were observed in the water use efficiencies of classes of feeds but also among the same feeds when sourced from different districts.

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2 FEED SUPPLY - DEMAND DATABASES AS DECISION MAKING TOOLS FOR PRIORITIZING LIVESTOCK INTERVENTIONS TO CLOSE YIELD GAPS AND REDUCE NEGATIVE ENVIRONMENTAL FOOT PRINTS Michael Blummel 1 *, Am are Haileslassie 2, Anand an Samireddypalle 3, Mario Herrero 4, Y Ramana Reddy 5 and Dianne Mayberry 4 * - m.blummel@cgiar.org Abstract: Feed data bases describing feed supply - demand scenarios are important tools for researchers, development practitioners and private sector for example to gauge opportunities and limitations fo~ increasing livestock production and to obtain information about potential feed surplus and deficit areas. The two pillars of such feed data bases are assessment of quantitative and qualitative feed availability to calculate present feed supply and livestock census data (livestock population, species composition, herd structure, productivity levels) to estimate feed demand. The present paper proposes, and demonstrates, that such feed supply-demand data bases can be further developed into decision making tools to prioritize and compare various interventions for increasing livestock production and productivity. For example feed-based interventions can be compared with herd-based interventions around animal species, breed and reproduction and the possible interdependence of interventions can be explored and modeled. In addition the implications of choices of interventions on environmental foot prints particularly water requirements and greenhouse gas emissions can ultimately be estimated by such tools. The paper presents example of how feed supply can be linked to water requirements based on the variables: 1) reference evapo-transpiration (ETO) calculated from temperature, wind speed, humidity and rainfall, 2) crop specific coefficient derived from crop phenology (Kc); and 3) length of growing period (LGP). Huge differences I International Livestock Research Institute (ILRI) PO Box 5689, Addis Ababa, Ethiopia 2 International Water Management Institute (IWMI), PO Box 5689, Addis Ababa, Ethiopia 3 International Livestock Research Institute (ILRI), c/o IITA, Ibadan, Nigeria 4 Commonwealth Scientific and Industrial Research Organization, St Lucia, Australia 5 International Livestock Research Institute (ILRI), c/o ICRISAT, Patancheru , India corresponding author ( m.blummel@cgiar.org) ICAR-NDRI, Karnal I February 6-8, 2016

3 were observed in the water use efficiencies of classes of feeds but also among the same feeds when sourced from different districts. Key words: Feed data base, Decision making tool, Livestock interventions, Yield gaps, Environmental foot print Background Feed resourcing and feeding are key issues in livestock production because feed costs largely determine the economic benefits from the production of Animal Soured Food (ASP). Feed costs relative to ASP price are increasing and checking and reversing this trend will be paramount for the survival of livestock producers (IFIF, 2012). In addition, teed resourcing and feeding is at the very interface where positive and negative effects from livestock are negotiated and urgent attention needs to be given for example to water reguirement for feed production (Bliimmel et al., 2014). Singh et al., (2004) has drawn attentiort ta the Omtt'l:n:m..._.,_,l t'i"inclv high water needs for extensive dairy production in India, calculating that in the state ot Gujarat, the heaman~ Vi~ '... hite revolution' (widespread increase in milk production), an average of 3.4 m 3 of water are required for the production of 1 kg of milk. The global average is 0.9 m 3 and the authors' traced high water needs to feed resourcing and production, concluding, based on life cycle analysis, that on average litre of water were required to produce the daily feed for one single dairy animal. Conventionally the relationship between livestock and water is associated with drinking water requirements and the fact that much more water is required for evapo-transpiration in feed production still escapes the awareness of many livestock nutritionists. They rarely include water requirement assessments in feed resourcing and ration design. Finally while many small holder will not be able, or prepared, to invest in intensifying ASP production, they will still have livelihood options around feed and fodder production such as forages as cash crops, fodder marketing and small scale feed enterprises. In short, ample reason exist why feed production, feed resourcing and feeding as the nexus largely determining beneficial and adverse effect of livestock should be given special attention in the realm of livestock research and extension as well as natural resource management. Feed supply - demand scenario as starting point However, well-informed decision making in the area of feed production, feed resourcing and feeding requires a good understanding of actual feed supply - demand scenarios. While this appears to be common sense, few structured feed - demand supply scenarios actually exist (FAO, 2012). Through coordinated central government and state efforts, India has systematically quantified feed and fodder resources, building up a data base from district level (NIANP, 2003). Feed sources were classified into greens (subclasses: ICAR-NDRI, Karnal I February 6-8, 2016

