2002 IFA REGIONAL CONFERENCE FOR ASIA AND THE PACIFIC Singapore, November 2002

Transcription

1 2002 IFA REGIONAL CONFERENCE FOR ASIA AND THE PACIFIC Singapore, November 2002 IMPLICATIONS OF SITE-SPECIFIC NUTRIENT MANAGEMENT IN IRRIGATED RICE ON FUTURE FERTILIZER USE IN SELECTED ASIAN COUNTRIES C. WITT, A. DOBERMANN, D. DAWE IRRI, The Philippines University of Nebraska, USA IRRI International Rice Research Institute DAPO Box 7777, Metro Manila, Philippines c.witt@cgiar.org

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3 IFA REGIONAL CONFERENCE FOR ASIA AND THE PACIFIC Singapore, November 2002 Implications of site-specific nutrient management in irrigated rice on future fertilizer use in selected Asian countries Paper by C. Witt, A. Dobermann, D. Dawe IRRI International Rice Institute, The Philippines University of Nebraska, USA I. Summary Rice production growth in Asia was forecasted to increase by about 25% from 1999 to 2020 using IFPRI s IMPACT model. Based on this forecast, different scenarios were explored to determine production requirements in irrigated rice for the given time period based on assumptions on harvest area and production growth on irrigated and non-irrigated rice land. On average, required rice production on irrigated land in Asia would need to increase at an annual rate of about 1.25% from 406 Mio. t in 1999 to about 527 Mio. t by the year This would assume a slight increase in irrigated rice land by 0.15% yr -1, so that yields would have to increase by 1.1% yr -1 from about 5.3 t ha -1 in 1999 to 6.7 t ha -1 in Using a large data set from on-farm experiments with irrigated rice in six Asian countries conducted between 1997 and 1999, fertilizer use, recovery efficiencies and indigenous nutrient supplies for N, P and K measured in farmers fields were used as initial input parameters for 1999 to simulate fertilizer requirements until 2020 using a modification of the model QUEFTS. Two scenarios were evaluated with i) no changes in nutrient use efficiencies and a baseline growth in total fertilizer use of 1.1% yr -1 comparable to the required growth rate in yield (scenario 1), and ii) increases in fertilizer growth rates of annually 0.55% for N, 0% for P and 4% for K assuming efficient fertilizer use and balanced nutrition through site-specific nutrient management (SSNM) by 2020 (scenario 2). Model simulations and results from on-farm evaluation of SSNM suggest that future yield and production requirements are likely to be met, if farmers had access to improved nutrient management strategies while gradually improving the general crop management. Germplasm was assumed to improve at rates like in the last 30 years. With scenario 1, only 90% of the targeted yield of 6.7 t ha -1 could be achieved in 2020 so that production would fall short by 56 Mio. t because of inefficient use of fertilizer N and unbalanced fertilization (N:P 2 O 5 :K 2 O ratio of 6.3 : 1 : 0.75). In scenario 2, yield and production targets were met through proper N management and a N:P 2 O 5 :K 2 O ratio of 3.1 : 1 : 1.6 that would be required by the year

4 Total fertilizer consumption in 2020 was similar for both scenarios but fertilizer K would have to increase on the expense of fertilizer N and P in scenario 2. Data from three years of on-farm testing of SSNM in were presented showing increases in yield (+7%), profit (+55 US$ ha -1 crop -1 ), N recovery from applied fertilizer (+24%) and agronomic N use efficiencies (+29%) compared with the farmers fertilizer practice. Responses to additional fertilizer K were evaluated where required and yield increases ranged from t ha -1. The principles of SSNM have recently been refined and the technology is now well positioned for wider scale farmer evaluation and dissemination. II. Introduction Current fertilizer management in irrigated rice in Asia is characterized by inefficient and unbalanced use of inorganic fertilizers. The amount of grain yield produced per unit fertilizer N applied is low and fertilizer P and K rates are not adjusted to meet plant nutrient requirements (Dobermann et al 1998; Dobermann et al 2002). Present yields in irrigated rice average about 5.3 t ha -1, which is only about 60% of the climate adjusted yield potential of existing highyielding varieties (Cassman and Harwood 1995; Matthews et al 1995). Considering the forecasted increases in agricultural production due to growing population and per capita income (IFPRI 1995), yields will have to further increase especially in the intensive irrigated rice based systems that contribute to about 70% of the total production. Attempts have been made to explore different scenarios of future fertilizer needs until 2015 and 2030 for major food crops including rice (FAO 2000). Scenarios included simulations assuming that total fertilizer use of N, P and K would increase according to the production growth necessary to meet the forecasted future demand, and a fertilizer growth case where the total fertilizer use efficiency is expected to increase. Fertilizer requirements were calculated based on assumptions about the relationship between yields and fertilizer application rates, and assumptions were adjusted over time for country and crops. Inspired by these fertilizer scenarios, we revisited own earlier attempts to forecast future fertilizer requirements (Dobermann and Cassman 1997; Dobermann and Dawe 2000) that were based on a modification of the model QUETS (QUantitative Evaluation of the Fertility of Tropical Soils) by Janssen et al. (1990). It was our aim to investigate in greater detail the fertilizer requirements in irrigated rice rather than to compare results with the model predictions by FAO that also included non-irrigated rice. 2

