central parts of the country covering Tabora Singida Dodoma and even further South towards Iringa and Ruvuma regions. In Uganda, the eastern part of the northern region is also a Striga area. Apart from the western part of Kenya, Striga is again only found at the coast. The coastal zone adjacent to the Indian Ocean in both Kenya and Tanzania is also a Striga zone, mainly dominated by a specific species (Striga asiatica) and covers Coast province of Kenya and the Tanga, Pwani, Lindi and Mtwara regions of Tanzania. 11) The dominant Striga species within most of East Africa is Striga hermonthica dominant in the Lake Victoria zone in all three east African countries, and the predominant species in Kenya and Uganda. In Tanzania Striga hermonthica, Striga asiatica and Striga forbesii can be found in almost equal proportion, the latter further inland. Striga asiatica is prevalent in the coastal zones of both Kenya and Tanzania. 12) Tanzania has the largest area under Striga infestation, with infestations reported almost throughout the country, totaling just under one million hectares (963,532 with the highest severity of infestations for Tanzania found along the Lake Victoria in the Mwanza and Mara regions. Much of the rest Tanzania s Striga infestations level are however in the low to medium severity levels and are predominantly in the central semi arid and southern plateaux of the country. 13) Kenya has approximately 340,000 hectares of cropland under Striga infestation along the lake Victoria basin (not accounting for the coastal zone area) while the area infested with Striga Uganda is the least of the other two East African countries coming at slightly over 100,000 hectares with Striga infestations typically more intensive along eth lake zone in both countries, and areas in either the low or medium infestation categories occurring further inland. 14) Proportionally, the.magnitude Striga severity is higher in Kenya as over 70% of the area infested in Kenya s area in the medium to severe infestation categories, while it is lower in Uganda and Tanzania respectively. YIELD AND ECONOMIC LOSS DUE TO STRIGA: 4 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo, J. Vitale, and J. Sanders Consultants for Kilimo Trust. April, 2009.

6 15) Results of this assessment indicate that Striga causes massive losses on the crop systems of the region and the available Striga control strategies variously offer substantial potential for improving crop production in the maize, millet sorghum and crop production systems. Significant yield increases can be particularly achieved within the maize crop. 16) As would be expected, the most Striga damage is to be found where Striga infestation is severe, generally around Lake Victoria. This is particularly the case in Kenya, where maize losses reach as high as 6 tons/ha in some instances. The aggregated loses in some districts both in Kenya and Tanzania reach as much 25,000 tons per district within the lake zone and even in the moderately infested areas, losses average of 5,000 tons occur. In Kenya, the average losses for maize are on average 1.2 tons/ha for maize while Sorghum and Millet losses due to Striga average about 250Kg and 500Kg per hectare respectively. Similarly in Tanzania, huge sorghum losses also occur, up to 25,000 tons in a district (and about 700Kg per hectare) particularly in the sorghum growing regions of Shinyanga, Tabora, Singida and Dodoma. 17) A total of about 185, 000 tonnes of maize are lost on average every year in Kenya due to Striga. This represents 10% of the approximately 2.5milion metric tonnes of Maize that Kenya produces annually. Kenya also loses close to 30,000 metric tonnes worth of sorghum (20% of national production) millet annually due to Striga in the dominant western part of country. 18) Tanzania has the largest share of absolute production losses, totaling 465,000 metric tons of maize per year. Tanzania s losses correspond to a substantive 13% of its annual maize production of about 3.6 million tonnes. Tanzania also loses an equivalent 193,000 metric tonnes of Sorghum and a further 233,000 metric tonnes of rice to Striga annually. 19) Uganda also has significant losses, estimated at 77,000 tonnes of maize, 5,000 tonnes of sorghum and 9,000 tonnes of rice over the current levels of production. 20) At farm level, the average losses for maize Tanzania are on average between 1 and 2 tons/ha for maize while Sorghum and Rice losses due to Striga average about 400Kg and 600Kg per hectare respectively. 21) On a per capita basis (loss per individual determined by dividing the total production loss by the population residing at respective district), loss of production due to Striga damage is highest in Kenya. On average, maize losses is about 40Kg per capita in Kenya, compared to 30 kg per capita in Tanzania. This indicates that the Striga damage is more thinly distributed in Tanzania than in Kenya. Tanzania s Striga 5 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo, J. Vitale, and J. Sanders Consultants for Kilimo Trust. April, 2009.

7 infestation occurs over a larger production area and impacts a less densely populated region compared to Kenya, whose Striga infestation is concentrated in the highly populated Lake Victoria region. 22) The economic losses from Striga infestation are significant. Considering maize alone, the overall estimated losses for Kenya, Uganda and Tanzania are pegged at 46,000,000, 19,000,000 and 116,000,000 US dollars annually respectively. Annual losses to rice crop are also huge, at 186,000,000 and 7,000,000 US dollars for Tanzania and Uganda respectively. Losses to the sorghum crop are pegged at 63,000,000 US dollars in the three east Africa countries. The largest overall economic losses, totaling US$356 million per year are in Tanzania, followed by Kenya at US$ 54 million and Uganda at US$ 27 million. ESTIMATES OF IMPACTS THAT THE PROGRAM TO CONTROL STRIGA CAN GENERATE 23) Across the East Africa study region, significant production gains can be can be achieved through introducing Striga control measures and eliminating Striga altogether. More than 2.3 million metric tonnes of maize would be added to current production levels across East Africa in the form of 1.4 million tonnes in Tanzania, 700,000 tonnes in Kenya and 220,000 tonnes in Uganda. 24) Eliminating Striga would also contribute in enhancing production in sorghum at the rate of close to 600,000 tonnes in Tanzania, over 91,000 tonnes in Kenya and close to 15,000 tonnes in Uganda. Rice gains would be over 800,000 tonnes in Tanzania and 25,000 tonnes in Uganda. 25) The primary Striga infested areas where the largest production gains could be achieved are largely those districts and counties that currently already contribute significantly to national cereals production. 26) On a per capita basis (production per person), in the heavily Striga infested areas of Kenya, per capita maize production would increase by as much 1.5 tonnes per person in some districts. This would generate a cereal surplus since only about 110 kg per person is required. Hence, these regions could export enough food to feed an additional 13 persons. The less heavily infested areas are capable per capita production increases between kg per person. While not enough to generate a surplus on its own, it is enough to significantly increase caloric intake in households, reduce food expenditures, and in some cases enable households to generate a surplus. 6 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo, J. Vitale, and J. Sanders Consultants for Kilimo Trust. April, 2009.

8 27) Across Kenya, Uganda and Tanzania, the total economic impact of eliminating are substantial. The net present value of economic benefits over a span of twenty years (2010 to 2030) are to the tune of close to US$ 2.2 billion (approximately US$460 million in Kenya, US$1.5 billion in Tanzania and US$170 millions in Uganda). This figure that roughly translates into a regional absolute benefit of about US$108 million will provide additional gross annual revenue of about US$23 million in Kenya, US$77 million in Tanzania and US$8 million in Uganda. 28) Given a producer cost of approximately US$380 million in three countries across the span of twenty years, a high producer benefit to cost ratio averaging about 5.65 is expected across the region. This ratio is higher in Tanzania (7.53), followed by Uganda (4.13) and Kenya (3.34). Similarly, a high regional donor benefit to cost ratio of 5.12 can be expected, (with donor investment pegged atus$39 million). These figures point to a high rate of return for both producers and donors for investments targeting Striga elimination in the region. 29) Reduction of Poverty: Striga has a disproportionate effect on the rural poor, with infestation more prevalent in areas with higher rates of poverty. In the heavily infested Striga zones rural poverty rates often exceed 70% or more. Where Striga infestation is lighter, poverty rates are often 20% or less. Consequently, introducing Striga control measures would also have differential impacts on reduction of rural poverty. Although it is the more impoverished areas that are most severely affected by Striga damage, they are also the group that would benefit the most from introducing Striga control measures. A majority of the impacts would be concentrated in the areas where rural poverty rates exceed 70%. 7 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo, J. Vitale, and J. Sanders Consultants for Kilimo Trust. April, 2009.

9 1.0 INTRODUCTION Maize and sorghum are the two most important cereal crops in East Africa 1. Among the millions of smallholder farmers in the region, maize and sorghum provide the principal sources of food and income upon which rural livelihoods are based. Over 90% of the maize and sorghum produced in the region is directly consumed by humans. The plurality of household food requirements, in both caloric and nutritional measures, is furnished by maize. Roughly 25% of the caloric intake amongst East Africa s 110 million inhabitants is supplied by maize, with sorghum providing an additional 12%. Maize is also a vital source of income, often serving as the sole cash crop in rural communities. Regional maize production lags demand, an on going trend in East Africa and largely true throughout SSA (Bertini and Glickman, 2009; Byerlee and Heisey, 1997). Land extensification, the traditional approach to increasing agricultural production, is no longer a viable option in East Africa. Population pressure has strained the supply of land, leaving rural households with shrinking land holdings. East Africa has significantly high carrying capacities, particularly among the rural poor. In Western Kenya, for instance, population densities can reach as high as 829 persons/km 2 (DeGroote et al., 2008). With domestic food production on the decline, East Africa has turned to food imports (Figure 1). In a typical year, for instance, Kenya imports about 427,000 tons of maize (FAO, 2009). This strategy drains foreign currency reserves and prevents domestic producers from generating needed sources of farm income. In Kenya, maize imports averaged $56.7 million over the past years. Per Capita Maize Production (kg/person) Imports (000 MT) Kenya Tanzania Uganda Figure 1 Declining yields and rising populations have created stagnation and declines in East Africa s ability to feed its population. 1 In this report, East Africa refers to Kenya, Tanzania, and Uganda. Kenya Tanzania Uganda 8 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo, J. Vitale, and J. Sanders Consultants for Kilimo Trust. April, 2009.

10 1.1 Food Crisis This trend towards import dependence leaves East African countries vulnerable food crises. Recent price shocks among basic food commodities have led to political and social unrest in various parts of the world. The so called world food crisis that coalesced across the globe in 2007,2008 and continues till now when the price of rice, corn, wheat, soybeans, cooking oils, and food more generally skyrocketed illustrates the crises. The food riots that erupted on virtually every continent demonstrate both the global integration of food and agricultural systems, and the severity of the problems inherent within them. Triggers such as commodity speculation, grain hoarding, the diversion of food crops for use as fuel crops, the growth of industrial methods of livestock production and meat consumption, and falling grain reserves have contributed to the rapid increase of food prices. The world s poor suffer the greatest blow as food prices have risen to unattainable levels, rations decreased or disappeared, and as a result, hungry people have taken to the streets in protest. The catastrophe of high food prices earlier this year was no doubt a food crisis in and of itself. Rising food prices are putting millions of people in East Africa at risk of severe hunger and destitution. Kenya is currently facing a serious food security situation due to a combination of factors: Displacement, insecurity, poor rainfall, rising food and other commodity prices, and reduced cereal production. The shortfall of maize combined with very high prices prevailing in the international markets in the first half of the year have pushed the internal price of maize to a very high level, thereby reducing access to food by the most vulnerable section of the population. At the end of 2008, the retail price of maize flour, Kenya s primary staple food, was Ksh. 60 per Kg, a price 50% higher than in the same period last year. The cost of maize flour has increased to an historic high of Sh. 60 per kg from the long term average of Kshs 30 per kg. At the beginning of January 2009, the President of Kenya declared the food shortage a national disaster. As a short term measure, the Government approved the importation of 900,000 MT (10 million 90kg bags) duty free maize grains into the country in order to boost supply. This importation is to be done by both the Government and the private sector. Despite the government s efforts to try to resolve this crisis with a new brand of maize flour be packaged to sell at Sh26 per kilo to target the poor, a price surge is being experienced in the maize flour market after the government withdrew the Sh200 rebate it was offering millers for every bag of maize. The subsidy had been aimed at making the staple more affordable to the majority of Kenyans who had in November complained that prices had risen beyond their means 9 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo, J. Vitale, and J. Sanders Consultants for Kilimo Trust. April, 2009.

11 Tanzania and Uganda are facing a similar crisis. Tanzania placed a ban on maize exports, and maize prices in Uganda and Tanzania are very close to the ones recorded in Kenya. In Tanzania for instance, market prices for maize have almost doubled, increasing from Tsh. 35,000 to Tsh. 60,000 over the recent past. 1.2 Yield Gaps To avoid continued reliance on food imports, cereal production shortfalls will need to be met through raising crop yields, which has been an on going challenge in East Africa. Table 1 summarizes the current yield gaps required to eliminate maize imports in East Africa. In Kenya, for instance, maize imports have averaged 303,869 MT over the past seventeen years while in Tanzania (Figure 2) below, losses due to Striga actually surpass the national maize grain requirements shortfalls. The Maize Balance in Tanzania 2007/ /08 Maize Balance in MT Thousands 4,500 4,000 3,500 3,000 2,500 2,000 1,500 1, Annual Production Annual Demand deficit Loss due to Striga Figure 2 The yield gap in Tanzania. Given the average maize yield of 1.7 MT per ha and a production base of 1.7 million ha, maize yields would need to be increased by an average of 10.5% to eliminate maize imports. A slightly larger increase would be required in Tanzania, where maize yields would need to be increased by 12.8% to eliminate maize yields. Uganda would require the most modest yield increase of 0.03 MT per ha, a 2.2% increase over current yield levels. 10 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

12 Table 1 Maize yields required to eliminate maize imports and corresponding yield gaps in Kenya, Tanzania, and Uganda. Country Cereal Imports ( ) Production Area (000 ha) Current Maize Yield (MT/ha) Required Yield (MT/ha) Yield Gap (MT/ha) Yield Gap (%) Kenya 303,869 1, Tanzania 99, Uganda 43,626 1, Source: FAO, Agricultural policy makers and planners continue to search for technically feasible and economical strategies to increase cereal yields. Unlike other regions of the world where the Green Revolution has been successful in increasing crop yields, SSA has been largely bypassed. In East Africa, maize yields are low, typically less than 2 tons per ha at the national level, and have been relatively stagnant over the past couple of decades, lagging population growth in the region. The use of modern crop technology remains low, particularly among the smallholder producers. Open pollinated varieties are commonplace, which are often planted with few soil or plant amendments. Yields can also be increased in more conventional ways through enhanced crop protection. In East Africa, pests constitute a major constraint to increasing crop yields. One of the most damaging pests is Striga (Scrophulariaceae), a parasitic weed found throughout East Africa that causes substantial yield loss that is of growing economic importance. Striga control technologies have been developed to combat Striga with promising results, but to date efforts have been only loosely coordinated. The purpose of this report is to project the potential ex ante impacts of introducing an integrated Striga control programme in East Africa, one that combines several existing technologies into a more unified approach to optimally equip producers with the tools they need to rid their fields of Striga. Once eradicated, producers will be better positioned to take advantage of improved technology and expand yields further, including the new technological frontiers that can be reached as the African Green Revolution comes to fruition. 11 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

13 2.0 METHODOLOGY Striga distribution: Field studies were undertaken by ICIPE, CIMMYT and the multiple partners within the three east Africa countries. These data were amalgamated to create a more comprehensive picture of Striga distribution with the region (see Annex 7 for the various sources of information). The spatial distribution of striga was mostly characterized using Striga species and density data collected at locations in the field and were used to develop a GIS database for striga infestation by mapping Geographical Positioning System (GPS) data points that had been collected and identifying locations and densities of striga infestations. Geostatistical techniques: Geo statistical techniques were used generate information and represent the data as a continuous surface. These interpolation techniques were used to determine what happens between the actual data points where the surveys were not done. The interpolation procedure a geo statistical technique called cokriging a geostatistical method based on statistical models that predict spatial autocorrelation of sampled data points. Cost, timeliness and convenience of measuring striga data limited what could be measured for analysis and therefore secondary information such as elevation data were used to mitigate these issues and help infer spatial statistics since such data are more densely sampled. Once weed density maps had been gridded and surface maps of weed density created, it was possible via data analysis procedures available within commercial GIS software packages to create summary statistic s of estimates of severity and acreages affected by striga by various administrative units, agro ecological zones and the countries in general. Spatial yield loss and yield gain estimation: The various infestation severity levels were used to estimate the percentage of yield losses & gains in each infested area (Yield). Yield loss information was derived from a series of deterministic models for each agro ecological zone that associated the number of emergent Striga to yields obtained. The expected yield gain under each Striga control technology was computed from percentage yield realized resulting from comparing where a specific Striga control technology had been applied versus the control experiments, information that was gleaned from the various field experiments and studies conducted by multiple partners of this project. Where it was not possible to get field experimental estimates, expert opinion was used to estimate these yield loses and yield gains. 12 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

14 The computed standardized percentage factors representing yield gain and losses were the multiplied by the current crop production statistics within each administrative unit. The current crop production crop area, crop yield totals etc. were obtained from national statistics databases. The forecast yield losses/gains realized were represented at district level in Tanzania, divisional level in Kenya and sub county levels in Uganda. Economic analysis: Economic losses and gains were then computed for each administrative unit by multiplying each of crop s yield losses/gain by corresponding prevailing prices. The economic surplus and benefit cost analysis model was used to evaluate the proposed program.. The changes in annual economic surplus due to the striga control were determined in monetary terms. The benefit cost analysis was used to determine whether there was result a significant return on producer and donor investment and give the total change in economic surplus due to the program, adjusted by the annual level of adoption. Targeting technology interventions: Targeting of the various Striga control technology intervention on the various farming systems was performed through a criteria based method of weighted linear combination. Criteria based targeting mapped the likelihood of adapting various Striga control technologies within the various prevailing farming systems in the region. Literature and expert opinion suggested the parameters that are likely to be associated with uptake of each intervention and included factors such as agro ecological potential, socio economic conditions of the farmers among other conditions. The area where each criterion persisted was mapped and, by overlaying individual layers, the areas where all or most of the criteria were satisfied were identified and that became the area where it was most likely that the specific intervention in question could be adopted. Detailed methodological aspects are represented in subsequent respective sections. 13 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

15 3.0 Quantification of the Striga Problem 3.1 Introduction Some of the most damaging pests are parasitic weeds in the genus Striga (Scrophulariaceae), with Striga hermonthica the most damagingg in the East Africa region (Oswald and Ransom, 2001). Striga is highly adaptedd to its environment. It will only germinate in response to specific chemical cues (i.e. plant exudates) received from the host plant. Although maize and sorghum are most often associated with Striga, other crops such as millet, upland rice, and sugar cane can also serve as a host for Striga. Once germinated, however, Striga integrates itself with the host plant, attaching itself to the vascular system within the root structure. Striga competes with the host plant for water and nutrients, wounds the outer root tissue, and weakens the host plant s ability to maintain its normal growth patterns by impairing photosynthesis (Joel, 2000). Striga exerts a potent phytotoxic effect on its host causing severe stunting and a characteristic "bewitched" and chlorotic whorl (Ransom et al., Figure 3 Striga is a purple flower w 1995). As a result, plant performance is severely belies its pernicious nature. Chlorot degraded by Striga with a large reduction in damage are evident in the host pla host plant height, biomass, and ultimately grain yield (Parker and Riches, 1993; Gurney et al., 1999). Striga is a persistent problem that won t go away on its own (Figure 3). Once an initial infestation has been established, Striga can spread quickly to other fields and neighboring farms. Striga seeds are tiny, and a single plant can produce hundreds of thousands of seeds. Often times the seeds are transmitted inadvertently by farmers traveling across fields. Once deposited in the soil, Striga seed banks can rest dormant for up to 20 years. whose beauty tic whirl and nts. 3.2 Distribution of the various Striga Species: Survey: The broad distribution of Striga across Africa have been recorded by numerous authors (e.g. Parker and Riches, 1993; Sauerborn, 1991, Mbwaga & Obilana, 1993, Frost, 1995). This work undertook more survey work to fill gaps in the distribution of Striga within the region. Notably, field studies were undertaken by ICIPE, CIMMYT and the 14 Page EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo, J. Vitale, and J. Sanders Consultants for Kilimo Trust. April, 2009.