4 cultivated fodder, grass from grazing, forests, fallows etc.), crop residues (subclasses: coarse and fine cereal residues, leguminous residues) and concentrates (subclasses: grains, cakes, bran, chunnies). Subclasses from crop residues (CR) are further differentiated to list the contributions from specific crops (NIANP, 2003). Livestock population (from livestock census) and productivity data were used to calculate feed demand factorially that is for maintenance and production. It is important to realize that the data base was constructed essentially from available secondary data sets. For 2003 it was calculated (Bliimmel et al., 2014) that on a dry matter basis, crop residues were the single most important fodder resource contributing about 71% to the overall feed resources.. Green fodder from all sources contributed about 23% and the area under planted forage was rather stagnant during the last two decades, increasing merely from tons in 1986 to tons in Fodder from common property resources, forests, pastures and fallow lands constituted less than 16% of the available fodder. Also notable was that concentrates represented a very low proportion ( < 7%) of the available feed resources, and there was no indication of any rapid increase in their use. The countrywide gap between feed demand and supply was minor for feed quantity (dry matter) but was large with regards to feed quality, since the estimated annual feed dry matter deficit was only 6% while digestible crude protein and total nutrients were estimated to fall short by 61 and 50%, respectively (NIANP 2003;Bliimmel at al., 2014). The various components of the NIANP ( 2003) feed data base are presented in Figure 1. Feed supply is calculated from cropping and land use pattern and various conversion factors such as harvest indices, milling and oil extraction efficiencies and biomass yield estimates for rangelands, common property resources (CRP) and so on. Feed demand is derived from livestock census and productivity assumptions about weight gains, milk production etc. and by using standard animal nutritional relationships. Crop data Ptoductioa or ~ruk, pulhs, oibeeds &otlters Land use Area under CPR. forests, pastures, fallows ok., Livestock census DD Figure 1: Structure, components and functionality of the NIANP Feed Data Base (Adopted from Bliimmel et al., 2014) ICAR-NORI, Karnal I February

5 The information which are presented in an aggregated form in Figure 1 are actually quite detailed when disaggregated as evident from Table 1 where the level of detail available on feed supply is high, extending in essence from the major feed and fodder categories such as crop residues, concentrates and greens down to individual crop level. Feed availability would be in tons aggregated to district level. Similarly even (on a national scale) minor animal species such as Mithun, Yak etc. are registered. More importantly, classification according to sex, age, purpose and productive status, in other words herd structures, are detailed (Table 1). Feed requirements are calculated factorially from maintenance requirements and production and are expressed on dry matter basis (where intake is estimated by constants based on body weight), digestible crude protein (DCP) and total digestible nutrients (TDN). The feed data base as it is conveys a range of information to the user. On the balance side i.e. does feed supply meet feed demand, the user can see if available feed biomass meets intake demand, and if DCP and TDN meet nutrient demand, or not. Degree of surplus or deficit in any of these three variables would be listed. Policymaker, development agent and private sector can use the information to decide for example on investments for and location of livestock value chains and opportunities for spatial feed transactions from surplus to deficit areas and so on. In addition, decision can be made about on specific feed interventions. For example knowledge of the proportional contributions of major feed categories (crop residues, concentrates and greens) to overall feed resources will help identifying strategic entry points for feed improvement. For example if crop residues provide major feed resources, investment in their upgrading by plant breeding and physical-chemical-biological treatment seems sensible. If maize is the major crop focusing on maize stover will be advisable. Along similar lines if feed biomass quantity is below voluntary feed intake level, investment in increasing feed quantity will be beneficial. In contrast, where feed biomass availability is adequate but DCP and TDN are lacking, investment in improving feed quality will have priority. This can be taken further and where for example TDN is sufficient but DCP is lacking, investment in protein sources will have priority (and of course vice versa). Obviously a critical interface effecting all these conclusion and ultimately decisions is voluntary feed intake (VFI). The data base as it is uses constants based on live weights to calculate estimate VFI, which in the final analysis is probably too much of an oversimplification, even when allowing for the need for simplicity in feed resource - supply demand scenarios. We therefore suggest use VFI predictions based on feed and fodder quality traits. As a further modification we suggest to use metabolizable energy (ME) instead of TDN since we think that ME expresses the impact of energy related feed quality more clearly than does TDN and provides a better interface for matching feed quality and animal requirement. ICAR- NDRI, Karnal I February 6-8, 2 016