5 The specific objectives of our study were:! to summarize current agronomic characteristics of irrigated rice based on a large data set from experiments in farmers fields of six Asian countries,! to re-evaluate future rice production requirements for irrigated and non-irrigated rice in Asia,! to evaluate two scenarios of growth in fertilizer use similar to the approach by FAO (2000) in comparison to likely production goals in 2010 and 2020 using a modification of the model QUEFTS (Janssen et al 1990),! to evaluate the fertilizer scenarios using results from the on-farm evaluation of a novel site-specific nutrient management (SSNM) strategy for irrigated rice in Asia that is based on the model QUEFTS (Dobermann and White 1999; Witt et al 1999), and! to summarize opportunities for the delivery and dissemination of improved SSNM strategies in irrigated rice in Asia. III. Current agronomic characteristics of irrigated rice farms in Asia Key parameters of productivity, nutrient supply and nutrient use efficiency were measured in 179 farms in Asia in (Table 1). The data set is probably representative for irrigated rice in Asia as data origin from nine sites in six Asian countries covering a wide range of biophysical and socio-economic conditions (Dobermann et al 2002). The data set was used to provide some of the initial model input parameters such as fertilizer use, recovery efficiencies and information on the indigenous soil nutrient supply. The latter was measured individually for N, P and K as nutrient limited yield in omission plots (0-N, 0-P, 0-K), where one of the three nutrients N, P or K is omitted while the others are supplied in ample amounts. Yields averaged about 5.2 t ha -1 ranging from 4.4 to 6.1 t ha -1 (interquartile ranges), while the indigenous supply of N was sufficient to support yields of 3.2 to 4.8 t ha -1 (median 4.0 t ha -1 ). The yield gain of about 1.2 t ha -1 over the 0-N plot was achieved with 114 kg N ha -1 at an agronomic N efficiency (AEN) of only 10 kg grain per unit fertilizer N applied. With good nutrient and crop management, agronomic efficiencies of kg kg -1 can be achieved. Inefficient use of fertilizer N was mainly related to inadequate splitting and timing of fertilizer N resulting in both low recovery efficiencies of applied fertilizer N (REN) and physiological N efficiencies (PEN, grain yield increase per unit plant N derived from fertilizer) as compared with the achievable values (Table 1). On average, farmers fertilizer P and K rates were sufficient to support current yields. Low internal efficiencies of P (IEP, kg grain produced per kg plant P at maturity) even indicated that fertilizer (median 41 kg P 2 O 5 ha -1 ) was applied in surplus, which was further supported by a positive P balance of 4 kg P ha -1 crop -1. Average fertilizer K rates of 29 kg K 2 O ha -1 were sufficient to support current yields of 4.5 to 6.0 t ha -1. However, more than 25% of all farmers did not apply any fertilizer K and the average amount applied was to low to balance nutrient removal with grain and straw (-27 kg K ha -1 crop -1 ). 3

6 Table 1. Yield, fertilizer use, resource use efficiency and indigenous nutrient supply in 179 farms in six Asian countries (3-4 crops sampled in , n = 675). Modified from Dobermann et al. (2002; 2003b). Irrigated rice in Asia per year 25% Median 75% Attainable Yield and fertilizer use Grain yield t ha N fertilizer kg ha P 2 O 5 fertilizer kg ha K 2 O fertilizer kg ha Resource use efficiency Recovery efficiency of N (REN) kg kg Physiological N efficiency kg kg (PEN) Agronomic N efficiency (AEN) kg kg Internal N efficiency (IEN) kg kg Internal P efficiency (IEP) kg kg Internal K efficiency (IEK) kg kg P balance kg ha K balance kg ha Indigenous nutrient supply a Grain yield in 0-N plots t ha Grain yield in 0-P plots t ha Grain yield in 0-K plots t ha a Statistics are based on eight (0-N) or three to four (0-P and 0-K) successive rice crops sampled in each field from (n=155). IV. Scenarios to predict rice production in Asia using the IMPACT model There are several models that have been used to forecast future global rice production. One of the best and most widely used is IFPRI s IMPACT model (Rosegrant et al 2001), which models more than 25 agricultural commodities (e.g., rice, wheat, corn, vegetables, oils, sugar, fish) in more than 35 different geographical regions/countries. The model allows for interactions in production and consumption between different commodities and allows for international trade to equilibrate national supply and demand in each commodity. Rice production in Asia was predicted until 2020 using data from 1999 as the baseline: the total production in 1999 was about 530 Mio. t on a total production area of about 135 Mio ha rice land excluding Japan, Korea and Chinese Taiwan (FAO 2002; DGBAS 2002). Average yield on irrigated and non-irrigated rice land was 3.92 t ha -1. We assumed that the 56% share in irrigated rice land of the total production area for 1991 (IRRI 1993) remained unchanged until