16 multiple partners within the three east Africa countries. These data were amalgamated to create a more comprehensive picture of Striga distribution with the region (see annex 7 for the various sources of information). The spatial distribution of striga was mostly characterized using Striga species and density data collected at locations in the field and were used to develop a GIS database for striga infestation by mapping Geographical Positioning System (GPS) data that has been collected and identifying locations and densities of striga infestations. Where these GPS coordinates were absent, inference was made to locality names where the study were conducted and coordinates for nearest urban localities used. Knowledgeable informants guided mapping the infestations where survey data were unavailable. The most dominant species of Striga in the region are Striga hermonthica (Del.) Benth, Striga asiatica (L.) Kuntze and Striga forbesii. Of the three, S. hermonthica is the most dominant species and is the predominant specie in the Lake Victoria Basin of Kenya, Uganda and Tanzania. Striga hermonthica (Del.) Benth is also quite extensive in terms of scope of geographical coverage and is to be found across a vast range of ecological environments in the three countries. Widespread occurrence of Striga asiatica can also be observed in Tanzania and the Kenyan coast while of more local importance is Striga, forbesii in Tanzania (See figures 4a and 4b). The eastern part of Tanzania (Tanga, Morogoro, Coast, Lindi, Mtwara, Ruvuma, Singida and Dodoma) has Striga asiatica and Striga forbesii as the dominant species, both of which parasitize maize. Striga hermonthica has a specialized locality in the North western Lake Victoria zone (Mara, Kagera, Tabora and Shinyanga). In Uganda, apart from the predominant Striga hermonthica, minor patches of Striga asiatica can also be observed while the predominant species in western region and the lake basin in Kenya is Striga hermonthica. 15 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

17 Figure 4a: Dominant species of Striga in Tanzania 16 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

18 Figure 4b: Striga s dominant species: Uganda and Kenya. 17 P age

19 Figure 5 Striga survey locations and density survey locations and density in East Africa. 18 P age

20 The overall zones of Striga infestation are plotted on maps shown in Figures 4a& 4b and 5 above. Figure 5 shows the distribution of locations of Striga by species as recorded by the various surveys conducted for this and other previous work. Annex 7 also illustrates the sources of information used to derive these distribution patterns. 3.3 Extent and Severity of Striga Infestation Geostatistical techniques. Geostatistical techniques were used to map the spatial distribution of striga. The Striga survey data conducted consisted of individual points across East Africa while it was necessary to represent Striga in a continuous map surface that estimated Striga presence even at locations where survey were not done. To generate information and represent the data as a continuous surface, interpolation techniques were used to determine what happens between the actual data points where the surveys were not done. The interpolation procedure used on multiple data points to ascertain data values between those points was the geo statistical technique called cokriging. Interpolation is a geostatistical method based on statistical models that predict spatial autocorrelation of sampled data points. The cokriging methodology used introduces a covariate in this process. Autocorrelation, or variography, is the statistical relationship among measured points in a data set. Kriging interpolation uses surrounding data points to predict an unknown value for an unmeasured location. In Kriging, the predicted point depends on a fitted model to the measured points, the distance from the unknown point to measured points, and the spatial relationship among the measured points around the predicted point. Kriging is considered the best predictor of non sampled locations, because mean residual error is minimized by its calculation. Kriging models use a semivariogram to depict the spatial autocorrelation between measured sample points. Semivariogram modeling is what separates spatial modeling from simple spatial description. The model assumes that measurements that are geographically close together are more similar than ones that are farther apart. Cost, timeliness and convenience of measuring striga data limited what could be measured for analysis and therefore secondary information such as elevation data were used to mitigate these issues and help infer spatial statistics since such data are more densely sampled. The cokriging procedure employed in this process involved the use of a secondary variable (covariate) that is cross correlated with the primary or sample variable of interest. The secondary variable is usually sampled more frequently and regularly (Isaaks and Srivastava, 1992), thus allowing estimation of unknown points using both variables. This can aid in minimizing the error variance of the estimation (Isaaks and Srivastava, 1992). In our case, the secondary variable was the NASA SRTM 19 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

21 (Shuttle Radar Topographical Mission) 90meter resolution elevation data product for continental Africa which provided a spatially rich data set of elevation/altitude across the landscape and which has been previously correlated with plant Striga occurance Once weed density maps had been gridded and surface maps of weed density created, it was possible via data analysis procedures available within commercial GIS software to create summary statistic s of estimates of severity and acreages affected by striga by various administrative units, agro ecological zones and even the national scale. Tanzania ranked as one of the most highly infested countries in terms of hectarage with infestations reported almost throughout the country. The area under striga totals to just under one million hectares (963,532) with the highest severity of infestations for Tanzania is found along the Lake Victoria in the Mwanza and Mara regions. Much of the rest Tanzania s Striga infestations level are however still in the medium to high severity levels and are predominantly in the central semi arid region and the southern plateaux of the country. Kenya has approximately 340,000 hectares of cropland under Striga infestation along the lake Victoria basin (not accounting for the coastal zone area) while the area infested with Striga Uganda is the least of the three East African countries coming at 108,000 hectares. Apart from the Lake Victoria crescent, the eastern region of Uganda around the Lake Kyoga basin where cereals like maize, millet, sorghum and rice are grown is also infested by Striga. Table 2 Summary of hectares infested by Striga in East Africa Country Infested Area (ha) Tanzania 963,532 Kenya 342,168 Uganda 107,798 TOTAL 1,413,498 The extent and intensity of Striga infestation are illustrated Figure 5a and 5b. The most severely infested areas of Striga are generally located near Lake Victoria in all the three countries. In these areas, Striga density is at least 14 plants per m 2, but often reach much higher levels. Further away from Lake Victoria, Striga infestation tapers off somewhat, but significant areas of medium (4 9 plants per m 2 ) and high (9 14 plants per m 2 ) striga infestation levels occur. Most of Kenya s Striga infestation is in the medium, high to severe categories. Striga infestation tapers as you move eastwards of the lake basin and rise with altitude with a cut off location in the range of about 1,600 meters 20 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

22 above sea level. Most of Tanzania has large areas under medium to high levels of Striga infestation density, notably throughout its central and southern regions. Only the small pocket of Tanzania near the lake zone is under Severe Striga infestation, around the Mwanza and Mara regions. The sorghum growing areas of Tabora Singida Dodoma are highly infested. In Uganda, most of the high Striga infestations occur in two major areas, the Lake Victoria crescent and the areas around lake Kyoga. Elsewhere in Uganda, Striga infestation intensities are typically low. 21 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

23 Figure 6a: Severity of Striga infestation in Tanzania 22 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

24 23 P age Figure 6b: Severity of Striga infestation in Kenya and Uganda.

25 3.0 Striga s Impact on Rural Livelihoods Striga is a cause of significant yield damage in East Africa, as it is across sub Saharan Africa. Estimates of Striga yield damage are widely varied. Although Striga damage is correlated to the intensity of Striga infestation, the ensuing damage is complicated by a range of agronomic and climatic factors that affect damage levels. According to some studies, Striga infestation can completely annihilate maize and sorghum fields, forcing farmers to abandon their Striga infested fields and search elsewhere (Doggett, 1984; Sprich, 1994). Under more typical conditions, Striga yield damage has been found to range between 30 to 50% (Hassan et al., 1994; Parker, 1991; Vissoh et al., 2004). Such crop losses occur on significant areas throughout East Africa. Figure 7 Striga can devastate farmer s fields as shown in this Western Kenyan field. But producers no longer need to stand by helplessly. The healthy maize plants in the background were planted with IR maize and show little if any Striga damage. Striga infestation creates immediate concerns over food security, as the staple food production and consumption of over 35 million people is threatened in East Africa. There are particular concerns among smallholder farmers, as Striga s heaviest infestations are typically found in poverty stricken areas where rural households produce on meager landholdings as intensively as possible. These groups are also the 24 P age

26 most susceptible to Striga infestation since most of their food intake is from maize and sorghum, with only limited opportunities to shift their diets to other food sources. Without adequate means, smallholder producers struggle to maintain proper agronomic conditions that would aid in reducing Striga infestation; Striga damage is more pronounced and widespread in areas where both soil fertility and rainfall are low, factors highly associated with poverty (Khan et al., 2001; Oswald, 2005). Instead, the marginal growing environments enable and exacerbate Striga infestation. Hence, Striga is often viewed as a poor man s problem. The corresponding economic damage is significant, with disproportionate impacts on the poor. Across sub Saharan Africa, striga s economic damage is estimated to reach $1 billion per year, negatively affecting the welfare and livelihoods of 100 million people. Yield loss and gain estimations Yield losses and yield gains in each infested area were determined with the equation: Yield Loss = A * Ymax * (1 infestation severity * Loss) where A is the infested area at a given infestation severity interval and Ymax is the expected yield in a striga free crop. Loss is represents a yield loss factor that was derived from a series of deterministic empirical equations representing degree of loss for each agro ecological zones and at a Striga infestation density (.this information was derived from field studies correlating data on crops yields with number of emergent striga). The expected yield gain under each Striga control technology was computed from percentage yield gains resulting from comparisons between where a specific Striga control technology was applied versus the control plots which had no striga control within similar ecological environments. So for instance to get a yield gain due to push and pull technologies, the yield resulting from fields in which push and pull technology is applied versus maize monocrop were compared for yield differences. Similarly to determine the impact of IR Maize, the benefits were determined by comparing results from test locations where the IR Maize technology has been applied versus control plots and or farmers plots that the technology has not been applied. Ecological environments were defined by a clustering of similar agro ecological zonations, length of growing periods, soils and altitude. Apart from ecological environments, these yield benefits were further differentiated by interpolated maps of Striga infestation severity based on three infestation severity intervals (low, medium and high of emerged Striga in the field). Where it was not possible to get field experimental estimates, information 25 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

27 gleaned from the farmer survey questionnaires were used to estimate these yield loses and yield gains. Spatially explicit estimates of yield loss and yield gains were then derived across the whole region where Striga was known to occur. Striga yield damage, and its corresponding impact production losses, is illustrated in Figure 7. The greatest yield loss due to Striga is found in Tanzania. The production losses in Tanzania are estimated to total over 890,0000 tonnes of cereals which the bulk of production losses occurring in Maize (464,599 tonnes), followed by rice (232,913) and then Sorghum (192,975). Kenya loses about an equivalent of 213,936 tonnes of production due to Striga, with significant production losses in maize (184,400 tonnes) and sorghum (27,678) in the striga belt in western part of the country. Uganda loses over 90,000 tones of cereal annually to Striga with the bulk of this loss coming in the form of loss of maize production (76,568 tonnes). Table 3 summarizes the production losses for each of the countries while Annex.6 shows production losses for each administrative unit within the three countries. In terms of the spatial distribution of losses, losses reach as high as 30,000 tonnes in some districts in Tanzania, while similar levels of losses can also be observed in Kenya and Uganda (see Annex 6). Figures 8 through 10 illustrate the spatial distribution of crop production losses due to Striga for selected crops in the region. While the association between severe infestation levels and maize loss is expected, the production losses also factor in planted area. Most of the cereal losses are concentrated in those districts which are both high cereal production areas while at the same time infested by striga. For example in figure 9, the largest production losses of sorghum in Tanzania are concentrated in the sorghum producing belt regions of Shinyanga, Tabora, Singida and Dodoma. The amount of maize production lost on average every year in Kenya due to Striga represents 10% of the approximately 2.5milion metric tonnes of Maize that Kenya produces annually while the 30,000 metric tonnes worth of sorghum represents 20% of its national production. In Tanzania the close to 465,000 metric tons of maize lost per year correspond to a substantive 13% of its annual maize production of about 3.6 million tonnes. The economic losses associated with these production losses from Striga infestation are significant and are presented in subsequent section (Chapter 7). 26 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

28 Table 3 Production losses due to Striga Country Maize Production Loss (tons) Sorghum Production Loss (tons) Rice Production Loss (tons) Millet Production Loss (tons) Total (tonnes) Kenya 184,400 27,678 1, ,936 Uganda 76,568 4,944 8,574 90,086 Tanzania 464, , , ,487 TOTAL 725, , ,487 1, P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

29 Figure 8 Annual maize production loss due to Striga in Tanzania 28 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

30 Figure 9 Annual sorghum production loss due to Striga in Tanzania 29 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

31 Figure 10: Annual Maize Production Loss due to Striga in Uganda and Kenya 30 P age

32 4.0 Interventions Strategies for controlling Striga Recent developments in crop science have created new hope in combating Striga. Unlike the past, farmers can now be equipped to control Striga with a suite of options, ranging from improved cultural practices to modern, bioengineered seed varieties (non GMO). Recommended control methods to reduce Striga infestation are summarized in the following table, including their pros and cons. Table 4 Pros and cons of recommended Striga control options. Strategy Pros Cons Manual Weeding Simple operation; effective control under light infestations; reduces Striga seed bank. Labor intensive; Does not provide immediate yield benefits. Fallow Ease of implementation Time required to accrue benefits; requires extensive production systems Crop rotation Effective in reducing seed bank. Agronomic co benefits Rotation crop often uneconomical; time required to accrue benefits. Intercropping Agronomic co benefits Trap crops often uneconomical. Push Pull Soil Fertility Genetic Resistance Chemical Control Seed dressing Agronomic co benefits, stemborer control; provides livestock feed. Immediate and highly effective control Ease of implementation Ease of implementation; prevents further increase in seed bank. Ease of implementation; provides immediate yield benefits. Trap crops often uneconomical; extensive training required; time required to accrue benefits; initial labor investments. High cost of fertilizer to smallholder farmer. Requires seed purchase every season Control occurs post emergence, too late to provide immediate benefits; high cost of herbicide; has no effect on reducing seed bank. Requires seed purchase every season. References: Cultural practices including hoeing and hand pulling (Ransom, 1996).Heavy application of nitrogen fertilizer (Igbinosa et al., 1996).Crop rotation (Oswald and Ransom, 2001).Trap crops (Gbe`hounou and Adango, 2003).Chemical stimulants to abort seed germination (Worsham et al., 1959).Herbicide applications (Oswald, 2005). Resistant/tolerant crop varieties (Showemimo et al., 2002). 31 P age

33 4.1 Traditional Practices: Manual Weeding and Fallow Manual weeding of Striga plants is a simple operation requiring only basic equipment, but it is inefficient in controlling Striga damage (Figure 11). Since weeding occurs postemergence, after Striga has inflicted its damage on the host crop, it will have little effect. Hence, the effect of weeding is on the long term reduction of the Striga seed bank. Since producers do not realize immediate benefits from weeding, and given its labor intensive nature, manual weeding has limited potential as a strategy to eliminate Striga problems. However, because of its ease of operation and low financial cost, manual weeding remains the most commonly used approach to eliminate Striga in East Africa. The effectiveness of manual weeding can be increased when combined with other strategies. Ransom and Odhiambo (1994) found that integrating hand weeding with soil fertility improvement generated significantly higher yields than either option, although it requires up to four seasons before for its benefits can be realized. Fallowing is another traditional practice that can control Striga. The benefits from fallow are largely through increased soil fertility and the eventual loss of the seed bank. While effective, fallow requires up to 20 years before it has any significant effective on controlling Striga. With population pressure, the opportunity cost of agricultural land is too high in most places for fallow to be a realistic option to consider. Figure 11: Manual weeding is a commonly used practice to control Striga. Without other options it remains popular, but is too inefficient and labor intensive to provide long-term control over Striga in East Africa, particularly in heavily infested areas. 4.2 Trap Crops: Crop Rotation, Intercropping, and Push Pull Some of the most promising Striga control methods are considered to be trap crops, including push pull technology. Trap crops are specially selected rotation crops with novel properties that enable them to reduce Striga seed density levels by acting as a false host. Most trap crops are legumes, i.e. desmodium, which secrete root chemicals that act in contradictory ways, masquerading the plant as a false host for Striga. Some compounds within the trap crop stimulate the germination of Striga seeds while others inhibit their growth (suicidal germination), making it difficult for such legumes to act as a host since Striga seedlings must attach to a host within a few days after germination to 32 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