6 Table 1: Main and sub categories of feed stuffs and animal species and herd structures used in the feed data base ofnianp (2003). FEED COMPONENT ANIMAL COMPONENT Main Category Sub Category Crop Species Herd Structure + Cop residues +Fine straw +Buffalo + Concentrate + Coarse straw Sorghum +Cattle CB Male:< 1 y +Greens + Legume straw Pearl millet +Sheep CB Male: 1-3 y Maize +Goat CB Male: Breeding Oats +Yak CB Male: Agriculture Ragi + Mithun CB Male: Bullock Small millet +Horse CB Male: Other Other cereals +Mules CB Female: < 1 y Mandua +Donkey CB Female: 1-3 y Kodo +Camel CB Female: In milk Kakoon + Pig CB Female: Dry +Poultry CB Female: Heifer CB Female: Other IN Male:< 1 y IN Male: 1-3 y IN Male: Breeding IN Male: Agriculture IN Male: Bullock IN Male: Other IN Female: < 1 y IN Female: 1-3 y IN Female: In milk IN Female: Dry IN Female: Heifer IN Female: Other Framework to combine feed resources data base and water input requirement estimates Estimation of water investment in livestock feed production is data intensive and complex (Haileslassie et al., 2011). Compounding factors are for example multiple use of agricultural water, limited knowledge of water productivity on natural pasture, CPR, forest, extent of use of plant biomass for feeding etc. However, the major challenges in estimating feed-mediated livestock water productivity lies in defining and describing feed resources and feed usage rather than in estimating water depletion. The feed data base tool as conceived and constructed by NIANP (2003) and described in the ICAR-NORI, Karnal I February 6-8, 2016

7 previous chapter provides a very good starting point. It simply needs to be connected and combined with modules that estimate total evapotranspired water per hectare land-use. Key parameters and variable required are: 1) reference evapo-transpiration (ETO) calculated from temperature, wind speed, humidity and rainfall, 2) crop specific coefficient derived from crop phenology (Kc); and 3) length of growing period (LGP) see also Figure 2. Specifications and tools to process and compute effective rain fall and evapo-transpiration are well established (e.g. Allen et al., 1998; FAO, 1998). Data used as input into these tools comprises rainfall and reference evapo-transpiration (ET 0 ) or detailed climatic parameters to compute ET 0 Many countries have a good density of climatic station network that, with relatively straightforward processing, can provide the necessary input data. There exist freely available global datasets containing a variety of climate variables. The WorldClim ( /worldclim.org/), and CCAFS ( / data/) data portals and local climate estimator [LocClim (FAO, 2005)], for example, contain information for long term averages. Local relevance and spatial resolution remain important challenges. Cross-checking with the above-mentioned climate station data is thus important. The evaporation power of the atmosphere is expressed by the reference crop evapo-transpiration (ETo). The reference crop evapo-transpiration represents the evapo-transpiration from a standardized vegetated surface. This needs crops specifications, like crop coefficients, stress resistance factors, rooting depth, which are Qlmfl.ti.' gafil. ~' f!fl11l. - Min and Max- Temperature ( oq - Humidity(%) -Rainfall - Wind speed ( km day ') - Sunshine (hrs day ') - Radiation (Mj m- 2 day-1 ) - Volume of water per ~!Jl.mrzla 2l [Qfll~ oo.d Total evapo-transpired Land use land irrigation and number of procedures water by feed sources oover (ha) as irrigation structured in CrDQS gad squ 2006) Table4 ~ - CropWat (FAO 1998; Feed resources by types - Soil type and structure Allen et al, 1998) (Kgha'> - Crop types and management practices ( food and fodder crops) - Length of growing period for different stages of development r- - Budget (Raes et al, type (m' ha"1) - Feed Dry Matter (kg m 'J i Conversion factors, '---o HI, feed use factor (as structured in Table 4) Figure 2: A simplified framework to combine feed resources data base and water input requirement estimates (Blummel et al., 2014) ICAR-NDRI, Karnal I February 6-8, 2016