7 Yield of irrigated and non-irrigated rice (5.36 and 2.08 t ha -1, respectively) were calculated for 1999 based on estimates from 1991 (4.9 t ha -1 vs. 1.9 t ha -1 ) (IRRI 1993) assuming a 1.1% yield growth necessary to generate the 1999 production figures. Note that the calculated irrigated rice yield of 5.36 t ha -1 in 1999 corresponds fairly well with the 5.17 t ha -1 measured in farmers fields in six Asian countries in (Table 1). The IMPACT model forecasted an approximate rice production growth in Asia of about 25% from This is approximately equal to the expected population growth in Asia during this period, as income growth in Asia no longer makes a substantial contribution to growth in rice demand. IMPACT forecasts a very small growth in rice area harvested, but we ignored this growth and assumed that all of the increased production will come from higher yields (this assumption will have only very minor impacts on our scenarios). We then consider four scenarios by which a 25% increase in rice production from 530 Mio. t in 1999 to 662 Mio. t in 2020 can be achieved: Scenario 1: Irrigated and non-irrigated rice area (76 vs. 59 Mio.ha in 1999) remain constant, while yield equally increases by 25% in irrigated and non-irrigated rice until Scenario 2: Irrigated and non-irrigated rice area remain constant at the 1999 levels, while yield increases in irrigated rice by 33% and remains constant in non-irrigated rice. Scenario 3: Irrigated rice area increases by 7.1% to 81 Mio. ha in 2020, while the non-irrigated rice area declines by 9.1% to 54 Mio. ha in Yield increases by +21% in both irrigated and non-irrigated rice from 1999 to Scenario 4: Irrigated and non-irrigated rice area change as given in Scenario 3, but yield increases in irrigated rice by 26% and remains constant in non-irrigated rice. Table 2. Production and growth rates for different scenarios to reach 661 Mio. t rice production from irrigated and non-irrigated rice land by 2020 in Asia (excluding Japan and Taiwan). Scenario Irrigated rice Non-irrigated rice Production Yield Growth rate Productio n Yield Growth rate Mio. t kg ha -1 % year -1 Mio. t kg ha -1 % year -1 Current (1999) Scenario 1 (2020) Scenario 2 (2020) Scenario 3 (2020) Scenario 4 (2020) We use round numbers in our simulations instead of the exact numbers from the IMPACT model. This is done primarily because we use a different base year than that used in the IMPACT model. Any small deviations between our simulations and those in IMPACT are well within any reasonable margin of error. 5

8 In scenarios 3 and 4, we assume that irrigated rice area increases while non-irrigated rice area decreases. This is consistent with the fact that the share of irrigated rice area in total rice area increased from 0.52 to 0.56 from the late 1970s to the early 1990s. Despite water shortages in many parts of the world, irrigated area is still expanding, albeit slowly. At the same time, rice area planted in non-irrigated systems is declining (Dawe et al 1998). As shown in Table 2, the required rice production on irrigated land will have to be in the range of 507 to 550 Mio. t by the year For the fertilizer scenarios described in the next sections, we assumed that irrigated rice production has to increase by 1.25% annually to 527 Mio. t by 2020 (+30%), which is about the average of the four scenarios in Table 2. This would further assume a moderate increase in irrigated rice land by 0.15% annually to 78.4 Mio. ha until 2020 (+3.2%). Yields in irrigated rice would then have to increase by 1.1% yr -1 from about 5.3 t ha -1 in 1999 to 6.7 t ha -1 in 2020 (+26%), which would be consistent with our earlier assumptions on yield growth between 1991 and V. Modeling fertilizer requirements We used a modification of the model QUEFTS by Janssen et al. (1990) to evaluate the effect of imbalanced fertilization on yield and to calculate balanced fertilizer requirements for specified yield goals from 1999 to The model was chosen because it has been successfully used for predicting field- and season-specific fertilizer N, P and K requirements in irrigated rice across a wide range of environmental conditions in six Asian countries (Dobermann et al 2002; Wang et al 2001; Dobermann et al 2003d). Details of the approach are described elsewhere (Witt et al 1999; Dobermann and Fairhurst 2000; Dobermann and White 1999), so that we only provide a brief overview of the general principles of QUEFTS and its modifications relevant to this paper. 1. QUEFTS model The model divides the relationship between yield and nutrient supply into several steps by taking interactions in supply, acquisition and utilization of N, P and K into account. Yield is predicted as a function of 1) yield potential, 2) definition of the relationship between grain yield and plant nutrient accumulation, 3) field-specific estimates of the indigenous nutrient supplies of N, P and K, and 4) estimated recovery efficiencies of fertilizer N, P and K. Fertilizer requirements for a specified yield goal can be estimated by employing an optimization routine to estimate balanced nutrient requirements based on estimates of the indigenous supply of N, P and K as measured in nutrient omission plots. The relationship between grain yield and nutrient accumulation at harvest has been calibrated for rice using the project s database with more than 2000 data entries (Witt et al 1999). The model predicts a linear increase in grain yield if nutrients are taken up in balanced amounts of 14.7 kg N, 2.6 kg P and 14.5 kg K per 1000 kg of grain until yield targets reached about 70-80% of the climate-adjusted potential yield (Ymax). The corresponding internal efficiencies (kg grain produced per kg nutrient uptake at harvest) were 68 kg grain kg -1 N, 385 kg grain kg -1 P and 69 kg grain kg -1 K for a balanced nutrition. 6

9 The model predicted a decrease in internal efficiencies when yield targets approached Ymax resulting in an increased plant nutrient requirement per unit yield. Unbalanced fertilizer rates that are not adjusted to indigenous nutrient supplies would typically lead to a situation where the internal nutrient efficiencies of one or more nutrients are sub-optimal. This may affect yield, if the supply of a particular nutrient does not match the plant demand in which case the internal efficiency of the particular nutrient would be above optimal levels (low nutrient concentration). In this study, we used initial fertilizer recovery efficiencies (kg plant N per kg fertilizer N applied expressed in %) of 30% for N (Table 1), 20% for P and 40% for K as these are typical average values measured in farmers fields with irrigated rice across five Asian countries in 1997 to 1998 (RTOP database, unpublished). A nutrient balance model was constructed taking into account nutrient inputs from fertilizer, manure, atmosphere, and biological N fixation (in case of N). Nutrient outputs considered crop removal with grain and straw, percolation and seepage and gaseous N losses due to volatilization and denitrification. Values were either based on averages of actual measurements or estimates from the literature. Further details on the construction of the nutrient balance are given by Dobermann et al. (2003a). 2. A simplified SSNM strategy based on QUEFTS principles The site-specific nutrient management approach based on QUEFTS was refined and simplified into tools and guidelines for the delivery of improved nutrient management in Asia s irrigated rice systems (Witt et al 2002a). In the following, we briefly describe these simplified principles and full details are given elsewhere (Witt and Dobermann 2003; Witt et al 2002b). The QUEFTS principles offer a basic plan for a pre-season calculation of balanced fertilizer rates considering the deficit between plant nutrient requirement and soil nutrient supply. This deficit largely depends on the expected yield gain, which we define as the required yield increase over the nutrient limited yield to reach a season-specific yield goal. To consider differences in soil supply among nutrients, yield gains have to be estimated for N, P and K separately. As a rule of thumb, we estimate that 40 kg fertilizer N, 20 kg P 2 O 5 or 30 kg K 2 O would be required to raise the respective nutrient limited yield by 1 t ha -1. These fertilizer rates were calculated based on QUEFTS principles: ( GY GY0 N ) UN' FN = [Equation 1] REN FP = FK = ( 0 GY GY P ) UP' [ 15%] REP GY GY ( 0K ) UK' 1.2 REK [ 15%] [Equation 2] [Equation 3] 7