34 survive (Worsham, 1987). Trap crops have been introduced in rotation with cereals (Berner et al., 1996) or in suitable intercropping arrangements (Khan et al., 2002; Oswald et al., 2002, Khan and Pickett, 2004). Various studies report on the significant reduction in Striga plants that achieved when intercropping cereals with legumes (Saunders, 1933; Carson, 1989; Webb et al., 1993; Babiker and Hamdoun, 1994; Carsky et al., 1994; Khan et al., 2000, 2001, 2002; Tenebe and Kamara, 2002; Khan et al., 2006a, 2006b). In particular, the use of fodder legumes as trap crops has been shown to reduce Striga seed bank population over the course of just a few years, a highly desired outcome. The use of legumes in a crop rotation system generates co benefits for the producer. Livestock producers benefit from legume production since legumes such as desmodium are a sought after, nutritious fodder crop. Leguminous crops also enhance soil fertility through introducing additional nitrogen in soil. Researchers continue to search for food legumes that could also act as an inhibitor to Striga. Trap crops can also be easily adapted into a push pull system that provides additional benefits in controlling Lepidopteron stemborers, another economically important pest in the region. 4.3 Push pull Striga Control Push pull is spillover technology developed by ICIPE, originally designed for stemborer control in maize. The push pull approach was developed in collaboration with KARI, Ministry of Agriculture and Rural Development (MOARD) and IACR Rothamsted in UK. It involves intercropping maize with the silver leaf desmodium (Desmodium Figure 12 When intercropped with maize, legume crops provide significant control over Striga by reducing seed bank. Napier grass on the edge of the field is used for Push-Pull control, which reduces stemborer populations for added benefits. uncinatum) or green leaf desmodium (Desmodium intortum) and surrounding the maize crop with Napier or Sudan grass. Khan et al. (1998) while investigating the effect of legumes on striga observed 40 times reduction in striga infestation in maize intercropped with either Desmodium uncinatum or D. intortum. This observation was also supported by a study carried out at Kibos where desmodium was found to stimulate striga seed germination even more than the host crop maize (Ndungu 2000). 33 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

35 Numerous field trials have been conducted over the past several years documenting the efficacy of the push pull technology (PPT) and the use of trap crops in controlling Striga populations. Initial field trials were begun by ICIPE in 2001 at the Thomas Odhiambo Campus in Mbita Point, Kenya, a peninsula in Lake Victoria. The four years of assays tested the performance of four species of desmodium, and cowpea, in controlling Striga and maize stemborer. ICIPE conducted experiment station trials for four seasons ( ) in Western Kenya at Mbita Point. Maize and sorghum were intercropped with several legumes, including food crops, to assess their performance as trap crops. The results found that greenleaf desmodium performed the best, providing superior control over Striga, the tallest plants, and generated the highest grain yields. The food crops, cowpeas, beans, and groundnuts, performed significantly better than the control (i.e. monocrop maize and sorghum) in some seasons, but not across the length of the experiment. On farm testing of PPT control over Striga began in 1998 in two districts in Western Kenya, Trans Nzoia, and Suba. The studies also included the effects of PPT in controlling maize stemborer populations. The field trials tested three alternative PPT configurations, including the use of desmodium as a push crop. The initial on farm trials were broadened beginning in Between 2001 and 2004, on farm testing was extended to include four additional districts in Western Kenya. Each of the six districts in these field trials had 10 farmers, randomly selected from local farmers meetings (baraza). Farmers agreed to plant three plots: PPT, a maize bean intercrop, and maize monoculture. In addition to monitoring crop yields, surveys were conducted to estimate the incremental labor costs associated with PPT and the maize bean intercrop (trap crop). Across all districts, PPT performed significantly better than either the maize bean intercrop or the maize monocrop, in terms of both yields and economic returns. For dairy and agro pastoral production systems, push pull technology is usually a winwin strategy since fodder can be utilized as a livestock feed and recycled as manure. The benefits of fodder are less certain for crop farmers since markets for livestock are often thinly developed. Agronomic co benefits are also provided by PPT. Trap crops such as desmodium fix atmospheric nitrogen, replenishing the stock of soil nutrients. Push pull technology will also need to overcome other economic hurdles, including the required labor investments in establishing the trap and border crops and the long term horizon over which the benefits accrue. Smallholder farmers tend to be myopic, highly discounting future income streams. 34 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

36 4.4 Crop Intensification: Soil Fertility Enhancement and herbicides Soil fertility has been found to be significantly linked to Striga infestation. In nutrient poor soils, Striga typically flourishes. Conversely, where soils are managed with adequate quantities of nutrients, Striga infestations are seldom encountered. While the exact mechanism(s) by which soil nutrients affect Striga is not completely understood, studies have suggested that soil nutrients: Reduce the effectiveness of the host plant in stimulating Striga germination Enable the host plant to develop more quickly before Striga can act as a parasite Act as a toxin to Striga Are not able to be absorbed directly by Striga, which prefers to obtain nutrients as amino acids from the host plant. Numerous studies have demonstrated the role of inorganic nitrogen especially when high amounts are involved (Kim and Adetiminin 1997, Pieterse et al 1996, Bebawi 1991, Mumera 1993). However most farmers cannot afford to buy fertilizer and those who do so apply amounts that are way below the recommended levels. The use of organic fertilizers available on farm can help maintain fertility of soils, particularly those that are not very depleted. Ransom and Odhiambo (1994) investigated the long term effects of maize stover alone or in combination with low levels of nitrogen fertilizer, and observed reduced striga infestation following incorporation of maize stover together with fertilizer over four seasons. They demonstrated the importance of integrating soil fertility improvement and hand weeding striga, which was reflected in increased maize yield and reduced Striga. Apart from crop residues, green manure from trees and/or shrubs found on farm boundaries, wood lots or roadside can also be another alternative source of nutrients on farm. These trees and shrubs utilize niches that are often not utilized by the crops. Gacheru and Rao (1998) evaluated foliar biomass from six such species over four seasons and observed reduced striga infestation with high quality biomass of tithonia that was comparable to the use 120 kg ha 1 of inorganic nitrogen alone or in combination with phosphorus fertilizer. This study also demonstrated the need of integrating phosphorus with nitrogen in P deficient sites in order to realise the yield benefits accruing to reduce striga infestation. Earlier on, studies by Odhiambo and Ransom (1994) also demonstrated the importance of addressing the issue of soil fertility simultaneously with other striga management options. Cost is the primary constraint to the use of fertilizers in combating Striga. Relative to the price of the host crop, fertilizer remains too expensive for producers to adopt. This is unfortunate since fertilizers are effective in controlling Striga. Research suggests that 35 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

37 fertilizer application rates of 150 lb of nitrogen per acre are required under normal agronomic conditions. These are typically out of the economic grasp of most producers. 4.5 Seed Coating: IR Maize With the advent of molecular biology, the development new striga control strategies has resulted the application of ALS inhibiting seed coating applied to imazapyr resistant (IR) maize seed. Research efforts on this strategy have demonstrated that low doses of the herbicide imazapyr provide effective striga control. Researchers at CIMMYT have developed an innovative approach to Striga control using herbicide resistant varieties of maize. Using conventional breeding techniques, IR maize varieties have been developed that are resistant to the herbicides imazapyr and pyrithiobac. Both of those herbicides have been found to be effective in controlling Striga plants. The IR maize technology applies the herbicide in an innovative manner, by coating the IR maize seeds with herbicide. This drastically lowers the application rate of herbicide yet maintains sufficient residual activity near the root zone where Striga attaches to the host plant. By coating the seed, application rates are lowered twentyfold compared to conventional foliar based sprays, i.e. less than 30 grams per hectare for IR maize, reducing herbicide costs and eliminating the need for spray equipment. Studies have also found that Striga is killed prior to emergence, the period in which it does most of its damage to the host plant. Hence the IR technology provides more effective Striga control than foliar sprays that are applied post emergence, too late to prevent the bulk of Striga damage. Moreover, foliar sprays are impractical when maize is intercropped with crops susceptible to herbicides, such as legumes. With such low application rates, even the relatively expensive herbicides are within the economic reach of smallholder farmer s, unlike conventional spray approaches that require high application rates and are not effective enough to generate positive returns. With adequate care, producers can introduce IR maize into existing rotations, such as maize bean rotations, even when the rotation crop is sensitive to imazapyr and pyrithiobac. Typically, a spacing of at least 12 cm between IR maize and other crops is sufficient to assure that the sensitive crop is unaffected by the herbicide. There is considerable empirical evidence supporting the efficacy of IR maize in reducing Striga infestation and in the process generating higher crop yields. In an initial study, IR maize was field tested by CIMMYT at various locations in East and Central Africa, including Kenya, Tanzania, Malawi, and Tanzania (Kanampiu et al., 2003). This included trials at three experiment stations on on farm trials at 96 sites between 1998 and Where applicable, the field trials were conducted in both the short and long rainy 36 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

38 seasons (Kenya). The field trials were conducted on commercial varieties of maize bred for IR resistance. Across all four countries, the results found that IR provided significant control over Striga plant density and significantly increased maize yields. Further yield increases are expected once the IR resistance is introduced into local varieties, rather than the commercial varieties used in the experiments. More recent CIMMYT field tests of IR maize have been conducted in Kenya (DeGroote et al., 2007). In 2002, both experiment station and on farm trials were conducted, although both sets of plots were researcher managed. The station trials were conducted at the Kibos experiment station and the on farm trials were located in Kisumu. The results of the 2002 IR maize trials are summarized in Table 5. IR maize was found to reduce Striga counts significantly better than the control plots containing unprotected plants. In 2004, on farm trials of IR maize were conducted in three districts in Kenya, Bondo, Vihigia, and Rachuonyo. All of the field trials were managed by farmer managed. The most recent field testing data on IR maize is available from AATF, which has conducted tests over the past two years. The AATF tests are also the most comprehensive, encompassing over 600 producers in Western Kenya. Figure 13 IR maize coated with STRIGAWAY has been found under both experiment station and onfarm trials to provide significant yield and 4.6 Conventional Breeding and Striga Resistant/Tolerant cultivars Host plants have been observed to display resistance to Striga infection (Johnson et al., 2000; Harahap et al., 1993; Ramaiah, 1991; Williams, 1959). This includes reduced host plant exudates that suppress Striga germination, and post germination barriers that prevent Striga from attaching to the host plant. Resistance (and tolerance) has varied, however, with performance significantly affected by climatic, agronomic, and other conditions. 37 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

39 In east Africa striga research was initiated in the early 1930s (Parker, 1994). During that time, Watt (1936) and later (Doggett 1954a, 1954b, 1965) showed the advantages of growing the early maturing sorghum variety, Dobbs (a Swaziland variety), over the others. Doggett 1965 crossed the variety Dobbs and came up with the variety Serena and later Seredo that is tolerant to S. hermonthica. At the KARI regional research centre at Kakamega (Kenya), sorghum breeding work was initiated in 1980 when sorghum lines, cultivars and land races were screened for striga resistance or tolerance (KIRIRO 1984). Some resistant and tolerant materials that out yielded the local farmer cultivars have now been identified by the centre (Mburu, 1998). In Tanzania, a a striga resistant cultivar P9401 has also been developed and tested across multiple locations while sorghum lines P9405 and P9406 have also shown resistance to Striga asiatica and Striga forbesii in Tanzania. Although maize is the most important food crop in the region, representing 80% of the national production of cereals, there has been no commercial maize cultivars breed specifically for striga resistance or tolerance (Ransom and Odhiambo, 1995). While breeding work was started in 1955 in Kitale (Kenya), work was only recently initiated to cater for mid altitude moist ecological zone that is infested with striga (Hassan 1998). In western Kenya, breeding maize for striga tolerance was started in 1989 (Odongo, 2000). Considering that farmers in striga infested areas have low resource base, KARI has come up with an open pollinated variety (KSTP94) that yields higher than the local land races available to farmers (Odongo et al., 1997). Compared to local land races that farmers cite as Striga tolerant, KSTP94 is comparable to the commercial hybrids under no striga infestation. This variety has been improved upon and is now a commercial variety that is readily available in the market. Currently efforts are going on in breeding and screening hybrids suitable for the striga prone areas for striga that are either resistance or tolerance to striga. Also CIMMYT s African Maize Stress Project has developed striga tolerant hybrids/ovp (open pollinated varieties) that are currently being tested under national performance trials. 4.7 Chemical Control Striga is effectively controlled by commercially available herbicides, including glyphosphates. However, two factors limit their use. One is that chemical control occurs post emergence, after Striga has inflicted its damage on the host crop. This prevents herbicides from reducing damage during its season of application, although it will act to reduce the Striga seed bank over the long term. 38 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

40 4.8 Intercropping and Rotation of Cereals with Legumes With strategic selection of appropriate improved fallow species, the system can be used for both striga seed bank reduction in the soil as well improving soil fertility. Improved fallows are defined as purposely planting usually a fast growing legume or tree/shrub species with the intention of fixing nitrogen in a crop fallow rotation (Sanchez, 1999). In western Kenya, some promising tree legumes for improved fallows species include Sesbania sesban, Crotalaria grahamiana, C. paulina, Tephrosia vogelii and T. candida. Improved fallow legumes provide nutrients for crop use through nitrogen fixation and deep soil uptake below the crop rooting zone and subsequently surface deposition through litter and root decay. On the other hand several food legumes have also been shown to suppress striga and induce suicidal germination including soybean, groundnut, cowpea and lablab. These legumes are already being grown on farmers field in western Kenya to suppress Striga. Striga infestation on maize following one year improved rotations with the legumes has been observed to be much lower compared to continuous maize cropping (Gacheru et al., 1998). Striga seed numbers with improved fallows was 42 59% lower than the seed numbers found in the continuous maize cropping while the natural weed fallow reduced seeds by only 35% in the same period. Similar results were observed in Eastern Zambia where S. asiatica infestation in maize following 2 years improved with S. sesban was also reduced significantly (ICRAF, 1995). For farmers with limited resources, improved fallows have been shown to provide high returns to labour and financial benefit than continuous maize growing, more so because their farms have low base yield (Place 2000; Shepherd, 1996). In part, this is due to benefits associated with soil fertility improvement, in particular where nitrogen fixing legumes are used. In western Kenya, some of the fast growing tree/shrub legumes used in improved fallows can accumulate between kg of N ha 1 in a period of 6 months to 2 years (Niang et al., 1996). In addition to improving soil fertility and controlling striga, improved fallows provide the much needed fuel wood and stakes that can be used in staking tomatoes of climbing beans. The potential of improved fallow adoption increases as the profitability of growing annual crops decline due to yield reduction, as the opportunity cost of labour increases and as access to off farm income increase. The economic benefit to the system is, however, limited under conditions of low rainfall and P deficiency (Jama et al., 1998). 39 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

41 4.9 Need for Policy Action There is an urgent need for policies to promote Striga control measures. Striga is a pervasive pest, and time is the essence. A single striga plant deposit s a seed bank containing hundreds of thousands of tiny, dust like seeds, establishing itself for several generations. Because of seed propagation, infestation levels accelerate rapidly once Striga has established itself; without a timely response, Striga damage grows exponentially. In East Africa, an estimated 600,000 ha of maize is currently infested with Striga. By 2020, the infested areas would increase to over 1 million ha if no further action were taken. Policy measures are required to address two critical needs. One is to equip farmers with the means to increase yields in Striga infested areas using appropriate control measures. The other is to adopt technologies to reduce Striga seed banks, assuring that Striga is adequately controlled over the long run. The next section documents the benefits that would be obtained from introducing and extending Striga control measures in East Africa (Uganda, Kenya, and Tanzania). The findings are derived from previous studies conducted by agricultural research scientists in the region, placing them in a unified context. Because Striga has been studied in only limited areas, study findings were extrapolated to nearby locations. In particular, the push pull and IR maize technologies are highlighted since they have been the most studied and deemed as having the greatest biotic and economic potentials. Moreover, they are complementary technologies that in many locations can be combined to provide even greater impacts. The benefits of the push pull and IR maize technology are projected using an economic model. Social and environmental benefits are also estimated in the analysis. 40 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

42 5.0 PROFITABILITY AND UPTAKE The wide spread use of the various methods has been limited by the reluctance of farmers to adopt them for both biological and socioeconomic reasons (Lagoke et al., 1991; Gbe`hounou and Adango, 2003). Various Striga control measures have been developed with varying degrees of success and economic viability. For instance, chemically based methods (i.e. herbicide and fertilizer) are usually too expensive for smallholder farmers to adopt and remain outside their economic means. Likewise, cultural practices are too labor intensive and lack adequate productivity for farmers to adopt them. In other cases, such as the use of crop rotations, the required interventions are too drastic to fit into existing cropping systems. Many Striga control options are profitable, however, as listed in Table 5. Table 5. Average maize yields, seasonal economic returns and suppression of Striga among different managements on 24 Striga infested farms (initial Striga seed bank >100 million per ha) over four consecutive seasons in west Kenya. (Source: Adapted by authors from Woomer et. al., 2008). Management practice Maize yield (kg per ha) Net return ($ per ha per season) Benefit cost Ratio Striga expression stems per plant Susceptible maize hybrid 1, Striga tolerant OPV 2, Striga evasive hybrid 2, Push pull system 2, Innovative intercropping 2, P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