8 available also for major crops from Allen et al., (1998) or already incorporated into these tools: selection of crop of interest suffices to capture these crop specific values. Validation of these for the local circumstances will be important. Soil data are also important inputs to these tools. Crop Wat, for example, requires a very simplified soil type in terms of its structure (FAO 1998) to compute soil water. Information such as FAO's global soil map or ISRIC's Harmonized World Soil Database can be also explored to capture such soil information. With increasing information technology and worldwide data networking, the opportunities to use relevant and good quality global or regional data sets are likely to increase. This approach was initially applied to estimate water requirement for the three major classes offeeds (NIANP, 2003) crop residues, concentrates and greens in four districts in the state of Karnataka in India, Bijapur, Tumkur, Raichur and Chikmagalur. As shown in Table 2 crop residues were the most and greens the least water-use efficient feeds with concentrates taking an intermediate position. The water invested in crop production serves the grain and residues (Haileslassie et al., 2009). In order to understand the water productivity of enterprises at household or system scale, partitioning the total ET water between feed and grain is important (Haileslassie et al., 2009). This way of allocating water is however only one approach others use economic criteria for the allocation of water for example the cost ratio crop by-products to grain (e.g. Singh et al., 2004). Here we assumed that the water used to produce a unit of grain and residue are equal and consequently used harvest indices to partition total ET in crops. However, total ET was allocated to greens. Table 2: Water use efficiency of different classes of feeds across different districts District Litres of water required to produce 1 kg of Crop Residues Concentrates Greens Bijapur Tumkur Raichur Chikmagalur Generally the greens in the study areas came from forests, wastelands, permanent pasture, fallows etc. The yield for these different land use units as estimated by NIANP (2003) ranges from 1-5 tons. The highest yield is for permanent pasture and grazing lands and the lowest yield for fallow and wasteland. Greater area under the latter land use and the aggregated biomass yield is therefore low. This factor will contribute to the low water use efficiencies for greens in producing a unit of biomass. More work needs to be done to understand the factors that contribute to variations in water use efficiency within a specific feed across different sites (Table 3). While the overall ranking in water use efficiency i.e. sorghum stover> groundnut haulm > pearl ICAR-NDRI, Karnal I February 6-8, 2016