10 where FN, FP and FK are the recommended fertilizer N, P 2 O 5 and K 2 O rates in kg ha -1 ; GY is the desired yield goal in t ha -1 ; GY 0N, GY 0P and GY 0K are the grain yields in t ha -1 measured in nutrient omission plots (0-N, 0-P, 0-K); UN, UP and UK are the plant uptake requirements of kg N, 2.6 kg P and 15 kg K per t grain yield; REN, REP and REK are the expected fertilizer recovery efficiencies of 40-50% for N, 25% for P and 50% for K. Fertilizer P and K rates were finally reduced by 15% because previous on-farm research has shown that the desired yield goal will not be reached every season due to constraints other than nutrient management (e.g., climate, pests, etc.). Such simple rules are only valid under the assumption that i) considering the law of diminishing return, a yield goal was chosen of less than 70-80% of the potential yield, ii) moderate to high N efficiencies can be reached with improved nutrient management under field conditions, iii) soil P and K fixation is low to moderate, iv) K losses due to leaching are small, and v) about 4-5 t straw/ha is returned after each harvest (incorporated or burned). The estimation of fertilizer N requirements based on Equation 1 is probably sufficiently robust for pre-season planning of N applications since opportunities exist to further fine tune N management within the season through the use of a leaf color chart (Balasubramanian et al 2000). The strategies outlined in equation 2 and 3, however, would suggest to apply fertilizer P and K only if a yield response was expected. This may be a sensible recommendation where farmers face short-term constraints in the availability of funds to purchase fertilizer, but would likely lead to a depletion in soil nutrient reserves when practiced for several seasons. A simple nutrient balance model was therefore constructed based on equations 2 and 3, but also taking into account i) nutrient inputs from irrigation water and organic sources such as farm yard manure, and ii) nutrient removal with grain and straw. This approach considers the soil indigenous nutrient supply as the status quo and takes more information (e.g., on straw management) into account when developing fertilizer P and K requirements. This would not only ensure that the plant requirements of a given yield goal are met, but also aim at long-term strategies in the adjustment of soil nutrient supplies that would more accurately addresses such important issues as mining and replenishment of soil P and K reserves. For maintenance, increased fertilizer P and K rates are probably required to replenish the greater crop removal with grain and straw at elevated yield levels (see example for P given in table 3). 8

11 Table 3. Maintenance fertilizer P 2 O 5 rates for irrigated rice depending on yield in 0-P plots and yield goal. Adapted from Fairhurst and Witt (2002). Yield in 0-P plots (t ha - 1 ) Yield goal (t ha -1 ) Fertilizer P 2 O 5 (kg ha -1 ) * * * * A lower yield target is recommended. VI. Predicting future fertilizer requirements for irrigated rice in Asia Initial fertilizer rates given in Table 1 were used as input parameters for the initial year 1999 and subjected to two different scenarios of fertilizer use to predict yield until 2020 using QUEFTS. Scenario 1 describes a situation where unbalanced fertilizer application as currently practiced is continued until 2020, while fertilizer requirements in scenario 2 were adjusted according to the principles of site-specific nutrient management. The following general assumptions were made for both scenarios based on the forecasted production requirements for irrigated rice as described in section 2:! Required production growth rate: +1.25% yr -1 (406 to 527 million t by 2020)! Assumed harvest area growth rate: +0.15% yr -1 (76 to 78.4 million ha by 2020)! Required yield growth rate: +1.11% yr -1 (5.3 to 6.7 t /ha by 2020) The yield potential across agro-climatic zones and growing seasons in Asia in 1999 was estimated with 8.3 t ha -1 based on earlier predictions using the model ORYZA (Matthews et al 1995). It has been estimated that the yield potential of newly released modern rice varieties increase by about 0.75% annually based on yields of varieties released over a 30-year period (Peng et al 1999). We assumed a moderate increase in yield potential of 0.5% yr -1 due to germplasm improvement from 8.3 t ha -1 in 1999 to 9.2 t ha -1 in 2020 (11% increase from 1999 to 2020). Internal nutrient efficiencies (yield increase per unit nutrient uptake) are likely to decrease when yield targets exceed 75-80% of the potential yield (Witt et al 1999), but yield targets would be below 75% of the respective potential yields in both 1999 and