43 Herbicide resistant maize 2, Uptake is determined by both internal factors, such as resource endowments and farming systems, as well as external factors such as markets and policy. Hence, the uptake of Striga control options will depend on: (1) household specific variables such as the availability of land, labor, and livestock holdings, and (2) market variables such as the cost and availability of financing and the strength of markets as indicated by prices. The household and marketing factors that determine the likelihood of uptake are summarized in Table 7. For instance, the uptake of cultural practices is more likely to occur in areas where land and labor are abundant. Likewise, trap crops and push pull technology are likely to be adopted where households posses livestock holdings, as well as having an adequate supply of land for intercropping and labor to make the initial labor investments. Table 7 Uptake of Striga control options will depend on several variables. Strategies that are positively related to the variables are indicated by +. Farming System & Resources Markets & Policy Strategy Option Labor Land Livestock Holdings Finance (Credit) Market Price Manual Weeding ++++ Fallow ++++ Crop rotation Intercropping Push Pull Soil Fertility Enhancement Genetic Resistance ++ Chemical Control Seed dressing The best fit choice for Striga control options also depends on the orientation of the household towards external markets and the nature of their farming system (Figure 14). Many of the options are expected to have boundaries within which they would be best suited. For cultural practices such as weeding and fallow, it s unlikely that uptake would occur outside of extensive systems that have primarily a subsistence orientation. Trap 42 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

44 crops, including PPT and other rotation schemes, would have the greatest likelihood of uptake in extensive systems that have some degree of commercialization. Trap crops require additional land since most often the rotation crop has lower profitability than the host crop. As systems become more intensive, the opportunity cost of land typically become too high for rotation and intercropping. A moderate level of commercialization is required to purchase seeds and to market livestock products. Resistant varieties such as IR maize have the fewest limitations, primarily since they are scale neutral. This means they generate the same economic return for both small, intensive farms as well as for larger, extensive farms. However, because they require cash outlays in purchasing seeds, a modest level of commercialization is required. Moreover, since resistant varieties are commercial products with a limited selection of varieties to choose from, subsistence farmers primarily concerned with taste might not be adopt them based on taste preferences. Figure 14 The likelihood of uptake of Striga control options can be assessed by the nature of farming and the orientation of households towards marketing opportunities. 43 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

45 5.1 Matching the technologies with the various systems Matching the available and potential Striga control technologies with the farming systems within East Africa is important in order to generate beneficial impacts for the largest possible groups of impoverished people. Not all of the technologies considered would be appropriate in every production system and country. Furthermore, the assessment of the relevance of individual technologies for different circumstances needs to be made not only on the basis of their impacts on the productive outputs targeted but also on their socio economic feasibility and their side effects and knock on impacts in other parts of the farming system. Table 8 below summarizes the major potential interventions and their main impact points within a generic maize livestock system. Even at this level of aggregation, this diagram clearly illustrates the potential for individual technological innovations to either interfere or complement one another (in either a negative or a self reinforcing fashion). This may be expected to result in a range of trade off situations that are likely to require more detailed analysis for individual circumstances. The table represents an overview (across all subsystems), based on the information presented in the preceding review of these Striga eradication technologies and on expert consultation, of the likely outcomes of the individual component interventions for the key productive elements of the system (arranged under the outcome groups; grain yields, feed yields, and soil fertility). It also indicates the various interventions that are considered. Table 8: The potential outcomes of a range of technological interventions for maizebased farming system 1 Potential outcomes 2 Grain Livestock feed Soil Technologies yields quantity fertility IR maize integrated in a push pull system Traditional practices (manuring, uprooting, burning) IR Maize Hybrid ? IR maize open pollinated varieties ? Push Pull (maize desmodium & napier) Innovative intercropping Legume intercropping & rotation Striga tolerant sorghum varieties ? Striga tolerant maize varieties ? 1. This generalized scheme assumes interventions are applied separately except for IR Maize push pull integration. 2. +, ++, +++, ++++,+++++ represent varying degrees of positive impact 44 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

46 The overall net impact Striga of technology will depend on appropriate targeting of specific technologies to appropriate systems to yield benefits to investment that offer the greatest returns on investment. The likelihood of adoption is affected by geo spatial factors, such as annual precipitation and temperatures, human population density, and socio economic factors, such as household incomes, labour availability, experience and education of the household head, size of land and market access amongst others. These socioeconomic factors (such as wealth, scale of operation, age of farmer, degree of intensification, ethnicity, or economic viability of enterprise) can be used to target better agricultural interventions and improve distribution of impacts. The inclusion of social and economic factors helps to addresses the questions of where interventions can make a major contribution to alleviating poverty in ways that are socially equitable, ecologically sound and economically efficient. Within the dominant Striga belts of East Africa, various farming systems can be identified. These are systems characterized by production system (i.e. crop livestock integration), ecological endowment and human population density. By broadly taking into consideration the variety of factors such as climatic and agro ecological zonations, dominant types of agricultural enterprise, population density, market integration and prevailing status of Striga control technologies, it is possible to identify distinct farming systems within which to recommend a set of Striga technology packages. For example in the pastoral arid/semiarid extensive systems, it would be difficult to apply technology such as IR Maize since the returns on investments would be minimal and these areas wouldn t be able to support the hybrid maize production due to the limited rainfall and shorter length of growing period. In small scale intensive purely crop systems of high potential where land holdings are very small and almost all of the land is cultivated, it might be prudent to apply IR maize over push and pull system as push and pull system would limit the amount land available for cropping. On the other hand, if these are mixed crop dairy systems, it would be beneficial to integrate push and pull together with IR Maize to provide knock off effects in terms of fodder for the livestock in these systems. Given the nature of data and information that was available to this work, targeting of the various Striga control on the various farming systems was both criteria and observation based targeting. Criteria based targeting relied on making use of multicriteria evaluation system through the method of weighted linear combination. Weighted linear combination was used to assess the weightings for factors, and to map the likelihood of adapting various Striga control technologies in the various farming systems. The literature and expert opinion suggested the parameters that are likely to be associated with uptake of each intervention such as climate, socio economic 45 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

47 conditions of the farmers among other conditions. The area where each criterion exists was mapped and, by overlaying the individual layers, the area where all or most of the criteria are satisfied identified: this is the area where it is most likely that the intervention will be adopted. This area of confluence is then reinforced by observationbased targeting involving observing where a particular Striga control technology has to some extent already been tested or adopted, then plotting these locations on a map and identifying which common characteristics the sites share. Targeting of the intervention can then be directed to other areas that share these same characteristics to infer probability of adoption. 5.2 Multi Criteria Evaluation (MCE) The method of multi criteria evaluation was used to assess the weightings for factors, and to map the best matched Striga technology within the various farming systems in the region. For example, to determine the probability of adoption of push and pull, some of the factor dataset that were considered included: Farm size: moderate to large farm size associated with adoption (land should not be a constraint). Human population density: medium to high population densities associated with adoption Total distance to existing areas of adoptions: closeness to existing locations where adoption has occurred is associated with adoption Agro climatic potential, expressed as PPE: higher PPE/Rainfall greater than 900mm per year associated with desmodium and napier grass necessary for push pull technology Improved cattle density: greater than 50 cattle per km 2 to benfit form increased fodder in a push and pull system. Stem borer problem (knock off effect of push pull technology package also controlling stem borers) etc. The multi criteria evaluation process included four steps: i) establishing the criteria (factors and constraints), ii) standardizing the factors, iii) establishing their relative weights, and iv) conducting the final multi criteria evaluation. The first step of MCE is the factors determination, such as: agro ecological potential, human population density, poverty (indicator of farmers ability to afford inputs), livestock density, proximity to locations where other striga control technologies are already adopted etc. Since the factors were in different units, they were standardized to a continuous common numeric range on a 0 to 255 scale. For each technology package, factor weighting was then performed on the variables with the following 46 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

48 values, 9 (extremely important), 7 (very highly important), 5 (highly important), 3 (moderately important) and 1 (equally important). The weighted linear combination procedure was to multiply each factor by its weight, and then adding the results by the following formula. S i = Σ((W 1 F 1i,..., W k F ki )/Σ(W 1,..., W k )) Where: S i = Final score for cell i F 1 k = Cell property for the factor 1 to k W 1 k = Factor weight 1 to k. This resulted in multiple technology matching maps, each showing the suitability for each Striga control technology. These technology maps were overlaid and the most suited technology adopted for each location. Where probability of adoption for multiple technologies overlapped a final decision was guided by picking the technology that would accrue the greatest economic benefit and similarly benefit the largest segment of the population. It is noted here that because of the numerous combinations and permutations that resulted from this exercise, and at disparate locations, most of the combinations were aggregated within the farming systems and the frequently occurring technology package in a particular administrative district was taken as the favorable technology package for that district. This was aid discernability and also help focus investment into a formal administrative boundary given that most intervention allocations would always be targeted towards administrative units. The results presented at this scale therefore act as a best bet guide among the available alternative Striga control strategies. Below are some of the GIS data layers used to guide the technology matching with brief explanation on how they were expected to influence adoption of the different Striga interventions. 1. Striga occurrence. Striga had to be present to target a technology and the areas where there was no Striga were not included in the analysis. 2. Farming systems. These are areas that have broadly similar resource bases, agricultural enterprise patterns, household livelihoods and constraints, and for which similar development strategies and interventions would be appropriate. Knowing the conditions under which different commodities are produced enables a 47 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

49 better understanding of the agricultural environmental interface. These farming systems provided the backdrop upon which the various striga control strategies would be applied. These broad farming systems also provided the level of scale within which a Striga technology would be applied. The most dominant or frequently appearing recommended Striga technology within any set of farming systems was applied to that farming system irrespective of the occurrence of other possible valid technology systems. The level of detail was deemed adequate for this regional scale of analysis. 3. Agro climatic potential. This was calculated from a combination of two parameters i.e. The length of growing period (LGP) from climate GIS layers and secondly as ratio of precipitation to potential evapo transpiration (PPE). The length of growing period (LGP) was defined as the period in days during the year when the rain fed available soil moisture supply is greater than half the potential evapotranspiration (PET). This potential determined what combination of crops could be grown in a Striga control strategy, both the crop itself and its accompanying technological package be it genetically inbuilt (e.g. IR maize or Striga tolerance) or external such as desmodium and legumes that are used to suppress Striga. 4. Human population density (number of persons per square kilometer). This information was derived from census data for the three countries. The rationale here is that of high human population density correspond to areas on intensive small scale agriculture where producers are likely to maximize production in limited pieces of land and where access to land is a significant constraint to production. Also, population pressure is seen as a key driving factor for uptake of productivity enhancing interventions as high demand motivates farmers to increase their productivity and these high population areas offer the opportunity for a largest segment of population (usually poor too) to benefit from a set of interventions. 5. Incidence of poverty. The poverty information was used to assess the probability of particular communities adopting specific Striga control technologies. Where the farmers were poor with little or no cash, too little labor, and were risk adverse, low input agriculture with Striga technologies such as use of open pollinated Striga tolerant varieties and sometimes at best IR open pollinated varieties would most likely be adopted. Where producers were moderately well off to well off, the adoption of hybrids such as IR maize hybrids for instance was most likely. The poverty layer was used a proxy for ability to afford inputs, labor etc on farm and was also an indicator of nature of farming system in place, either subsistence of market orientation. 6. Livestock Density. The dataset indicated current areas of high cattle concentration. Areas where there is a high per capita livestock population, there is more likelihood of adoption of technologies that offer the potential to increase fodder availability for 48 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

50 livestock at the farm level and technologies such as push and pull provided the ability increase the dry matter available to animals. It is instructive to note that these factors were used in an integrated fashion as already explained above such that livestock per capita alone would not have been adequate to guide a decision for recommending for instance push and pull strategy. In the arid and semi arid pastoral and agro pastoral systems for instance, per capita livestock numbers may be high but the agro ecology and farming systems may not be conducive to planting of desmodium/napier. On the other hand, combination of smaller farm size, higher population densities associated with accessibility to roads or markets and higher agro climatic potential associated with sedentary high value livestock keeping such as fodder crop cut and carry systems (zero grazing), can be associated with those striga control strategies such as push and pull that produce Napier grass (Pennisetum purpureum), forage legumes such as desmodium as fodder for increased milk production. Other factors that were also considered were where stalk borers were a dominant problem (the push pull approach; tackles this) while existence of previous farm trials, experiments and adoption of various Striga control strategies was associated with a higher probability of uptake of the tested technologies. Recommendation for technology system matching Based on the above identified systems, a set of technology system maps were constructed (Figures 15 17). These have been designed to represent the technologies that are most likely to be relevant in each of the systems and countries. 49 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

51 Figure 15 Suggested appropriate Striga control strategies in Kenya 50 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

52 Figure 16 Suggested appropriate Striga control strategies in Tanzania 51 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

53 Figure 17 Suggested appropriate Striga control strategies in Uganda 52 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

54 In summary, these results point to the general trend that where the length of growing period was high (i.e. >270 days or humid) there was a higher the potential for adopting IR Maize Hybrid technology. Higher agricultural potentials provide the allowance for maximizing crop production associated with hybrid and multiple cropping seasons. Furthermore, legume rotation and intercropping in these locations would most likely further reduce parasitism and provide additional protection against Striga while at the same time generating extra income from sale of legumes. The locations are also almost associated with high altitude and thus temperate climatic conditions suitable for intensive rearing of cattle such as dairy. The legumes would provide fodder for the livestock. Since population is high in these localities, access to land is a significant constraint and control strategies such as use of desmodium which grows slowly would take up land that would otherwise been used to grow legumes for cash and are therefore not recommended here. In Kenya, the areas that fall under this category belong to sub humid to humid midlands and upper midlands districts. In Uganda these areas would belong to the small pockets of Striga in Kasese and Bushenyi in the Montane farming system of the west where maize, beans, wheat, millet, rice are grown. The level of poverty in these areas is usually moderate, with production very much market oriented. Where length of growing period LGP was in the range days i.e. sub humid, IR Maize open pollinated varieties are recommended. This zone is predominantly located around the lake Victoria basin in all the three countries, does not have an adequate LGP to support hybrid cereals but can readily support open pollinated varieties. Production is subsistence based and incidences of poverty are high meaning that the investment potentials are weak and the communities are risk averse and thus farmers cannot readily engage in high input agriculture This region is also characterized by high population pressure meaning access to land is still a significant constraint to production in these areas. As you move further inland however, land sizes begin to become slightly larger. In this area, apart from the open pollinated varieties of IR Maize, integration with push pull and intercropping with legumes is appropriate. Since these happen to be mixed systems where livestock are also kept, the push and pull system provides much needed fodder for the livestock. These are land constrained systems integrated with livestock in which: a portion of the livestock dry matter fed to animals is produced on the farm and in which annual average stocking rates are low at about 10 tropical livestock units per hectare of agricultural land. In Uganda this is the banana / finger millet / cotton farming system dominated by cotton, robusta coffee, beans & maize around the lake Victoria crescent and the the 53 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

55 areas around lake Kyoga. In Tanzania, this is represented by the Sorghum/Bulrush millet/livestock farming system found in Sukumaland, Shinyanga and rural Mwanza. Sorghum, millet, maize and cotton, oilseeds and rice are dominant crops here while the area also typically has intense population pressure. In Kenya this is the lake Victoria basin, primarily cereal production zone growing maize, millet and sorghum. The arid: (LGP of less than 75 days) and semi arid (LGP in the range days) are typically areas of low rainfall and systems here are represented mostly by the pastoral and agro pastoral systems in Tanzania and Uganda. In Tanzania this farming system is found in the semi arid areas of Dodoma, Singida, parts of Mara and Arusha; Chunya districts, Mbeya and Igunga district in Tabora. There is strong attachment to livestock and a simple cropping system including shifting cultivation primarily dominated by sorghum and millet. Population is mostly moderate (density 30 ppskm). In Uganda these are the northern farming system (Apac, Gulu, Kitgum, Lira, Pader) dominated by cereals of finger millet, sorghum and the pastoral systems around Moroto and Kotido in which some millet, sorghum, & maize are cultivated. 54 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

56 6.0 RETURNS TO INVESTMENTS IN STRIGA CONTROL 6.1 METHODOLOGY: The returns of Striga control were projected over the long term, from 2010 to 2030, using an economic impact model (Alston et al., 1998). Across all three countries, the economic returns, R, are given by the aggregate benefits generated by Striga control, B, less the sum of increased production costs required by Striga control, C, and external investment costs, I. Using this notation, the net social returns, R S, are written as: R S = B C I Private economic returns, those accruing directly to producers, exclude external investment costs. Private returns, R P, are given by the following: R P = B C Striga control generates its primary benefits from higher crop yields, ΔY. Certain technologies, such as PPT, provide additional benefits in the form of increased animal feed, ΔF, which are also included. Benefits are calculated at the most elemental level, i.e. administrative, by valuing the increased production at prevailing market crop and feed prices, P c and P f, as follows: B = P c ΔY + P f ΔF The increased production costs required of Striga control, C, is calculated by summing over the individual component costs. The added costs vary depending on technology type, but in general the added costs include improved seed, S, trap crop seed, TC, fertilizer, Z, labor, L, weeding costs, W, and harvesting costs, H. The costs are calculated at the unit level (per hectare basis) and then multiplied by the total production area, A, to arrive at the costs at the administrative level. Hence, the added costs, C, for an administrative unit are given by the following: C = (S + TC + Z + L + W + H)*A Investments made by donors, governments, and the private sector are also included in the aggregate economic returns. Each Striga control technology will have its own investment requirements. This will include investments in extension (farmer training, information dissemination, feedback and monitoring), seed multiplication, seed quality 55 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

57 monitoring, research and development (fine tuning existing technologies and innovations), advertising, credit, and marketing facilities. Donors will also provide inputs to producers for the first few growing seasons, including fertilizers, herbicides, and seeds. External investment costs are given by the following: I = EXT + SEED + RD + CRDT + MKT + INPUT where EXT are extension costs, SEED are the costs associated with seed multiplication and certification, RD are the research and development costs, CRDT are the credit costs, MKT are the market costs, and INPUT are the input production costs provided to producers for the initial years of the project. Time is an important factor in projecting economic returns from Striga control since benefits accrue gradually over time. Technology adoption is not an immediate process as only a small percentage of producers, the innovators, will be willing to adopt initially. Typically these are producers with fewer concerns over risk, often are more educated and better informed, and tend to have greater resource endowments. Over time, as the technology is disseminated and the remaining non adopters become better informed, more producers will adopt. It can take several years, however, before the late adopters and the laggards to adopt new technology. Adoption of agricultural technology has been found to follow an S shaped, logistic curve (Figure 18). Under adoption, economic returns in year t, R t, are given by the following: R t = NΩ ) ( 1 + e ) α(t T 0 where N is the total number of potential adopters, α is a coefficient that determines the rate of change of adoption over time, Ω is the maximum adoption (in percent), and T 0 is a parameter that modifies the shape of the curve. 56 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