9 millet forage is similar to that found for classes of feeds in Table 2. However water use efficiency for sorghum stover and groundnut haulm could vary - dependent on the district, by about 100% (Table 3). Key variable for the calculation of water use efficiency was reference evapo-transpiration (ETO) calculated from temperature, wind speed, humidity and rainfall, the crop specific coefficient derived from crop phenology (Kc), length of growing period and of course biomass yield (see also Figure 2). Future work should investigate and model the proportional importance and relevance of these different variables. While biomass yield will likely be a key factor determining water use efficiency, better understanding of the co-factors will be important for designing water use efficient feeding strategies. Table 3: Water use efficiency of different representatives of classes of feeds across different districts District Bijapur Tumkur Raichur Chikmagalur Litres of water required to produce 1 kg of Sorghum stover 1 Groundnut cake' Pearl millet forage In any event by linking information contained in Table 1 with process outlined in Figure 2 it will be possible to establish a causal relationship that associated feed production with water requirements and ultimately feed mediated water requirements for the production of ASF. Using feed data bases to prioritize interventions to dose yield gaps in dairy production and productivity in India Livestock play an important role in the livelihoods of smallholder farmers but production and productivity of ASF such as milk and meat is generally low, and often below the genetic potential of the animals. However, there are huge farmer to farmer variations as evident in Figure 3. Reason for the higher milk yields realized by top farmers and strategies to increase milk and meat production and productivity include: improving nutrition of individual animals to increase growth rates, daily milk yields and length of lactation; altering the local herd structure by culling unproductive animals; and replacing indigenous livestock breeds with higher-producing exotic breeds and crossbred animals. (Interestingly even with a breed milk yields between the 10% best farms and the rest varied by almost threefold (Figure 3) suggesting that in this instance genetics of the animals was not the primary constraints. ICAR-NDRI, Karnal I February 6-8, 2016

10 COw type Milk yield gap VDSA-India 2013 o- l I' i" r Cows(locai)COWS(ml)rOYedltross bred) AI s.. tes Gu;llm GAP-869 GAP-2393 GAP(l.ICoWiday)-2811 GAP GAP-3150 fllrop 10% of1he households that """'1h highest- milk )'leldil]the other 90% Figure 3: Milk production levels of the 10% top farmers (left bars) versus milk levels of the remaining 90% farmers (right bars) in different states of India (VDSA Survey 2013 ). Aforementioned strategies may or may not require an increase in the amount and/or quality of feed resources. The key question here will be if replacement oflower producing animals by higher producing ones will result in an overall reduction oflivestock numbers. If so feed requirement will decrease because of the shift of feed use from maintenance to production. For example Bliimmel et al., (2013) calculated that, relative to the production of 81.8 million tons of milk in India in 2005, feed requirement in terms of ME could be reduced by 27 and 41% if average across herd daily milk production would increase from 3.6 to 6 and 9 kg, respectively, when accompanied by a correspond decrease in number of dairy animals. That is the same amount of milk million tons- would be produced from fewer animals. This could be achieved with reallocation of existing feed resources i.e. without increasing feed quality (Tarawali et al., 2011). In any event, due to competing land uses (e.g. cropping, industry and urbanization) there are limited opportunities to increase feed and fodder production. We present here feed supply - demand approach currently used in a project funded by the Bill and Melinda Gates Foundation (BMGF) to identify opportunities to increase dairy and meat production in India within the constraints of current feed resources. This case study that looks at dairy production in the irrigated agricultural zone of the state of Bihar is used as an example in this paper. Bihar is a high priority state for many donors and other development actors. The described feed data base ofnianp (2003) contained unfortunately no updated Bihar data so more recent data about Bihar were distilled from Ministry of Agriculture (MoA 2014a, MoA 2014b) to come up with the kind of information contained in Table 1. Based on animal numbers and average daily milk yields taken from the 2012 livestock census (Table 4; MoA 2014a, MoA 2014b), average daily milk production in ICAR-NDRI, Karnal I February 6-8, 2016