12 We therefore assumed that the internal nutrient efficiencies would not change until 2020 also considering that there appears to be little genotypic variation in nutrient requirements of modern, high-yielding varieties under fertilized conditions (Singh et al 1998; Witt et al 1999). A constant harvest index of 0.48 kg kg -1 was chosen in this study based on average values measured in farmers fields in six Asian measurements (Witt et al 1999). For the nutrient balance calculations, we assumed that on average 30% of the straw is returned to the field with further nutrient losses of incorporated materials (50% N, 10% P and 30% K). The annual input of farm yard manure (0.5% N, 0.1% P, 0.5% K) was set to 0.5 t ha -1 considering the limited use in Asia nowadays (Dobermann et al 2003d). Yields predicted by the QUEFTS model were 0.6 t ha -1 higher than the measured values in 1999 using fertilizer rates and indigenous nutrient supplies provided in Table 1. Simulated and measured nutrient uptake of N, P and K corresponded well (data not shown) and were sufficient to achieve higher yields, but yield losses occurred due to nutrient imbalance, pest and weed problems, and lack of water (Dobermann et al 2003d). For a situation of unbalanced nutrition (scenario 1), we assumed constant yield losses of 0.6 t ha -1 y -1 until 2020, while yield losses would decline to 0.4 t ha -1 by 2020 with balanced nutrient management (scenario 2). We finally assumed that restrictions due to water availability remain unchanged in the future. Water availability may decline in certain areas, but this could be offset by increased water use efficiency (water saving crop management, better infrastructure and irrigation technologies). Fertilizer prices were based on current regional averages and assumed to remain constant until Non-fertilizer costs were estimated with 350 US$ ha -1 (Moya et al 2003). Economic and socio-economic factors influencing farmer s decision making were not considered in this study. 1. Baseline growth (scenario 1) In this scenario, we assume no changes in current farmers nutrient management strategies except for a general increase in fertilizer rates until Fertilizer N, P, and K use would each increase by annually 1.1%, which is the same as the yield growth rate required to achieve the production goal of 527 Mio. t in irrigated rice by The N : P 2 O 5 : K 2 O ratio of 6.3 : 1 : 0.75 in 1999 (Table 1) would thus remain unchanged until Farmers splitting and timing of fertilizer applications would follow the current practice also in 2020 so that fertilizer recovery efficiencies are not improved. Model predictions for 1999, 2010 and 2020 are given in table 4. Fertilizer rates would increase from currently 114 to 143 kg N, from 41 to 52 kg P 2 O 5 and from 29 to 36 kg K 2 O ha -1 by The total fertilizer consumption would increase from currently Mio. t to Mio. t in 2020 (+30%). Yields, however, would only reach 6.0 t ha -1 by 2020 (+13%) so that the targeted yield (6.7 t ha -1 ) could not be reached and production would fall short by about 56 Mio. t rough rice in

13 The predicted nutrient use efficiencies for 2020 indicate that the N and K uptake by the crop would be insufficient to sustain higher yields. The agronomic efficiency (increase in grain yield per unit fertilizer N applied) is low, because the N management is not timed to meet the crop requirement during the growing season so that recovery of applied fertilizer remains low (which was the major assumption). Plant nutrient uptake could only be increased, if the N management was improved. The scenario 1 for 2020 is also characterized by imbalanced fertilization. Fertilizer K rates would be sufficient to support yields of 6.0 t ha -1 in 2020, but probably insufficient to reach yield targets of 6.7 t ha -1 as the predicted internal nutrient efficiencies for 2020 are already above the optimal value. The internal P efficiency is below the optimal value in 1999 and this difference further increases until 2020 indicating that fertilizer P is increasingly used in excess. The P balance would also be slightly positive throughout 1999 to 2020, while the negative K balance of 19 kg K ha -1 would decrease to about 6 kg K ha -1. It can be concluded from these results that yields, nutrient efficiencies and profit could be increased by 2020, if farmers i) followed an improved splitting and timing scheme of fertilizer N applications, and ii) increased fertilizer K use at the expense of fertilizer P. 11

14 Table 4. Production, fertilizer requirements, crop productivity, resource use efficiency and indigenous nutrient supplies based on baseline growth in fertilizer use as predicted by the model QUEFTS (scenario 1). Irrigated rice in Asia per year Achievable Production required Million t Production predicted Million t Fertilizer N Million t Fertilizer P 2 O 5 Million t Fertilizer K 2 O Million t NPK fertilizer Million t Productivity per crop Yield potential t ha Yield required t ha Yield predicted b t ha Fertilizer N kg ha Fertilizer P 2 O 5 kg ha Fertilizer K 2 O kg ha Fertilizer cost US$ ha Gross return over fertilizer cost US$ ha kg kg -1 Resource use efficiency Recovery efficiency of N (REN) a Recovery efficiency of P (REP) a kg kg Recovery efficiency of K kg kg (REK) a Agronomic efficiency (AEN) b kg kg Physiological efficiency (PEN) b kg kg Internal N efficiency (IEN) b kg kg Internal P efficiency (IEP) b kg kg Internal K efficiency (IEK) b kg kg P balance b kg ha K balance b kg ha a Model input parameters. b Model predicted. 2. Nutrient efficiency growth (scenario 2) In this scenario, we assumed that farmers increasingly follow improved N management strategies and balanced fertilization so that the recovery efficiencies of applied fertilizer would increase by annually 2% for N and 0.5% for P and K. It was further assumed that balanced nutrition would reduce yield losses by 2% yr -1 due to a reduction in pest problems and lodging.we then 12