58 90% 80% 70% Adoption Rate 60% 50% 40% 30% 20% 10% 0% Year Figure 18 Adoption curve illustrating the expected uptake of Striga control technology in East Africa. In certain cases, the benefits generated by new technology accrue gradually as well. In particular, trap crops and PPT typically require five years before they are able to achieve their maximum effect on crop yields. A ramp function, ψ(t), is used to model the time varying effects of trap crops and PPT, which is defined as follows: 0 ψ(t) = F(t) 1 if t T if t T r0 if t T r0 r1 & t < T r1 where T r0 and T r1 are points in time when the technology is first introduced and when its maximum benefits have been reached, and F(t) is the function that describes how returns from trap crops and PPT respond between T r0 and T r1. With the ramp function included, the overall return in year t is given by the following: NΩψ (t) Rt = αt (1 + e ) More generally, seed multiplication and producer training and learning require start up periods, and the development of credit and marketing facilities is also expected to take time. These are also factored into the economic returns using ramp functions as previously described. Overall economic returns are calculated using the net present value (NPV) approach to account for the time lag between when investments are made and when benefits 57 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

59 actually accrue. The NPV of a Striga control technology is calculated by summing the economic return, R, over the twenty year time horizon from 2010 to 2030, discounting future economic returns by the factor δ. The NPV of economic returns is given by the following: t=2030 NPV= R t δ t t=2010 Since investments are limited, the economic impact model maximizes economic returns subject to a given level of donor investments by selecting the optimal set of Striga control technologies. Using the subscript i to denote crop and j to denote the administrative unit, the optimal set of Striga control technologies are selected using the following integer math programming model: t=2030 Maximize NPV= X R δ t t=2010 By choosing X Subject to I i I max where X is the integer (0 1) decision variable. Hence, for a given level of funding, the math programming model identifies the optimal set of Striga control measures that maximize economic returns. By gradually increasing investment levels, a priority ranking of Striga control technologies was developed. This identified the optimal location and crops to target initial donor investments. 6.2: Yield Gains Striga control technology generates impacts through yield gains. A Striga damage Striga control model was used to determine the yield gain from Striga control. Yield damage from Striga is dependent on the intensity of Striga infestation, φ, which decreases yields from their potential, Y 0, under no Striga infestation. The exponential function is used to relate yield damage to intensity using the following form: Y = Y 0 e βφ where β is a parameter that relates yield damage to Striga intensity, φ. The corresponding yield damage, Y D, is given by the difference between the yield potential Y 0, and yield under Striga damage, Y: 58 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

60 Y D = Y 0 (1 e βφ ) Yield gains can also be achieved through co benefits and the use of new technology. Certain Striga control measures, such as PPT, provide yield co benefits by addressing other constraintss such as stem borer. The use of legume crops can enhance soil nutrient levels and increase yields. Effective Striga control, by reducing damage and increasing productivity, can also provide incentives for producers to adopt new technology and farm practices. In terms of the Striga damage model, co benefits and new technology can increase Y 0 beyond its level under Striga damage to Y F, the yield frontier. Hence, the overall yield gains are: Y = YY D + Y CB + Y NT Where Y CB and Y NT are the yield gains from the co benefits as well as the adoption of new technology. Figure 18 Yield damage-yie eld control modeling framework illustrating the impacts of Striga control. 59 Page EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo, J. Vitale, and J. Sanders Consultants for Kilimo Trust. April, 2009.

61 6.3 Impact Model Data The model considered seven Striga control alternatives, including those which bundled two or more technology packages together. The economic model was developed using various data sources, including field trials conducted throughout East Africa. Table 9 lists the yield gains that various Striga control technologies have achieved for maize and sorghum. The amount of yield damage recovered, ΔY D, varied across the Striga control technologies. Trap crops, including PPT, have the lowest yield gains since their yields are time dependent and it takes an average of five years for these technologies to reach maximum effectiveness. Under High Striga infestation, where 75% yield losses occur, trap crops and PPT would recover 64.1% of the maize yield. The unrecovered portion, 10.9%, is explained by the partial control efficacy of trap crops, 95% at full efficacy, and the time (five years) required achieving maximum yields. IR maize has higher yield gains than trap crops and PPT since IR maize obtains its maximum control efficacy of 95% immediately in its first year of operation. Under High infestation, IR maize generates a yield gain of 71.3%, which is 7.2% higher than the yield gains from trap crops or PPT. Combining IR maize with the trap crops and PPT provides the highest yield gains since the control efficacy is increased to 98% since both types of technology are able to control Striga. Hence, the bundling of PPT with seed coating technology has particular appeal since each complements the other. Seed coating provides immediate yield gains, while PPT addresses the long term problem of reducing the Striga seed bank. Under High infestation, yield gains are 73.5% with the integrated technology of IR maize and either trap crops or PPT, corresponding to a 2.2% increase over IR maize s yield gains. Trap crops, including PPT, generate co benefits for producers. The co benefits include animal fodder production, nitrogen fixation, and stem borer control (PPT only). Typically, trap crops and PPT produce between 7 and 12 MT per ha of high quality, leguminous animal fodder. By fixing nitrogen, trap crops and PPT act as a green manure, supplying the soil with about 20 kg per ha of nitrogen. Using a nitrogen response rate of MT per N Kg, the added nitrogen translates into a yield gain of 0.46 MT per ha. Push pull technology also provides stem borer control, which provides an additional yield gain of about 15%. Once Striga is adequately controlled, producers can further enhance performance with new technology. Hybrid maize and sorghum varieties can be grown to increase yields by 21% and 16%. On maize, applied fertilizer increases yields by MT per kg N and by MT per kg N on sorghum. The actual amount applied is site dependent since nitrogen response reaches a plateau once plant uptake requirements are satisfied, 60 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

62 which varies on climate, soils, and cropping system, including the contribution from green manures. When applied on sorghum, Striga control technologies generate lower yield gains, particularly under new technology since sorghum responds less favorably to improved germplasm and nitrogen than maize. Since data was available at only limited number of locations, extrapolation techniques were used to project outcomes at adjacent and remote locations (Appendix). Since no data was available for rice and millet, the model used maize yield gains for rice and sorghum yield gains for millet. Table 9 Crop yields and co-benefits associated with Striga control technologies used in the economic impact model. ΔY D ΔY CB ΔY NT Intensity Co Benefit Technology Technology Low a Med High Stemborer Forage (tons/ha) N Fixation (MT/ha) Improved Variety Fertilizer MT/ (ha Kg N) Maize Trap Crop (TC) 8.5% 42.3% 64.1% PPT 8.5% 42.3% 64.1% 15% IRM 9.5% 47.5% 71.3% TC +IRM 9.8% 49.0% 73.5% PPT+IRM 9.8% 49.0% 73.5% 15% TC + IRM +NT 9.8% 49.0% 73.5% % PPT + IRM +NT 9.8% 49.0% 73.5% 15% % Sorghum Trap Crop (TC) 8.1% 41.1% 62.1% PPT 8.1% 41.1% 62.1% 15% IRM 9.2% 46.4% 69.9% TC +IRM 9.7% 47.6% 72.7% PPT+IRM 9.7% 47.6% 72.7% 15% TC + IRM +NT 9.7% 47.6% 72.7% % PPT + IRM +NT 9.7% 47.6% 72.7% 15% % Sources: Kilimo Trust Field Surveys; Khan and Pickett, a Yield gains are measured relative to yield under no Striga infestation, Y 0. For instance, a 75% yield gain is equal to 0.75*Y 0. Low, Med, and High represent Striga severity infestation levels with corresponding yield damage of 10%, 50%, and 75%. b Stem borer co benefits are generated by push pull technology. Yield gains are measured relative to yield under no Striga infestation, Y 0. A 15% yield gain, for instance, is equal to 0.15*Y P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

63 The increased production costs incurred with each of the Striga control technologies is listed in Table 10. The push pull technology has the largest unit cost, primarily due to the increased labor and start up costs for the Napier grass and Desmodium seeds. Table 10 Increased production costs incurred by producers in the adoption of Striga control technologies Item ($/ha) PPT a IR Maize b IR Maize + PPT+IR Maize Trap Crop Fertilizer + Fertilizer Labor Napier Splits Desmodium Seeds Maize Seed Bean seed Herbicide Fertilizer Total a See Table 3, Khan et al. (2008). Labor costs reflect average costs over the first five years of PPT. b Cost for OPV producer. Seeding rate of 20 kg per ha. Herbicide applied at kg per ha. Investments from donor and government agencies are listed in Table 11. The investments are included in the model on a unit (per hectare) basis, entering the model as scale neutral. This overstates their cost somewhat since certain investments are likely to contain an economy of scale, i.e. lowered unit costs as investments are increased. For the first five years, donors provide the production inputs required by Striga control, thus investments will vary by Striga control technology. This typically includes seeds, chemicals, and fertilizers. After this initial period, when Striga control is presumed working at full efficiency, producers are charged with purchasing inputs under provided credit. Seed supply is increased over time, and in some investment scenarios could be limiting in the first few years. Extension costs are projected based on the increased frequency of visits, longer duration of existing visits, and increased costs of providing information apart from visits. Extension costs are required only for the first five years of introduction, after which implementation 62 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

64 Table 11 Donor investment costs required for implementation and uptake of Striga control technologies Item ($/ha) PPT IR Maize IR Maize + PPT+IR Maize Trap Crop Fertilizer + Fertilizer Production Inputs Extension Visits Information Seed Certification Seed Multiplication R&D Marketing & Infras Total a See Table 3, Khan et al. (2008). Labor costs reflect average costs over the first five years of PPT. b Cost for OPV producer. 7.0 SECTOR IMPACTS: OPPORTUNITIES, EQUITY, AND CONSTRAINTS The economic impacts of introducing Striga control measures are summarized in Table 8. Impacts are presented as the NPV since benefits accrue gradually whereas investments and other costs are made in the beginning. A 15 percent discount rate was used to discount future benefit and cost streams to 2010, the starting year of the Striga control programme. The adoption rate was 80 percent, and took place over a ten year period following the S shaped logistic curve of adoption illustrated above. 7.1 Economic Returns of Striga control For Kenya, the total NPV of economic benefits would be $465 million, with an associated cost of $139 million that producers would incur (Table 12). The overall NPV of net economic returns for Kenya would be $326 million. Maize would generate both the highest economic returns and benefit cost ratios compared to sorghum and millet. The benefit cost ratio for producers, B/c, would be 3.34, indicating a sufficiently high rate of return for Kenyan producers. For this level of adoption, 80 percent, donor investment 63 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

65 would be $11.5 million, spread out over the first five years of the Striga control programme. For donors, the benefit cost ratio would be slightly lower than the producers, with a benefit cost ratio of Hence, for every dollar invested a net social return of $2.08 would be obtained. Tanzania would generate the largest NPV of economic gains, totaling $1.53 billion over the twenty year investment period (Table 12). This is explained since Tanzania has both the largest number of acres under Striga and would generate the largest benefit cost ratios. For instance, the producer and donor s benefit cost ratios would be more than twice as high in Tanzania than in Kenya. Uganda would generate the lowest NPV of economic gains, $167 million (Table 8). Uganda s largest impacts would be found in the Lake Victoria region, where impacts of $103 million would be generated, primarily from maize (Table 12). Although Uganda s aggregate economic impacts would be the lowest of the three countries, its benefit cost ratios would be slightly larger than Kenya s. 7.2 Donor Investments and Benefit Cost Ratios The total NPV of benefits generated throughout East Africa would be $2.15 billion across the twenty year investment period from 2010 through 2030 (Table 12). To achieve this level of benefits, producers would incur an additional $380 million in production costs and donor investments of $39.3 million would be required. These impacts correspond to an overall net economic return of $1.77 billion, and benefit cost ratios of 5.65 for producers and 5.12 for donors. The largest economic impacts would be generated by maize, with an NPV of $1.12 billion in economic benefits. Sorghum would generate an NPV of $354 million in economic benefits, millet $4.09 million, and rice an additional $666 million. 64 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

66 Table 12 Economic impacts of Striga control measures in East Africa: Country Crop Production Gains (tons per year) NPV of Economic Benefits (B) $ NPV of Producer Costs (c) $ NPV of Donor Investments (I) $ Producer Benefit Cost B/c Donor Benefit Cost B/(I+c) Kenya Lake V. Maize 256, ,424,694 38,216,091 3,825, Sorg. 54,692 25,694,010 17,936,971 1,795, Millet 1, , ,859 41, Midland Maize 441, ,615,251 73,016,008 4,910, Sorg. 36,558 17,174,852 7,843, , Millet 5,144 3,291,309 1,915, , Total 795, ,998, ,339,605 11,549, Tanzania Lake V. Maize 372, ,322,901 34,593,503 4,912, Sorg. 226, ,130,141 8,897,799 1,805, Rice 350, ,366,571 23,728,547 3,369, N Central Maize 133,766 97,806,246 15,397,776 2,186, Sorg. 180, ,794,382 7,890,029 1,600, Rice 2,820 3,092, ,824 94, Southern Maize 396, ,138,106 74,824,197 6,639, Sorg. 76,126 35,763,481 4,150, , Rice 235, ,754,502 32,585,712 2,891, Total 1,975,507 1,527,169, ,734,167 24,343, Uganda Bushenyi Maize 5,264 3,195, ,385 77, Sorg ,391 74,600 8, Rice ,591 41,795 5, L. Victoria Maize 139,718 84,806,134 18,247,655 2,192, Sorg. 7,571 4,177, , , Rice 17,104 14,156,998 2,233, , NE S arid Maize 50,048 30,378,172 9,499,519 1,141, Sorg. 3,639 2,008, ,768 86, Rice 5,192 4,297, ,567 97, N central Maize 25,273 21,182,055 6,355, , Sorg. 2,485 1,893, ,799 60, Rice 1,965 2,245, ,884 37, Total 259, ,710,007 40,312,578 4,609, East Africa Maize 1,821,468 1,127,618, ,006,075 25,415, Sorg. 588, ,541,697 49,069,274 7,032, Millet 6,391 4,089,675 2,326, , Rice 613, ,092,026 60,313,106 6,658, Total 3,030,483 2,152,342, ,715,434 39,339, P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

67 7.3 Alternative Levels Donor Investments An alternative range of donor investments was considered to investigate how economic returns responded as donor investments were increased (Figure 19). Most of the benefits from Striga control would be obtained with an investment of $20 million, onehalf of the $40 million investment required to reach the presumed full adoption (80%). With a $20 million donor investment, the NPV of economic benefits would be $1.57 billion, or 73.3% of the $2.15 billion in economic benefits generated with an investment of $40 million. An investment of $30 million would increase the NPV of economic benefits to $2.03 billion, which would be nearly 95% of the benefits generated at the maximum investment of $40 million. Hence, the findings indicate diminishing returns from investments beyond $30, suggesting that donor investments need not go beyond $30 million. The effect of diminishing returns is also evident in the social benefit cost ratios (B/(I+c)) across the alternative range of donor investments. At $10 million, a social B C ratio of 10.5 would be obtained, which would fall only modestly as investments are increased to $20 and $30 million, where social B C ratios would be 9.10 and Investing beyond $30 million, however, would result in a significant decline in the social B C ratio. 700 B/(I+c) = 5.12 Economic Benefits ($ million) B/(I+c) = 8.68 B/(I+c)=9.10 B/(I+c) = Year 10 million 20 million 30 million 40 million Figure 19 Economic benefits of Striga control programme in East Africa at alternative levels of donor investments. Economic benefits are primarily generated within the initial $30 million, with only marginal returns from the last $10 million. 66 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

68 7.4 Impacts across Technologies The economic benefits and corresponding returns that would be generated by the suite of Striga control technologies is illustrated in Figure 20. Over one half of the economic benefits and economic returns would be generated by just two of the technologies, trap crop and the combined use of trap crops with IR maize (IRMZ + TC). Together, these two technologies would generate a NPV of $1.16 billion in economic returns over the 20 year planning horizon from 2010 to Important returns would also be generated by PPT when combined with IR maize, which would generate $0.31 billion in net economic returns. Striga tolerant sorghum (STS) would provide an additional $0.28 billion in net economic returns. The results emphasize the need for integration of the Striga control technologies. Nearly one half of the net economic returns, 48.5%, would be obtained by combining IR maize with either a trap crop or with PPT. Moreover, virtually all of the returns generated by trap cropping, $0.61 billion, would be from upland rice where integrated technology has yet to be developed. 2,500 2,000 1,771 Benefit & Cost ($ million) 1,500 1, IRMZ + TC PPT Trap Crop PPT+IRMZ STS Total Striga Control Technology Benefits Inc. Prod. Cost Net Returns Figure 20 Net present value of benefits, increased production costs, and net returns generated by Striga control technologies. 67 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

69 7.5 Production Gains The impacts of introducing Striga control measures on maize production are illustrated in Figure 20. Across the East Africa study region, significant production gains can be can be achieved through introducing Striga control measures. A total of 1.82 MMT of maize would be added to current production levels across East Africa, with most of the increased production coming from Kenya, MMT, and Tanzania, MMT (Table 12). The impacts would be more modest in Uganda and Tanzania, yet still significant, with an increase of MMT. The primary areas where the largest production gains could be achieved are in around Lake Victoria in Kenya, the Central regions of Tanzania and other areas south of Lake Victoria, and in a few isolated areas in Uganda (Figure 14). In Kenya, the largest production gains are generally found in zones with the most Severe Striga infestation around Lake Victoria. The increased maize production would reach as high as 40,000 tons per year in some divisions, enough food to feed an additional 3,000 persons per year. In Tanzania the production gains are more widespread. In the Central regions, a total increase of 155,000 tons of maize production could be achieved through Striga control. In the areas south of Lake Victoria, the maize production gains would total 145,405 tons. Throughout the rest of Tanzania, Striga control would achieve an additional 59,400 tons of maize production. 68 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

70 Figure 21 Annual economic gains in Tanzania. 69 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

71 Figure 21 Annual economic gains in Uganda and Kenya. 70 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

72 7.6 Increased Per Capita Production On a per capita basis, the production gains from introducing Striga control technologies are illustrated in Figure21. This measure of performance provides is more meaningful when considering the impacts on household welfare, reflecting how households are able to either reduce their dependence on food purchases or increase their marketed surplus. In the heavily Striga infested areas of Kenya, per capita maize production would increase by as 1,500 kg per person. This would generate a cereal surplus since only about 110 kg per person is required. Hence, these regions could export enough food to feed an additional 13 persons. Similarly, areas with per capita increases between kg per person would also be able to generate a surplus, although a more modest increase between 2 3 persons. The less heavily infested areas have increases between kg per person. While not enough to generate a surplus on its own, it is enough to significantly increase caloric intake in households, reduce food expenditures, and in some cases enable households to generate a surplus. Tanzania has more limited areas capable of generating a surplus, with only one region increasing production much beyond 150 kg per person. On average, Tanzania would increase maize production by 34.4 kg per person, about one third the increase in Kenya, where maize production would increase by an average of 98.8 kg per person. 71 Page EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo, Figure 23 Per capita J. Vitale, production and J. Sanders gains. Consultants for Kilimo Trust. April, 2009.