11 Bihar was estimated to be 19.7 thousand tons. This equated to approximately 190 g milk per person per day. The daily and annual energy requirements for cattle and buffalo to maintain this level of production were estimated using equations from AFRC (1993). These equations account for animal live weight, sex, activity, amount of milk produced and milk fat content. The amount of energy required in livestock feed was greater than that produced locally from crop residues, greens and concentrates, so it was assumed that additional feed resources were sourced from outside of Bihar. This highlights one of the limitations of a localized feed supply - demand scenarios namely that while livestock population and crop production data are at district and state level, feed resources often move across boundaries and may even be imported from overseas. Values for the amount of feed resources imported from other areas (or exported from Bihar) were difficult to obtain. We assumed that enough feed was available to support the current level of dairy production, and used a spreadsheet-based calculator to investigate how milk production could be increased without increasing the total demand for feed. This necessitated a decrease in the total number of dairy animals. Table 4: Total number of cattle and buffalo in Bihar state, number oflactating animals and average milk yields. Data is from 2012 cattle census (MoA 2014a). Animals Lactating Ani- Average milk yield (kg/head/day) (millions) mals (millions) All zones Irrigated zones Indigenous cattle Crossbred cattle Buffalo Results suggest that reasonable increases in milk production at a state level can be made by reducing the total number of local cattle and better feeding the remaining animals (Table 5). While this may be considered a risky or undesirable strategy at an individual farmer level, movement of people out of the agricultural sector means that it is not implausible. If this strategy was accompanied by culling of mature male cattle, production could be increased even further. Male cattle have little value to farmers except as draught animals, and nationally, there was a 19% decrease in the number of male cattle and buffalo between 2007 and 2012 (MoA 2014a). This may indicate a shift away from the use of draught animals towards mechanized agriculture and an increased focus on dairy production. Improving production of crossbred cattle gave smaller increases because they comprise only a small portion of the current livestock population. Larger gains could be made from replacing indigenous cattle with crossbred cattle or buffalo, especially if they were fed better to achieve higher daily milk yields. Replacing 50% of local cattle with buffalo or crossbred cattle and increasing individual animal milk yields through better feeding would increase milk availability to around 250 g per person/ day. ICAR-NDRI, Karnal I February 6-8, 2016

12 Table 5: Examples of interventions to increase milk production in Bihar, assuming a constant level of feed energy available. Baseline milk production estimates are based on animal numbers from the 2012 livestock census and reported average milk yields (MoA 2014a; MoA 2014b). Animal equivalents are based on a 350 kg cow. Scenario Animal equivalents Milk/day Milk per person (millions) (thousand t) (glday) Baseline Replace 50% local cattle with buffalo Increase average milk production from crossbred cattle Increase average milk production from indigenous cattle Change population structure of local cattle herd (less mature males) Replace 50% local cattle with crossbred cattle Increase average milk production from buffalo Increase average milk production from crossbred cattle and buffalo Replace 50% local cattle with buffalo increase buffalo milk production Change population structure of local cattle herd + increase milk production Replace 50% local with crossbred cattle increase crossbred cattle milk production In this example, use of a feed supply - demand tool provided clear guidelines on where the BMGF and other organizations could target programs to increase dairy production. Our analysis indicated that the biggest advantages could be achieved by targeting farmers that currently keep local cattle breeds - reducing animal numbers and increasing individual levels of nutrition, followed by a transition from local cattle to buffalo and crossbred cattle. However, it should be noted that the accuracy of results from the feed balance exercise are limited by the accuracy of data on livestock populations and feed resources. While India conducts a regular livestock census, similarly detailed livestock data is not available for all countries, especially in the developing world. In regards to feed resources, in addition to accurate reporting of grain yields, there is an assumption that factors used to estimate availability of stovers, chunnis and other livestock feeds based on grain production are sound, but this will be highly dependent on local crop management, harvest and storage practices. Estimating the amount of green ICAR-NORI, Karnal I February 6-8, 2016

13 ""''"'J'! ICAR. feed available from common lands, wasteland, fallows, forests and cultivated forages is also challenging. However many of above criticized assumptions and constants can be regarded as placeholder to be replaced once better input data are available. In other words, a conceptually sound frame work is more important than specific and currently perhaps inadequate input data. In this context it is important to understand that the scenarios presented in Table 4 and 5 are essentially based on the kind of information presented in Table 1. These information combined with process outlined in Figure 2 will ultimately yield feed mediated water requirements for different scenarios of production of ASF as compared in Table 4 and 5. ICAR-NDRI. Karnal I February 6-8, 2016