15 calculated the growth in fertilizer N, P, and K use separately following the simplified SSNM strategy based on QUEFTS principles (Witt and Dobermann 2003; Fairhurst and Witt 2002) to match the production requirements in According to this scenario, fertilizer N would have to increase by only 0.55% yr -1 from 114 kg ha -1 in 1999 to 128 kg ha -1 in 2020 (+12.3% by 2020). Fertilizer P use could remain constant at 41 kg P 2 O 5 ha -1, while fertilizer K would have to be increased by 4% yr -1 from 29 kg K 2 O ha -1 in 1999 to 66 kg ha -1 in 2020 (+126% by 2020). This would results in an improved N:P 2 O 5 :K 2 O ratio of 3.1 : 1 : 1.6 or a N : K ration of about 2:1 by Model predictions for the years 1999, 2010 and 2020 are given in Table 5. Table 5. Production, fertilizer requirements, crop productivity, resource use efficiency and indigenous nutrient supplies based on nutrient efficiency growth using the model QUEFTS (Scenario 2). Irrigated rice in Asia per year Achievable Production required Million t Production predicted Million t Fertilizer N Million t Fertilizer P 2 O 5 Million t Fertilizer K 2 O Million t NPK fertilizer Million t Productivity per crop Yield potential t ha Yield required t ha Yield predicted b t ha Fertilizer N kg ha Fertilizer P 2 O 5 kg ha Fertilizer K 2 O kg ha Fertilizer cost US$ ha Profit US$ ha Resource use efficiency Recovery efficiency of N (REN) a kg kg Recovery efficiency of P (REP) a kg kg Recovery efficiency of K (REK) a kg kg Agronomic efficiency (AEN) b kg kg Physiological efficiency (PEN) b kg kg Internal N efficiency (IEN) b kg kg Internal P efficiency (IEP) b kg kg Internal K efficiency (IEK) b kg kg P balance b kg ha K balance b kg ha a model input parameters b model predicted 13

16 The QUEFTS principles were apparently maintained with the simplified and more practical approach for extensionists as the model predicted that target and production goals would be reached by 2020 through efficient use of fertilizer N and balanced nutrition. The predicted nutrient efficiency indicators indicated nearly optimal values. The predicted agronomic efficiency increased from only 11 to 19 kg grain produced per kg fertilizer N applied in 2020, which was due to the increase in fertilizer N recovered by the crop through better splitting and timing of fertilizer N applications. Nearly optimal values for both physiological N use efficiency (increase in grain per unit plant N taken up from fertilizer) and internal nutrient use efficiencies indicated that fertilizer would be efficiently taken up and translated into grain yield. Although fertilizer P rates would remain constant until 2020, the P uptake by the crop would be more than sufficient to reach the desired yield target, as internal P efficiencies would be slightly below the optimum. Fertilizer P application would balance nutrient removal with grain and straw by 2020, while the negative K balance in 1999 would be reversed to +6 kg ha -1. Although there are considerable uncertainties associated with such nutrient balances, it can be concluded that the simplified SSNM strategy would ensure that plant nutrient requirements are met for the specified yield goal, while also providing long-term strategies in the adjustment of soil nutrient supplies. Fertilizer cost would increase by 21% from 67 in 1999 to 81 US$ ha -1 in 2020, but his increase would be accompanied by a 51% increase in profit from 377 US$ ha -1 to 569 US$ ha -1. Compared to scenario 1, total fertilizer consumption in Asia would remain about the same with scenario 2 in 2020 (18.4 vs Mio. t ) as the recommended increase in fertilizer K compensates the slower increase in fertilizer N use and zero growth in fertilizer P use. The total fertilizer cost for the farmer would also be comparable for the two scenarios (81-84 US$ ha -1 ), but profit in 2020 would increase by about 24% from 467 US$ ha -1 in Scenario 1 to 569 US$ in scenario 2. The increased profit with scenario 2 is associated with a 12% increase in yields compared to scenario 1. VII. Opportunities for site-specific nutrient management The presented fertilizer scenarios suggest that yield and production requirements in 2020 could only be met through improvements in farmers nutrient management strategies. In the following, we present evidence that the assumptions leading to above given scenario 2 are realistic. The site-specific nutrient management (SSNM) strategy has been successfully tested in about 175 farmers fields in six Asian countries between 1997 and In previous publications, we had summarized data from farmers fertilizer practice (FFP) and SSNM for four consecutive rice crops (Dobermann et al 2002; Dobermann et al 2003d). Two additional seasons (year 3) were added to the data set as shown in Table 6, but data from Thailand were excluded because of asynchronous planting schedules in year 3. Major changes in year 3 compared to previous years included a simplification of N management strategies (Dobermann and Fairhurst 2000), and a refinement of P and K recommendations as summarized by Fairhurst and Witt (2002). The previous success of the SSNM approach has been retained despite the simplification of the concept in year 3 (Table 6). Yield increases with SSNM over FFP were comparable to previous 14