73 8.0 THE WAY FORWARD: ACTORS INVESTMENTS, AND POLICY Striga is a major threat to staple food in East Africa that will only grow more ominous over time. With recent advances in crop protection technology, policy makers now have viable alternatives to win the battle against Striga. This research found that both pushpull and IR maize technologies could significantly reduce Striga losses, providing the East Africa region with $1.77 billion in benefits and 3.03 million tons of increased maize production over the next 20 years. 8.1 The actors and their roles In order to reap such benefits, investments and new policies will be required. Various actors will need to facilitate the successful introduction of Striga control technology (Table 13). This will be challenging since existing linkages among the actors are either weak or non existant, particularly in rural areas. Throughout East Africa, many areas remain in subsistence based agriculture where households have scant interaction with the agribusiness sector. Incorporating producers in the agribusiness sector will take time, but will become easier to facilitate once producers begin to realize the benefits and levels of trust are obtained. Producers are the primary implementer of Striga control measures. While producers will be Figure 24 Striga control will require the provision of inputs by the among the primary beneficiaries of Striga supply chain. For some farmers this will be their first time control, they will also be asked to adopt accessing purchased inputs on an annual basis. Donors and NGO s will need to provide adequate policy measures so farmers have new farming techniques and to take on access to credit through farmer associations, cooperatives, or new production challenges. For this to be private lender. achieved, producers will need to be linked to new institutions, policies, and a stronger value chain. Local farm associations can play a critical role in providing micro credit to producers. This is likely to include cereal banks to improve the marketing posture of smallholder farmers, and through revolving credit can provide farmers with the financing required in adopting Striga control measures. The development of farm associations will likely require external monitoring as mismanagement and corruption have often marred their performance. 72 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

74 Table 13 Various actors will need to coordinate their activities in order for Striga control to be successful. Each actor will need to fulfill their roles and maintain linkages with other actors. Actor Roles Contributions/Benefits Producers Local Farm Associations International Research Institutions National Agricultural Research Organizations Agribusiness Sector Donors Extension Agencies Primary implementer of Striga control measures. Provide local means of credit Cereal banks to improve marketing posture of smallholder farmers Generate continued innovations in Striga control Provide on going solutions to constraints using advanced scientific methods Mapping of Striga infestation Incorporate IR maize technology in local varieties as technically feasible Maintain variety lines. Establish recommended practices for technology practices Establish testing to validate new technologies Assist farmers in developing ways to adapt and fine tune Striga control technology Seed replication Fertilizer procurement Distribution and marketing of production Value added for livestock fodder Micro credit Increase priority for Striga control programs Conduct farmer workshops to disseminate information on Striga control measures Provide training and establish recommended practices Provide technical support Land and labor Mangement New Striga control technologies New and adapted technologies New marketing opportunities Financial support Increase likelihood of uptake. 73 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

75 Monitor and document adoption process. 8.2 The Agribusiness Sector The agribusiness sector has many roles to assist farmers in introducing Striga control measures. Seed companies will need to quickly ramp up production to supply producers with new products such as IR maize, while maintaining adequate seed quality. Once full adoption is reached (80% rate), an annual supply of 15,889 MT of IR maize would be required. Because of lower seeding density and lower uptake, Desmodium seed supply would reach 322 MT per year. In both cases, demand would increase gradually as illustrated in Figure 24. Input supply channels will need to be expanded to include the distribution of chemical inputs such as herbicides and fertilizers. By 2024, when full adoption has effectively been reached, 15,334 MT of nitrogen fertilizer would need to be supplied, along with 23.8 MT per year of herbicide. 18, IR Maize Seed (MT per year) 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2, Desmodium Seed (MT per year) IRMZ Figure 25 Demand for IR maize and Desmodium seed under the full adoption scenario (80% adoption rate). Year Desmodium 0 74 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

76 The agribusiness sector will also be challenged to find new markets and alternative uses of products. This includes establishing a Striga control program will also require the strengthening of existing linkages along the agricultural value chain, including the creation of new markets to handle the influx of cereal production. While food security is a primary concern in the region, in better years of production food surpluses can be generated. This will be especially true for livestock fodder produced as part of trap cropping and PPT systems. Without adequate demand, increased production from Striga control could put downward pressure on prices and erode benefits to producers. Hence, efficient distribution and innovative marketing of production will be required. The agribusiness sector can also provide micro credit financial services to assist producers in purchasing inputs. Figure 26 A Striga control program will require an overall strengthening of the value chain for the cereal crops. 75 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

77 8.3 Investments Implementing the envisioned Striga control program would provide significant benefits to the upper portions of the maize value chain, including producers, but would also require substantial downstream investments and supporting policies. On the investment side of the value chain, national and international research centers will be called on to develop innovations in new Striga control technology. This will include incorporating feedback from producers, via extension services, to target constraints and problems associated with adopted technologies. National research centers can facilitate by helping farmers adapt technology to their local needs. From the onset, policy measures will be required to shift more resources into agricultural development. This needs to be done at the national and regional level, with assistance from outside donors. Investments in agricultural R&D have been falling over the past decades in sub Saharan Africa, which will need to be reversed in order for subcontinent to achieve its full agricultural potential. Other supporting policies will also need to be enacted within the agricultural sector. This will include continued funding for extension services and agricultural development agencies. While the government will have a continued role in the agricultural sector, it should also encourage the private sector to take over as many roles as possible. 76 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

78 Figure 27 A Striga control program would provide significant benefits to the upper portions of the maize value chain, including producers, but would also require substantial downstream investments. 8.4 RECOMMENDATIONS The complex nature of Striga infestation precludes blanket recommendations throughout East Africa. Rather, recommendations are given for each of the major Striga infestation regions, and further broken down along agro potential gradients. Recommended new technology packages include combinations of Striga control practices since successful Striga control strategies must: Target Striga control beneath the soil surface to prevent Striga emergence and procure immediate (same season) benefits. Provide long run Striga control through depleting Striga seed bank. Generate positive economic returns. Fit within the constraints of existing farming systems. 77 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

79 In the Lake Victoria Region, our recommended course of action is a combination of trap crops with an improved variety, containing some form of resistance to Striga infection. Resistance could be achieved through genetics or through synthetic means, such as IR maize. This is a highly complementary technology package that compensates for each other s deficiencies, in turn generating the highest returns. For instance, resistant varieties provide immediate benefits but do little to reduce Striga seed banks, unlike trap crops that reduce seed banks but take multiple seasons to achieve their full benefits. Trap crops IR maize It benefits from the immediate effectiveness of IR maize and the breakdown of the Striga bank over the long term. The most attractive trap crop is desmodium. It provides the best Striga control, factoring in the multiple year horizon over which its benefits accrue. Improved soil fertility has many attractive features, but is not likely to be economically feasible throughout the region. 78 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

80 REFERENCES Agbobli, C.A Effect of nitrogen rates on Striga asiatica emergence on maize culture in Togo, p , In J. K. Ransom, et al., eds. Proceedings of the 5th International Symposium on Prasitic Weeds. CIMMYT, Nairobi. Aliyu, L., S.T.O. Lagoke, R.J. Carsky, J. Kling, O. Omotayo, and J.Y. Shebayan Technical and economic evaluation of some Striga control packages in maize in the Nigerian Guinea Savanna. Crop Protection 23: Badu Apraku, B., and A.F. Lum Agronomic Performance of Striga Resistant Early Maturing Maize Varieties and Inbred Lines in the Savannas of West and Central Africa. Crop Science 47: Baguma, S.D., and G. Bigirwa Occurance of Striga species in western Uganda, p African Crop Science Vol. 1. African Crop Science Society. Berner, D.K., F.O. Ikie, and E.I. Aigbokhan some control measures for Striga hermonthica utilizing critical infection period on maize and sorghum. Proc. East. south. Afr. Maize Conf. Maize Res. Stress Environ. 4th. CIMMYT, Harare, Zimbabwe. Berner, D.K., J.G. Kling, and B.B. Singh Striga research and control: A perspective from Africa. Plant Dis. 79: Carsky, R.J., L. Singh, and R. Ndikawa Suppression of Striga hermonthica on sorghum using a cowpea intercrop. Exp. Agric. 30: Carsky, R.J., D.K. Berner, B.D. Oyewole, K. Dashiell, and S. Schulz Reduction of Striga hermonthica parasitism on maize using soybean rotation. International Journal of Pest Management 46: Combari, A., R. Pineau, and M. Schiavon Influence du degre de decomposition de produits organic sur la germination de graines de Striga hermonthica. Weed Research 30: De Groote, H Striga economics, p , In J. Gressel and G. Ejeta, eds. Integrating new technologies for Striga control: Towards ending the witch hunt. World Scientific Publishing, Hackensack, NJ. De Groote, H., and F. Kanampiu Herbicide Resistant Maize Technology to Combat Striga in Africa, p , In P. Pardey, et al., eds. Science, technology and skills. International Science and Technology Practice and Policy (InSTePP) center, University of Minnesota. De Groote, H., L. Wangare, and F. Kanampiu Evaluating the use of herbicide coated imidazolinone resistant (IR) maize seeds to control Striga in farmers fields in Kenya. Crop Protection 26: P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

81 De Groote, H., L. Wangare, F. Kanampiu, M. Odendo, A. Diallo, and D. Friesen. 2005a. Potential markets for herbicide resistant maize seed for Striga control in Africa. Poster paper presented at the European Association of Agricultural Economists Congress, Copenhagen, Denmark, August 2005 ( De Groote, H., L. Wangare, F. Kanampiu, M. Odendo, A. Diallo, H. Karaya, and D. Friesen Potential for herbicide resistant maize seed for Striga control in Africa. Agricultural Systems 97: De Groote, H., Z. Khan, J. Kikafunda, D. Nyagol, G. Odhiambo, I. Rwiza, F. Kanampiu, and B. Vanlauwe. 2005b. Participatory assessment of Striga, stemborer and soil fertility problems in the Lake Victoria Basin. "Integrated Pest and Soil Management to Combat Striga, Stemborers and Declining Soil Fertility in the Lake Victoria Basin" Project, Nairobi, Kenya. Debrah, S.K., and D. Sanogo Ex ante evaluation of the profitability and adoption Potential of 2,4 D for Striga control: A contingent valuation analysis ICRISAT, Bamako, Mali. Debrah, S.K., T. Defoer, and B. M'Pie Integrating farmers' knowledge, attitude and practice in the development of sustainable Striga control technologies. Netherlands Journal of Agricultural Sciences 46: Diallo, A.O., F. Kanampiu, S. Mugo, H. De Groote, and P. Mbogo. 2005a. Herbicide Resistant Maize: A Novel Method to Control Striga in Africa. Diallo, A.O., F. Kanampiu, S. Mugo, H. De Groote, and P. Mbogo. 2005b. Herbicide Resistant Maize: A Novel Method to Control Striga in Africa, Paper presented at the 5th West and Central Africa Biennial Regional Maize Workshop, 2 6 May 2005, IITA Cotonou, Benin. Diallo, A.O., F. Kanampiu, S. Mugo, H. De Groote, and P. Mbogo Herbicide resistant maize: a novel method to control Striga in Africa, p , In B. Badu Apraku, et al., eds. Demand Driven Technologies for Sustainable Maize Production in West and Central Africa. Proceedings of the fi fth biennial regional maize workshop, IITA Cotonou, Bénin, 3 6 May, WECAMAN/IITA, Ibadan, Nigeria. Dugje, I.Y., A.Y. Kamara, and L.O. Omoigui Infestation of crop fields by Striga species in the savanna zones of northeast Nigeria. Agriculture, Ecosystems & Environment 116: Franke, A.C., J. Ellis Jones, G. Tarawali, S. Schulz, M.A. Hussaini, I. Kureh, R. White, D. Chikoye, B. Douthwaite, B.D. Oyewole, and A.S. Olanrewaju Evaluating and scaling up integrated Striga hermonthica control technologies among farmers in northern Nigeria. Crop Protection 25: P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

82 Frost, H KARI/ODA/CIMMYT Striga Research Programme Final report, Kisumu, Kenya Agricultural Research Institute. Frost, H Striga Hermonthica surveys in Western Kenya, p Brighton Crop Protection Conference Weeds 1995, Brighton. Gacheru, E., and M.R. Rao Managing Striga infestation on maize using organic and inorganic nutrient sources in western Kenya. Intl. J. Pest Manage. 47: Gacheru, E., M.R. Rao, B. Jama, and A. Niang The potential of agroforestry to control Striga and increase maize yields in Sub Saharan Africa, p Maize Production Technology for the Future: Challenges and Opportunities, Proceedings of the Sixth Eastern and Southern Africa Regional Maize Conference, September, CIMMYT (International Maize and Wheat Improvement Center) and EARO (Ethiopian Agricultural Research Organization), Addis Ababa, Ethiopia. Gressel, J Problems in qualifying and quantifying assumptions in plant protection models: Resultant simulations can be mistaken by a factor of million. Crop Protection 24: Gressel, J., and G. Ejeta, (eds.) Integrating new technologies for Striga control: Towards ending the witch hunt,. World Scientific Publishing, Hackensack, NJ. Gurney, A.L., M.C. Press, and J.K. Ransom The parasitic angiosperm Striga hermonthica can reduce photosynthesis of its sorghum and maize hosts in the field. J. Exp. Bot.: Hassan, R., and J.K. Ransom Determinants of the incidence and severity of Striga infestation in Maize in Kenya, p , In R. M. Hassan, ed. Maize Technology Development and Transfer. A GIS Application for Research Planning in Kenya. CAB International, Oxon, UK. Hassan, R., J.K. Ransom, and J. Ojiem The spatial distribution and farmers' strategies to control Striga in maize: survey results from Kenya, p , In D. C. Jewell, et al., eds. Maize Research for Stress Environments, Proceedings of the Fourth Eastern and Southern Africa Regional Maize Conference, held at Harare, Zimbabwe, 28 March 1 April, CIMMYT, Mexico D.F. Kabambe, V.H., A.E. Kauwa, and S.C. Nambuzi Role of herbicide (metalachlor) and fertilizer application in integrated management of Striga asiatica in maize in Malawi. African Journal of Agricultural Research 3: Kanampiu, F Success with the Low Biotech of Seed Coated Imidazolinone Resistant Maize, In J. Gressel and G. Ejeta, eds. Integrating new technologies for Striga control: Towards ending the witch hunt. 81 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

83 Kanampiu, F.K., J.K. Ransom, and J. Gressel Imazapyr seed dressings for Striga Control on acetolactate synthase target site resistant maize. Crop Protection 20: Kanampiu, F.K., J.K. Ransom, D. Friesen, and J. Gressel Imazapyr and pyrithiobac movement in soil and from maize seed coats to control Striga in legume intercropping. Crop Protection 21: Kanampiu, F.K., V. Kabambe, C. Massawe, L. Jasi, D. Friesen, J.K. Ransom, and J. Gressel Multi site, multi season field tests demonstrate that herbicide seed coating herbicideresistance maize controls Striga spp. and increases yields in several African countries. Crop Protection 22: Khan, Z Field Developments on Striga Control by Desmodium Intercrops In J. Gressel and G. Ejeta, eds. Integrating new technologies for Striga control: Towards ending the witchhunt. Khan, Z., R.A. Hassanali, W. Overholt, T.M. Khamis, A.M. Hooper, J.A. Pickett, L.J. Wadhams, and C.M. Woodcock Control of Witchweed Striga hermonthica by Intercropping with Desmodium spp., and the Mechanism Defined as Allelopathic zone. Journal of Chemical Ecology 28: Khan, Z.R., J.A. Pickett, L.J. Wadhams, and F. Muyekho Habitat management strategies for control of cereal stemborers and Striga weed in maize based farming systems in Kenya. Insect Science and its Application 21: Khan, Z.R., J.A. Pickett, J. Van den Berg, L.J. Wadhams, and C.M. Woodcock Exploiting chemical ecology and species diversity: stemborer and Striga control for maize and sorghum in Africa. Pest Management Science 56: Khan, Z.R., J.A. Pickett, L.J. Wadhams, A. Hassanali, and C.A.O. Midega Combined control of Striga hermonthica and stemborers by maize Desmodium spp. intercrops. Crop Protection 25: Khan, Z.R., C.A.O. Midega, E.M. Njuguna, D.M. Amudavi, J.W. Wanyama, and J.A. Pickett Economic performance of the push pull technology for stemborer and Striga control in farming systems in western Kenya. Crop Protection (forthcoming). Kim, S.K., and V.O. Adetimirin Striga hermonthica seed inoculum rate effects on maize hybrid tolerance and susceptibility expression. Crop Science 37: Kim, S.K., V.O. Adetimirin, C. The, and R. Dossou Yield losses in maize due to Striga hermonthica in West and Central Africa. International Journal of Pest Management. 48: P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