17 years (+0.34 t ha -1 ), but the refined concept offered opportunities to utilize nutrients more efficiently. Compared to previous years, fertilizer N, P and K use in SSNM were reduced year by year because i) fertilizer rates in year 1 were assuming too optimistic yield targets, ii) we learned to manage N more efficiently by increasingly shifting towards real-time N management using a chlorophyll meter, and iii) fertilizer P and K rates were adjusted to improved estimates of indigenous nutrient supply and more realistic yield goals following refined concepts. Fertilizer costs with SSNM decreased from 86 US$ ha -1 in year 1 to 65 US$ in year 3, and the 3-year average was comparable to the FFP treatment (74 vs. 72 US$ ha -1 ). Note that the saving in fertilizer cost with SSNM contributed only about 20% to the increased profit with SSNM in year 3. The gross margin above fertilizer cost was 55 US$ greater with SSNM, but the contribution of reduced fertilizer cost was only 10 US$ ha -1. Thus, future increases in farmers profit would most likely have to be achieved through increases in yield assuming that prices for fertilizer and rough rice would not change dramatically with time. Adequate amounts of well balanced fertilizer rates would then be required to meet the increased nutrient requirements as yields increase. Compared to FFP, less fertilizer N (-13%) and P (-31%) was applied with SSNM in year 3, while fertilizer K rates were 15% greater. Average fertilizer use was 106 kg N, 29 kg P 2 O 5, and 56 kg K 2 O ha -1 to achieve yields of 5.7 t ha -1. For comparison, the recently published SSNM guidelines for practical use in extension suggest fertilizer rates 30 kg P 2 O 5, and 50 kg K 2 O ha -1 for yield targets of 6 t ha -1, if the nutrient supply of both P and K is sufficient to support yields of about 5 t ha -1 (Fairhurst and Witt 2002). In scenario 2, the model suggested fertilizer rates of 118 kg N, 41 kg P 2 O 5, and 36 kg K 2 O ha -1 to achieve a yield of 5.7 t ha -1. The SSNM recommendations of year 3 (Table 6) differed from the model predicted values of scenario 2, because we assumed in the latter that i) fertilizer recovery efficiencies would only increase gradually until 2020, ii) farmers fertilizer P rates remained constantly at levels of 1999, and iii) initial fertilizer K rates were lower than recommended with the SSNM concept. Furthermore, scenario 2 assumes that the dissemination of the SSNM concept would only gradually increase in Asia until 2020, so that fertilizer requirements would consequently not match the actual recommendations given to farmers. However, the increased nutrient use efficiencies achieved with SSNM in (Table 6) indicate that the assumptions made for scenario 2 are realistic, if SSNM was successfully disseminated among Asian rice farmers. The agronomic efficiencies (18.1 kg kg -1 ) and recovery efficiencies (0.43 kg kg -1 ) achieved with SSNM in year 3 (Table 6) are close to the values that need to be achieved in 2020 (AEN 19 kg kg -1, REN 0.45 kg kg -1, Table 5). The average yield increases of about 0.4 t ha -1 with SSNM compared to the FFP appear to be small (Table 6), but are sufficient to sustain required yield increases in Asia for about 6 years. Furthermore, current yield levels in irrigated rice in Asia are relatively high and future yield increases are likely to occur in small, incremental steps that involve improvements in crop management and adjustments of fertilizer recommendations every 5-10 years as yields increase. Crop, pest and water management in SSNM was the same as in the farmers practice and current research efforts seek to further exploit the synergy that occurs, if nutrient, pest and crop management was improved simultaneously. 15

18 Table 6. Performance of site-specific nutrient management in irrigated rice fields of five Asian countries (means of 155 farms in China, India, Indonesia, the Philippines, and Vietnam; six consecutive crops in , n = 893). Treatment b Levels a SSNM FFP c P> t c Grain yield All <0.001 (t ha -1 ) Year Year <0.001 Year HYS LYS <0.001 N Fertilizer All <0.001 (kg ha -1 ) Year Year Year <0.001 HYS LYS P Fertilizer All (kg ha -1 ) Year <0.001 Year Year <0.001 HYS LYS K Fertilizer All <0.001 (kg ha -1 ) Year <0.001 Year <0.001 Year HYS LYS <

19 Table 6. (continued) Treatment b Levels a SSNM FFP c P> t c Total fertilizer costs All (US$ ha -1 crop -1 ) Year <0.001 Year Year <0.001 HYS LYS Gross returns above All <0.001 fertilizer costs Year (US$ ha -1 crop -1 ) Year <0.001 Year HYS LYS <0.001 Agronomic efficiency All <0.001 of fertilizer N (AEN) Year <0.001 (kg grain kg N -1 ) Year <0.001 Year <0.001 HYS <0.001 LYS <0.001 Recovery efficiency All <0.001 of fertilizer N (REN) Year <0.001 (kg N kg N -1 ) Year <0.001 Year <0.001 HYS <0.001 LYS <0.001 a All - all six successive rice crops grown from 1997 to 2000; Year 1 crops 1 and 2; Year 2 crops 3 and 4; Year 3 crops 5 and 6; HYS High yielding season; LYS Low yielding season. b FFP - farmers' fertilizer practice; SSNM - site specific nutrient management. c = SSNM - FFP; P> t - probability of a significant mean difference between SSNM and FFP. VIII. The delivery of improved nutrient management strategies The experimental design of on-farm experiments established in did not allow to separate effects of improved N, P and K management on yield. The design was chosen because the SSNM strategy aims at developing and testing improved fertilizer rates for all three nutrients N, P and K simultaneously recognizing the significance of nutrient interactions. Instead of testing the yield response to N, P and K separately, fertilizer rates are developed based on plant based estimate of indigenous nutrient supply and well defined nutrient requirements for pre- 17