84 S. T. O. Lagoke, et al. (ed.) Second General Workshop of the Pan African Striga Control Network (PASCON), Nairobi, Kenya June Mboob, S.S A regional programme for Striga control in West and Central Africa, p , In T. O. Robson and H. R. Broad, eds. Proceedings of the FAO/OAU All African Government Consultation on Striga Control, Marova (Cameroon). FAO, Rome. Mbwaga, A.M Status of Striga species in Tanzania: Occurence, distribution and on farm control packages., p , In K. Leuschner and C. S. Manthe, eds. Drought tolerant crops for southern Africa, SADC/ICRISAT regional sorghum and pearl millet workshop, Gaborone, Botswana. ICRISAT, Pantancheru, AndhraPradesh, India. Mbwaga, A.M., and A.T. Obilana Distribution and host specificity of Striga asiatica and S. hermonthica on cereals in Tanzania: a preliminary study. International Jounal of Pest Management 39: Mbwaga, A.M., R.I. Lamboll, and C.R. Riches. 1998a. The Striga Problem in Dodoma Region and the Lake Zone of Tanzania: Analysis of the Problem and Research Priorities Natural Resources Institute, Chatham, Kent. Mbwaga, A.M., Z. Mndruma, and J. Kaswende. 1998b. Integrated Striga control in maize for small scale farmers in Tanzania: Survey report from Ruvuma region. Mloza Banda, H.R., and V.H. Kabambe Integrated Management for Striga Control In Malawi. African Crop Science Journal 5: MULEBA, N., J.T. OUEDRAOGO, and J.B. TIGNEGRE Cowpea yield losses attributed to striga infestations. Journal of Agricultural Science 129: Mullen, J.D., D.B. Taylor, M. Fofana, and D. Kebe Integrating long run biological and economic considerations into Striga management programs. Agricultural Systems 76: Odhiambo, G., H. De Groote, Z. Khan, J. Kikafunda, D. Nyagol, I. Rwiza, F. Kanampiu, and B. Vanlauwe Participatory assessment of striga, stemborer and soil fertility problems in the Lake Victoria Basin. Unpublished Manuscript, Nairobi. Odhiambo, G.D., and J.K. Ransom Long term strategies for Striga control, p , In D. C. Jewell, et al., eds. Maize Research for Stress Environments, Proceedings of the Fourth Eastern and Southern Africa Regional Maize Conference, held at Harare, Zimbabwe, 28 March 1 April, CIMMYT, Mexico D.F. Oswald, A Striga control technologies and their dissemination. Crop Protection 24: P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

85 Oswald, A., and J.K. Ransom Striga control and improved farm productivity using crop rotation. Crop Protection 20: Oswald, A., and J.K. Ransom Response of maize varieties to Striga infestation. Crop Protection 23: Oswald, A., J.K. Ransom, J. Kroschel, and J. Sauerborn Suppression of Striga on Maize with intercrops, p Proceedings of the sixth Eastern and southern Africa regional maize conference. CIMMYT (International Maize and Wheat Improvement Center) and EARO (Ethiopian Agricultural Research Organization), Addis Ababa, Ethiopia. Oswald, A., J.K. Ransom, J. Kroschel, and J. Sauerborn Intercropping controls Striga in maize based farming systems. Crop Protection 21: Parker, C Protection of crops against parasitic weeds. Crop Protection 10:6 22. Pieterse, A.H., and J.A.C. Verkleij Effect of Soil Conditions on Striga Development A Review, p , In J. K. Ransom, et al., eds. The 5th International Symposium on Parasitic Weeds. CIMMYT, Nairobi. Riches, C.R., (ed.) Striga Distribution and Management in Tanzania. Proceedings of a Stakeholder Workshop, September 1999 Dar Natural Resources Institute, Chatham, Kent. Rutto, E., H. De Groote, B. Vanlauwe, F. Kanampiu, G. Odhiambo, and Z. Khan Farmers' Perceptions and Evaluation of Integrated Approaches to Combat Striga, Stemborer and Soil Fertility Problems in Western Kenya: Preliminary Results Paper presented at the 7th African Crop Science Conference, Entebbe, Uganda, 5 9 December Sanginga, N., K.E. Dashiell, J. Diels, B. Vanlauwe, O. Lyasse, R.J. Carsky, S. Tarawali, B. Asafo Adjei, A. Menkir, S. Schulz, B.B. Singh, D. Chikoye, D. Keatinge, and R. Ortiz Sustainable resource management coupled to resilient germplasm to provide new intensive cereal grain legume livestock systems in the dry savanna. Agriculture, Ecosystems & Environment 100: Saueborn, J The economic importance of the phytoparasites Orobanche and Striga, p , In J. K. Ransom, et al., eds. Fifth international symposuim on the parasitic weeds. CIMMYT, Nairobi, Kenya. Sauerborn, J., B. Kranz, and H. Mercer Quarshie Organic amendments mitigate heterotrophic weed infestation in savannah agriculture. Applied Soil Ecology 23: P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

86 Showemimo, F.A., C.A. Kimbeng, and S.O. Alabi Genotypic response of sorghum cultivars to nitrogen fertilization in the control of Striga hermonthica. Crop Protection 21: Terry, P.J., (ed.) A review of major weed control problems in East and Central Africa with particular emphasis in Striga and intercropping practices, pp Comminwealth Service Council, London. Tesso, T An Integrated Striga Management Option Offers Effective Control of Striga in Ethiopia, In J. Gressel and G. Ejeta, eds. Integrating new technologies for Striga control: Towards ending the witch hunt, Addis Ababa. Vanlauwe, B., F. Kanampiu, G. Odhiambo, H. De Groote, L.J. Wadhams, and Z.R. Khan Integrated management of Striga hermonthica, stemborers, and declining soil fertility in western Kenya. Field Crops Research 107: Wangare, L., H. De Groote, and F. Kanampiu Economic Analysis of Herbicide Coated Maize to Combat Striga. Poster paper presented at the 25th International Conference of the International Association of Agricultural Economists, August, Durban, South Africa Wanyama, J.W., Z.R. Khan, D.M. Amudavi, E.M. Njuguna, C.A.O. Midega, N. Dibogo, P. Akello, and J.A. Pickett Farmers Perceptions and Adoption of a Push Pull Technology for Control of Cereal Stemborers and Striga Weed in Western Kenya. Paper prepared for presentation at the 23rd Conference of Association of International Agricultural and Extension Education (AIAEE) in Polson, Montana, USA, May Weber, G., K. Elemo, S.T.O. Lagoke, A. Awad, and S. Oikeh Population dynamics and determinants of Striga hermonthica on maize and sorghum in savanna farming systems. Crop Protection 14: Zeyaur R. Khan, Z.R., C.A.O. Midega, A. Hassanali, J.A. Pickett, and L.J. Wadhams Assessment of Diff erent Legumes for the Control of Striga hermonthica in Maize and Sorghum. Crop Science 47: P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

87 ANNEX 1 STRIGA BASELINE CONDITIONS DATA Field surveys were conducted to determine baseline Striga information, i.e. Striga infestation levels and Striga damage, in Kenya, Uganda, and Tanzania. The surveys were a rapid appraisal of existing Striga conditions, but also elicited information on cropping systems, livestock holdings, household demographics, and production statistics. A copy of the questionnaire used in the surveys is included in the Annex. Extension agents were asked to rate the extent and intensity of Striga infestation in their district based on field experience and expert opinion. To provide consistency, Striga intensity was defined in five categories ranging from None to Severe. Each category was assigned a specific range of Striga plant density, which was explained to the respondents prior to the survey. For instance, Minor infestation was defined as having between 1 and 4 Striga plants per m 2, Medium had between 4 and 9 plants per m 2, and Severe infestation had more than 9 Striga plants per m 2. The respondents were also asked to estimate the yield damage caused by Striga in each category. Table A1.1 Extent of Striga infestation in the East Africa study region Extent of Infestation a Country Very Low Low Medium High Very High Total Kenya 51,397 53,927 42,959 26,807 41, ,998 Tanzania 3,920 79, , ,102 73,937 1,085,835 Uganda 5,598 31,563 56,155 20, , ,709 Total 60, , , , ,892 1,564,542 Table A1.2 Intensity of Striga infestation in the East Africa Study region on maize planted area Intensity of Infestation a Country Minor Medium High Severe Total Kenya 28, ,111 60,542 8, , P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

88 Tanzania 3,920 79, , ,268 Uganda 5,598 31,563 76, , ,709 Total 37, , , ,220 2,254,532 Table A1.3 Maize production losses in the Kenya Study region Intensity of Infestation a Country Minor Medium High Severe Total Kenya 27, ,704 47,831 3, ,812 Tanzania 6,181 52, , ,451 Uganda 33, , , ,492 Total 67, ,521 1,158, ,428 1,919,755 Table A1.4 Economic losses from Striga infestation the Kenya Study region Intensity of Infestation a Country Minor Medium High Severe Total Kenya 5,577,000 30,740,800 9,566, ,400 46,568,400 Tanzania 1,112,685 9,392, ,556, ,061,488 Uganda 5,344, ,383 33,190,987 77,441, ,079,074 Total 12,034,389 40,235, ,313,774 78,125, ,708,962 Table A1.5 Maize production gains from introducing Striga control technologies in the Kenya Study region Intensity of Infestation a Country Minor Medium High Severe Total 87 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

89 Kenya 95, , ,749 23,765 1,173,524 Tanzania 8,194 69,167 1,197, ,274,483 Uganda 30, , , ,943 Total 134, ,947 1,706, ,370 3,100,950 Table A1.6 Economic gains from introducing Striga control technologies in the Kenya Study region Intensity of Infestation a Country Minor Medium High Severe Total Kenya 19,161, ,241,000 64,549,800 4,753, ,704,800 Tanzania 1,474,955 12,450, ,481, ,407,090 Uganda 4,810,240 92,144 29,871,904 69,696, ,471,184 Total 25,446, ,783, ,903,691 69,696, ,583,074 ANNEX 2 STRATEGIES AND INTERVENTIONS Several Striga control strategies and interventions have been developed. Some strategies are highly effective in controlling Striga, but typically their costs are prohibitively high for uptake. More affordable options are often only marginally effective in controlling Striga and also suffer from limited uptake potential. Hence, despite the variety of options, no single alternative has been found to be universally adopted by producers. Strategies and interventions have unique characteristics, with varying strengths and weaknesses. Moreover, technical and economic performance are influenced by numerous factors, most of which vary spatially. Features of the Striga control strategies are summarized in Table A2.1. Direct strategies address Striga control by reducing the number of Striga plants in the field. These include chemical control methods and weeding. Indirect strategies attack the parasite by altering growing conditions, creating unfavorable conditions for Striga development. 88 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

90 Table A2.1 Description of Striga control strategies and interventions Strategy Option Advantages Drawbacks Primary Investments Benefits a ($/ha) Invest. Costs ($/ha) B/C Manual Weeding Simple operation Labor intensive Labor Fallow Ease of implementation Time required to accrue benefits Spare land Crop rotation Agronomic cobenefits Rotation crop often uneconomical Trap crop, spare land Intercropping Agronomic cobenefits Trap crops often uneconomical Trap crop Push Pull Agronomic cobenefits, stemborer control Trap crops often uneconomical, extensive training required, Time required to accrue benefits Trap crops Soil Fertility Enhancement Genetic Resistance Immediate and highly effective control Ease of implementation High cost of fertilizer to smallholder Requires seed purchase every Fertilizer Crop seed 89 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

91 season Chemical Control Ease of implementation Control occurs too late, cost of herbicide Herbicides Seeddressing Ease of implementation Requires seed purchase every season Crop seed a Benefits and investments costs are computed using Net Present Value since the benefit and cost streams can accrue over a multi year horizon. b Benefits and costs were calculated based on average conditions in East Africa, based on an average farm household in the region. Clearly, in other locations such benefits and costs could be substantially different; a more complete treatment of how benefits and costs vary spatially is presented in the Profitability section. Traditional Practices: Manual Weeding and Fallow Manual weeding of Striga plants is a simple operation requiring only basic equipment, but it is inefficient in reducing Striga damage. Weeding occurs post emergence, after Striga has inflicted its damage on the host crop. Hence, the effect of weeding is on the long term reduction of the Striga seed bank. Since producers do not realize immediate benefits from weeding, and given its labor intensive nature, manual weeding has limited potential as a strategy to eliminate Striga problems. Because of its ease of operation, manual weeding is the most commonly used approach to eliminate Striga. Trap Crops: Crop Rotation, Intercropping, and Push Pull Some of the most promising Striga control methods are considered to be trap crops (i.e. push pull) and herbicide resistant approaches. Push pull control and trap crops rely on rotation crops to assist in eliminating Striga seed density levels. Striga seedlings must attach to a host root within a few days after germination else they will die (Worsham, 1987). Researchers have introduced the use of trap crops, particularly legumes, which have novel properties. Certain legumes, i.e. desmodium, secrete root chemicals that act in contradictory ways, and masquerade the plant as a false host for Striga. Some compounds stimulate the germination of Striga seeds while others inhibit their growth (suicidal germination), making it difficult for such legumes to act as a Striga host. The trap crops have been introduced in rotation with cereals (Berner et al., 1996) or in 90 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

92 suitable intercropping arrangements (Khan et al., 2002; Oswald et al., 2002, Khan and Pickett, 2004). Various studies report on the significant reduction in Striga plants that achieved when intercropping cereals with legumes (Saunders, 1933; Carson, 1989; Webb et al., 1993; Babiker and Hamdoun, 1994; Carsky et al., 1994; Khan et al., 2000, 2001, 2002; Tenebe and Kamara, 2002; Khan et al., 2006a, 2006b). In particular, the use of fodder legumes as trap crops has been shown to reduce Striga seed bank population over the course of just a few years, a highly desired outcome. The use of legumes in a crop rotation system generates co benefits for the producer. Livestock producers benefit from legume production since legumes such as desmodium are a sought after, nutritious fodder crop. Leguminous crops also enhance soil fertility through introducing additional nitrogen in soil. Researchers continue to search for food legumes that could also act as an inhibitor to Striga. Trap crops can also be easily adapted into a push pull system that provides additional benefits in controlling Lepidopteron stemborers, another economically important pest in the region. Push pull Striga Control Numerous field trials have been conducted over the past several years documenting the efficacy of the push pull technology (PPT) and the use of trap crops in controlling Striga populations. Initial field trials were begun by ICIPE in 2001 at the Thomas Odhiambo Campus in Mbita Point, Kenya, a peninsula in Lake Victoria. The four years of assays tested the performance of four species of desmodium, and cowpea, in controlling Striga and maize stemborer. ICIPE conducted experiment station trials for four seasons ( ) in Western Kenya at Mbita Point. Maize and sorghum were intercropped with several legumes, including food crops, to assess their performance as trap crops. The results found that greenleaf desmodium performed the best, providing superior control over Striga, the tallest plants, and generated the highest grain yields. The food crops, cowpeas, beans, and groundnuts, performed significantly better than the control (i.e. monocrop maize and sorghum) in some seasons, but not across the length of the experiment. On farm testing of PPT control over Striga began in 1998 in two districts in Western Kenya, Trans Nzoia, and Suba. The studies also included the effects of PPT in controlling maize stemborer populations. The field trials tested three alternative PPT configurations, including the use of desmodium as a push crop. The initial on farm trials were broadened beginning in Between 2001 and 2004, on farm testing was 91 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

93 extended to include four additional districts in Western Kenya. Each of the six districts in these field trials had 10 farmers, randomly selected from local farmers meetings (baraza). Farmers agreed to plant three plots: PPT, a maize bean intercrop, and maize monoculture. In addition to monitoring crop yields, surveys were conducted to estimate the incremental labor costs associated with PPT and the maize bean intercrop (trap crop). Across all districts, PPT performed significantly better than either the maize bean intercrop or the maize monocrop, in terms of both yields and economic returns. On farm evaluation of PPT began in 2003 across 14 districts in Western Kenya. A total of 20 farmers were selected in each district, chosen at random from the early adopters of PPT in the region. Seems to be an evaluation of 1,500 farmers in the region as well. The results of the PPT and trap crop field trials are summarized in Table_PPT. Table_PPT District AEZ Treatment Yield PPT Yield Increase (%) Potential Maize Area Affected Trans Nzoia LH3 PPT Maize Mono 3.9 Suba UM1 PPT Maize Mono 1.3 Bungoma UM1 UM3 PPT Maize Mono 3.1 Kisii UM1,LH1 2 PPT Maize Mono Busia LM1 LM4 PPT Maize Mono P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

94 Vihigia LM1 UM1 PPT Source: Khan et al Maize Mono Crop Intensification: Soil Fertility Enhancement and herbicides Soil fertility has been found to be significantly linked to Striga infestation. In nutrient poor soils, Striga typically flourishes. Conversely, where soils are managed with adequate quantities of nutrients, Striga infestations are seldom encountered. While the exact mechanism(s) by which soil nutrients affect Striga is not completely understood, studies have suggested that soil nutrients: Reduce the effectiveness of the host plant in stimulating Striga germination Enable the host plant to develop more quickly before Striga can act as a parasite Act as a toxin to Striga Are not able to be absorbed directly by Striga, which prefers to obtain nutrients as amino acids from the host plant. Cost is the primary constraint to the use of fertilizers in combating Striga. Relative to the price of the host crop, fertilizer remains too expensive for producers to adopt. This is unfortunate since fertilizers are effective in controlling Striga. Research suggests that fertilizer application rates of 150 lb of nitrogen per acre are required under normal agronomic conditions. These are Striga Resistant Varieties Host plants have been observed to display resistance to Striga infection (Johnson et al., 2000; Harahap et al., 1993; Ramaiah, 1991; Williams, 1959). This includes reduced host plant exudates that suppress Striga germination, and post germination barriers that prevent Striga from attaching to the host plant. Resistance (and tolerance) has varied, however, with performance significantly affected by climatic, agronomic, and other conditions. Seed Coating: IR Maize There is considerable empirical evidence supporting the efficacy of IR maize in reducing Striga infestation and in the process generating higher crop yields. In an initial study, IR maize was field tested by CIMMYT at various locations in East and Central Africa, including Kenya, Tanzania, Malawi, and Tanzania (Kanampiu et al., 2003). This included trials at three experiment stations on on farm trials at 96 sites between 1998 and P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

95 Where applicable, the field trials were conducted in both the short and long rainy seasons (Kenya). The field trials were conducted on commercial varieties of maize bred for IR resistance. Across all four countries, the results found that IR provided significant control over Striga plant density and significantly increased maize yields. Further yield increases are expected once the IR resistance is introduced into local varieties, rather than the commercial varieties used in the experiments. More recent CIMMYT field tests of IR maize have been conducted in Kenya (DeGroote et al., 2007). In 2002, both experiment station and on farm trials were conducted, although both sets of plots were researcher managed. The station trials were conducted at the Kibos experiment station and the on farm trials were located in Kisumu. The results of the 2002 IR maize trials are summarized in Table_IR. IR maize was found to reduce Striga counts significantly better than the control plots containing unprotected plants. In 2004, on farm trials of IR maize were conducted in three districts in Kenya, Bondo, Vihigia, and Rachuonyo. All of the field trials were managed by farmer managed. The most recent field testing data on IR maize is available from AATF, which has conducted tests over the past two years. The AATF tests are also the most comprehensive, encompassing over 600 producers in Western Kenya. Chemical Control Striga is effectively controlled by commercially available herbicides, including glyphosphates. However, two factors limit their use. One is that chemical control occurs post emergence, after Striga has inflicted its damage on the host crop. This prevents herbicides from reducing damage during its season of application, although it will act to reduce the Striga seed bank over the long term. 94 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

96 ANNEX 3 PROFITABILITY ANALYSIS An impact assessment model was developed to project the benefits from introducing Striga control measures in East Africa. The primary focus is on the push pull (i.e. trap crop) and IR maize technologies since they hold the highest potential for large scale introduction in the East Africa region. Baseline Striga conditions were established using data from several sources. These sources included recently conducted field surveys, expert opinion, and previous studies that documented Striga conditions in the region. This generated information on Striga infestation levels throughout East Africa and their corresponding damage on maize and sorghum yields. Using GIS, the Striga infestation was georeferenced and mapped. The mapping of Striga infestation required both the extent and intensity of Striga, which are included in our baseline analysis. The maps then provide a clear description of where, how much, and how many Striga plants are located in particular regions throughout the region. The benefits from introducing Striga control measures are calculated based on primary data collected by field crop scientists. Experiments, at both research stations and onfarm trials, have documented the efficacy of Striga control measures such as push pull and IR maize technologies. The push pull control measure has been tested at several locations in Western Kenya, including an extensive study using on farm trials. IR maize has been tested at experiment stations in Kenya, including some on farm trials. Statistical models were developed to estimate the control efficacy of each control measure, i.e. push pull and IR maize technologies. In the process, a Striga damage model was estimated that relates Striga infestation levels to crop yields. This yield damage model was used to estimate crop production losses due to Striga. Because the baseline Striga and field trial data were collected at a limited number of locations, it was necessary to extrapolate the results. The extrapolation was performed using a spatially explicit framework that was constructed based on the principle of geographic equivalence. Regions with similar soils, topography, and climate are expected to perform in a similar manner to one another. Statistical techniques were used to identify regions with geographic similarity using a GIS platform, which included intersecting data layers of soils, topography, and climate to obtain the geographically similar units. The geographic equivalence procedure was conducted at the sub district level. The variables used in predicting the benefits of Striga control measures, e.g. Striga density, crop yields, and control efficacy, were extrapolated using the spatially explicit framework. 95 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

97 The economics of Striga control were incorporated in the analysis to replicate the adoption decision undertaken by the producer. This module acts as a filter, removing from consideration unprofitable and/or undesirable control measures from the analysis. In the model, the adoption of Striga control measures is based on profits. If one of the Striga control measures increases profits over baseline conditions, then adoption occurs. The profit function includes both the direct benefits from Striga control, i.e. higher crop yields, as well as co benefits. For instance, the push pull strategy produces animal forage such as desmodium, which has a significant economic value in areas where livestock production occurs. The incremental costs associated with adopting push pull technology and IR maize are also included in the analysis. Benefits were aggregated across the study region by multiplying the gains at the unit level in each AAZ (i.e. per hectare) by the corresponding production area in the AAZ. This procedure provided national level impacts of the Striga control technologies, including impacts across all three countries in the East Africa study region. The economic benefits were delineated across social strata to investigate how Striga control would impact poverty measures. The increased food production and farm income were fed into poverty measures to assess their effect on improving rural livelihoods. 96 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

98 ANNEX 4 THE WAY FORWARD Various actors are required to facilitate the successful introduction of Striga control technology. This will be challenging since existing linkages among the actors are either weak or non existant. Existing value chains are weak, particularly in rural areas. Establishing a Striga control program requires the strengthening of existing linkages along the agricultural value as well as creating new markets. Actor Role Contributions/Benefits Producers Local Farm Associations International Research Institutions National Agricultural Research Organizations Agribusiness Sector Primary implementer of Striga control measures. Generate continued innovations in Striga control Provide on going solutions to constraints using advanced scientific methods Mapping of Striga infestation Incorporate IR maize technology in local varieties as technically feasible Maintain variety lines. Establish recommended practices for technology practices Establish testing to validate new technologies Assist farmers in developing ways to adapt and fine tune Striga control technology Seed replication Fertilizer Land and labor New Striga control technologies New and adapted technologies New marketing opportunities 97 P age

99 Donors Distribution and marketing of production Value added for livestock fodder Micro credit Increase priority for Striga control programs Financial support Actor Role Contributions/Benefits Extension Agencies Conduct farmer workshops to disseminate information on Striga control measures Provide training and establish recommeneded pracitces Provide technical support Monitor and document adoption process. Increase likelihood of uptake. 98 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo,

100 Information & Institutions Extension Services, Farmers Associations, Cooperatives Micro Credit Information on Extension & Monitoring & Striga Control Training Evaluation Investments $12 million in maize seeds Seed Multipli cation Seed Quality Control Input Channels Producers Processing/ Manufacturing Retail/ Consumers Seed Certification Cultural Practices Price Incentives ICIPE CIMMYT AATF NAT L RESEARCH Policy & Change Governments, NGO s, & Donors 0 Page

101 ANNEX 5 STAKEHOLDERS AATF ICIPE CIMMYT OKLAHOMA STATE UNIVERSITY Stakeholders UGANDA: REGISTERED SEED COMPANIES East African seeds, 3. Naseco, 4. FICA seeds, 5. Monsanto, General and Allied Ltd, 6. Mt. Elgon Seed, 7. AK Oils and Fats U Ltd, 8. Otis Garden Seeds, 9. Victoria Seeds. 10. Akuku farm seeds; 11. Grow more seeds and Chemicals Ltd, 12. Safari Seeds Ltd, 13. El Shaddahai International, 14. Amla seed enterprises. SEED INSPECTION AND TESTING: 15. Chemiphar Laboratories Ltd which deal in seed inspection and testing, PROVISION OF APPROPRIATE SEED TECHNOLOGIES AND FARMER TRAINING 16. AT Uganda DISTRIBUTION NETWORK OF FIELD CROPS INPUTS AND VEGETABLE SEEDS 17. UNADA 105 P age

102 KENYA: Currently, the country has about 8 major seed production and marketing companies supplying seeds to the domestic and regional export market. The companies produce maize, wheat, sorghum, millet seeds etc. Company Types of Seeds Handling capacity (tons) 1 East African Seeds Co. Ltd Maize 390,000 2 Farm Chem Ltd Maize 257,000 3 Kenya Seed Company Maize, Wheat, Sorghum, Millet 10,178,000 4 KARI Seed Unit Maize, Wheat, Millet 66,300 5 Monsanto (K) Ltd. Maize 200,000 6 Pannar Seed (K) Ltd Maize 28,000 7 Western Seed Co. Maize, Sorghum 874,400 8 Lagrotech Ltd Sorghum, Maize 26,000 Source: Ministry of Agriculture, P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo, J. Vitale, and J. Sanders Consultants for Kilimo Trust. April, 2009.

103 ANNEX 6: PRODUCTION LOSSES AND GAIN STATISTICS FOR THE COUNTRIES BY ADMINISTRATIVE UNITS DISTRICT TOTAL STRIGA AREA (ha) ANNUAL MAIZE PRODUCTION LOSSES (tonnes) ANNUAL SORGHUM PRODUCTION LOSSES (tonnes) ANNUAL MILLET PRODUCTION LOSSES (tonnes) Kenya ANNUAL MAIZE PRODUCTION GAIN (tonnes) ANNUAL SORGHUM PRODUCTION GAIN (tonnes) ANNUAL MILLET PRODUCTION GAIN (tonnes) ANNUAL ECONOMIC GAINS (US$) ANNUAL ECONOMIC LOSSES (US$) BONDO 8,768 1, ,744 3, ,746, ,340 BUNGOMA 7,264 19, , ,600,560 4,092,720 BUSIA 47,237 8,523 1, ,439 11,365 1,623 15,556,900 2,230,900 BUTERE MUMIAS 17,755 3, ,425 1, ,125, ,520 GUCHA 60 6, , ,221,580 1,296,840 HOMA BAY 44,955 12, ,687 28,137,400 2,519,600 KAKAMEGA 1,975 7, , ,151,900 1,593,060 KISII CENTRAL 1,956 13, , ,660,700 2,823,600 KISII NORTH 642 5, , ,196,300 1,269,800 KISUMU 8,389 16,972 7, ,899 8, ,277,060 5,636,760 KURIA 3,335 4, , ,217, ,300 LUGARI 14 3, , ,114, ,220 MIGORI 16,195 8,374 1, ,674 18, ,187,800 2,136,800 MT. ELGON 262 2, , ,389, ,080 NYANDO 76,000 8,406 5,196 9,360 5,786 3,492,080 3,136,080 RACHUONYO 69,840 17,798 5, ,826 30, ,223,460 5,034,380 SIAYA 20,000 31,990 4, ,621 4, ,405,080 7,548,020 SUBA 4,000 3, , , ,500 TESO 4,647 1, ,068 3,628 1,240 3,201, ,460 VIHIGA 7,684 5, ,311 1, ,915,780 1,155, P age

104 Tanzania REGION TOTAL STRIGA AREA (ha) Arusha Babati ANNUAL MAIZE PRODUCTION LOSSES (tonnes) ANNUAL SORGHUM PRODUCTION LOSSES (tonnes) ANNUAL RICE PRODUCTION LOSSES (tonnes) ANNUAL MAIZE PRODUCTION GAIN (tonnes) ANNUAL SORGHUM PRODUCTION GAIN (tonnes) ANNUAL RICE PRODUCTION GAIN (tonnes) ANNUAL ECONOMIC GAINS (US$) ANNUAL ECONOMIC LOSSES (US$) DSM 4, ,897 3,003 8,679 7,543,800 2,516,000 Dodoma 58,827 47,673 59, , ,416 1,913 60,519,480 27,060,920 Iringa 17,999 21,455 3,500 6,641 61,719 8,464 16,060 27,561,720 10,583,800 Kagera 11,971 1, ,710 1,229 1,486, ,400 Kigoma 34,313 12, ,596 2,981 2,981 14,338,680 3,082,200 Kilimanja Lindi 52,720 20,067 14,237 7,537 49,026 30,645 16,589 31,657,000 14,029,360 Manyara Mara 21,975 13,310 9, ,470 18, ,163,720 5,482,640 Mbeya 43,568 18,824 4, ,944 12, ,250,280 5,026,080 Morogoro 59,337 60,123 11,369 48, ,787 29, , ,152,120 53,652,720 Mtwara 22,717 16,217 6,032 5,326 39,786 11,786 10,106 19,342,080 9,193,160 Mwanza 80,086 13,834 4,515 28,632 65,322 19, , ,452,040 26,936,600 Pwani 23,775 17,521 1,658 30,816 47,830 4,182 76,288 71,767,360 28,621,240 Rukwa 24,522 14, ,159 70,972 4,377 76,928 76,962,360 14,384,680 Ruvuma 138,269 75, , , ,163 76,756,600 37,370,960 Shinyanga 139,938 64,850 44,159 37, , , , ,107,640 55,632,120 Singida 50,906 4,596 14, ,603 52, ,036,160 5,154,040 Tabora 116,121 12,989 6,587 15,239 76,677 32,202 94,161 99,680,760 16,633,360 Tanga 48,529 37, ,146 95, ,076 23,958,120 9,289,400 Tarime 13,800 10,113 11,429 3,691 27,481 30,290 9,421 21,514,200 8,175, P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo, J. Vitale, and J. Sanders Consultants for Kilimo Trust. April, 2009.

105 Uganda DISTRICT TOTAL STRIGA AREA (ha) ANNUAL MAIZE PRODUCTION ANNUAL SORGHUM PRODUCTION ANNUAL RICE PRODUCTION LOSSES (tonnes) ANNUAL MAIZE PRODUCTION GAIN (tonnes) ANNUAL SORGHUM PRODUCTION ANNUAL RICE PRODUCTION GAIN (tonnes) ANNUAL ECONOMIC GAINS (US$) ANNUAL ECONOMIC LOSSES (US$) Adjumani 2, , ,150, ,400 Apac 1, , ,509, ,680 Bugiri 5,661 4, , ,352 15,293,680 1,333,680 Bushenyi , , ,760 Busia 2,963 3, , ,007 10,861,000 1,036,000 Gulu , ,000,960 85,200 Iganga 8,719 8, ,070 26,310 1,425 3,220 32,860,320 2,734,440 Jinja 1,831 1, , ,170, ,640 Kabarole ,337,960 59,720 Kaberamaido 3, ,720 12,600 Kamuli 9,411 10, ,233 27,797 1,508 3,403 16,365,240 3,155,760 Kapchorwa ,840 71,760 Kasese 1,349 1, , ,556, ,400 Katakwi 16,996 Kotido 5,098 2, ,260 1, ,202, ,120 Kumi 9,988 10, ,322 26,699 1,447 3,269 28,566,400 3,385,040 Lira 2, ,921,080 Mayuge 1, , ,646, ,640 Mbale , ,408, ,200 Moroto , ,320 98,160 Nakapiripirit , , ,800 Pader 3,218 1, , ,923, ,280 Pallisa 7,844 7, ,172 1,258 2,835 28,298,440 2,376,800 Sironko ,360 57,200 Soroti 11,640 12, ,488 39,433 2,137 4,827 17,997,080 3,805,560 Tororo 7,176 6, , ,119 64,398,120 1,960,800 Yumbe 1, , ,621, , P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo, J. Vitale, and J. Sanders Consultants for Kilimo Trust. April, 2009.

106 ANNEX 7: SOURCES OF STRIGA SURVEY INFORMATION Kenya : Sources of Striga Information 110 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo, J. Vitale, and J. Sanders Consultants for Kilimo Trust. April, 2009.

107 111 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo, J. Vitale, and J. Sanders Consultants for Kilimo Trust. April, 2009.

108 Uganda : Sources of Striga Information 112 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo, J. Vitale, and J. Sanders Consultants for Kilimo Trust. April, 2009.

109 113 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo, J. Vitale, and J. Sanders Consultants for Kilimo Trust. April, 2009.

110 Tanzania: Sources of Striga Information 114 P age

111 Source: (Mbwaga & Obilana, 1993). 115 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo, J. Vitale, and J. Sanders Consultants for Kilimo Trust. April, 2009.

112 ANNEX 8: STRIGA QUESTIONNAIRE Transect survey Methodology Developed by: Hugo De Groote, Ignath Rwiza, Adventina Babu - Objectives of the survey o General: estimate maize and sorghum areas infested with Striga, and estimate intensities o Practice: map with points, with points, systematically spread over the zone, with striga infestation levels on a limited scale (see Kenya Map example overleaf) - Methodology o transects are traveled, striga observed, and farmers/offocers interviewed, using systematic geographic sampling o Practice: 5 or so transects, following major roads, are traveled, aiming for the steepest gradient, up to the altitude where no more striga is observed. o Observations at each point: Point itself, and walk further till first point with cereal crop is reached Striga score: 0=none, 1=little, 2=medium, 3: heavy Crop: 1=maize, 2=sorghum, 3=millet, 4=other o Selection of points Transects: major roads focusing on known areas of infestation Each 10Km, depending on how long this takes, stop and observe striga infestation levels Observe striga score If not a cereal plot, walk further till cereal plot is reached, Each 30 km: farmer closest to the point is visited and interviewed about striga intensity levels, and yield loss levels due to striga Report from officer/farmer interview o Striga on your farm: Do you have striga in your fields? How much, o Striga in the area o Area and crops affected and crop loss levels o Resources GPS, compass, questionnaires, Map of the country: hard copy map guide electronic: roads, altitude, population density Time: Each transect is maybe km, - Note: the full interview is better done on a random sample, since road side interviews tend to be biased, given the better road and market access. Ideally, farm households should be interviewed, stratified over agro ec ological zones 116 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo, J. Vitale, and J. Sanders Consultants for Kilimo Trust. April, 2009.

113 , 117 P age EX ANTE ASSESSMENT OF STRIGA CONTROL IN EAST AFRICA L. MacOpiyo, J. Vitale, and J. Sanders Consultants for Kilimo Trust. April, 2009.