20 determined yield goals. Furthermore, the SSNM concept aims at identifying suitable long-term strategies for P and K rather than rates that would only promise yield responses in the short-term. The latter strategy would also have the disadvantage that P and K responses may not be observed in every season, so that the identification of suitable rates could be lengthy with such approaches. The SSNM strategy has been intensively tested and validated (Dobermann et al 2003d; Dobermann et al 2002; Witt et al 1999). The leaf color chart for efficient N management was successfully integrated in the approach, and performance indicators such as yield, plant nutrient uptake and nutrient efficiencies measured throughout the period of development were used to further refine the concept, and (Balasubramanian et al 1999; Witt et al 2002b; Fairhurst and Witt 2002; Dobermann and Fairhurst 2000). The development of improved nutrient management strategies for dissemination includes the identification of adequate N management strategies that are adapted to site-specific needs. These range from location-specific N fertilizer splitting schedules for preventive N management to real-time corrective N management using a leaf color chart (LCC). The latter requires the periodic assessment of plant N status, and the application of fertilizer N is delayed until N deficiency symptoms start to appear. This need-based approach would does not require the estimation of soil N supply or the calculation of a preseason fertilizer rate. Nitrogen management strategies are disseminated to farmers through training and promotional materials such as leaflets, and guidelines on the use of the LCC are printed on the back of each chart. The estimation of P and K requirements through the use of omission plots is challenging for individual farmers, so that current dissemination approaches aim at developing recommendations that would be valid for larger areas of similar nutrient supplying capacity. Practical guidelines to accurately estimate the nutrient limited yield of N, P and K for such larger recommendation domains have recently been developed. It has been estimated that only farms with one omission plot per nutrient would have to be established in a high yielding season to adequately describe nutrient limited yield for irrigated rice domains of about km 2 size (Dobermann et al 2003c). Recommendations should be developed and tested in participatory approaches, which may also involve the on-farm demonstration of increased (or reduced) fertilizer P and K rates. Where increased K use is recommended, farmers would probably need concrete evidence of positive marginal effects of the additional K application on either yield or milling quality (Dawe and Tiongco 2002). In 2001, IRRI and its partners of the National Agricultural Research and Extension Systems (NARES) initiated a new set of on-farm research and demonstration experiments at project sites in India (Tamil Nadu and Uttaranchal), Thailand (Chacheungso), Vietnam (Hanoi) and the Philippines (Maligaya) to test the hypothesis that increased fertilizer K rates would be required at these sites to sustain higher yields. Yield responses to increased fertilizer application were generally in the range of 0.2 to 0.4 t ha -1 and first season results are presented for the two sites in India (Table 7). 18

21 Table 7. Grain yield and fertilizer use at three sites in India with irrigated rice in the states Tamil Nadu (Thanjavur, 2001 kuruvai crop, n = 15), and Uttaranchal (Pantnagar, 2001 rice crop, n = 9). FFP = farmers fertilizer practice; SSNM = site-specific nutrient management; FN, FP, FK = fertilizer N, P, K; FC = fertilizer cost; GRF = gross return above fertilizer cost. Grain yield (t ha -1 ) FN (kg ha -1 ) FP (kg ha -1 ) FK (kg ha -1 ) FC (US$ ha -1 ) GRF (US$ ha -1 ) Thanjavur FFP 6.26 c a b 35 a 44 b 49.8 b c SSNM 6.64 b a 35 a 46 b 53.1 b b SSNM + K 6.93 a a 35 a 91 a 62.5 a a Pantnagar FFP 5.83 c a 42 b 21 c b c SSNM 6.13 b b 48 a 48 b b b SSNM + K 6.51 a b 48 a 72 a a a a Means followed by the same letters are not statistically different at P<0.05 (LSD-test). Two SSNM treatments with the same N and P management were established, but K rates were increased to recommended levels in SSNM+K. Fertilizer K rates were about % greater with SSNM+K and yields increased by about 0.7 t ha -1 compared to the FFP. The additional K contributed a yield increase of t ha -1 (43-56%) to the overall yield increase with SSNM. The remaining yield increase was mainly due to improved N management that was accompanied with an increase in fertilizer N rates in Thanjavur but a reduction in fertilizer N at Pantnagar. Fertilizer cost increased in the order FFP < SSNM < SSNM+K, but the yield increases in SSNM+K were sufficient to further increase profits. Farmers recognized the effect of additional K applied at panicle initiation, which greatly facilitated the dissemination of improved K management strategies in the pilot villages. IRRI is involved in the wider-scale farmer evaluation and dissemination in a number of countries through partnerships with the National Agricultural Research and Extension Systems (NARES), as part of the irrigated rice research consortium (IRRC) and the rice-wheat consortium (RWC). Interdisciplinary NARES teams are involved in on-farm evaluation of innovative nutrient management strategies in Bangladesh, China, India, Indonesia, Nepal, Myanmar, Pakistan, the Philippines, Thailand, and Vietnam. The involvement of public and private sector partners is being strengthened to facilitate the dissemination of information and the delivery of SSNM to rice farmers. 19

22 IX. Acknowledgements This paper could not have been written without the dedicated work of many researchers and support staff participating in the project on Reversing Trends in Declining Productivity, now continuing under its new name Reaching Towards Optimal Productivity as a workgroup of the Irrigated Rice Research Consortium ( We also thank Ms. Olivyn Angeles, Ms. Julie Mae Christie Cabrera-Pasuquin and Mr. Edsel Moscoso for assistance in statistical analysis. The Swiss Agency for Development and Technical Cooperation (SDC), the International Fertilizer Industry Association (IFA), the Potash and Phosphate Institute (PPI/PPIC), and the International Potash Institute (IPI) provided funding for this research. X. References Balasubramanian V, Morales AC, Cruz RT, Abdulrachman S On-farm adaptation of knowledge-intensive nitrogen management technologies for rice systems. Nutr.Cycl.Agroecosyst. 53: Balasubramanian V, Morales AC, Cruz RT, De NN, Tan PS, Zaini Z Leaf Color Chart (LCC): a simple decision tool for nitrogen management in lowland rice. Poster presented at the ASA meeting, Minneapolis, Minnesota, November Cassman KG, Harwood RR The nature of agricultural systems: food security and environmental balance. Food Policy. 20(5): Dawe D, Barker R, Seckler D Water Supply and Demand for Food Security in Asia. Proceedings of the Workshop on Increasing Water Productivity and Efficiency of Rice-Based Irrigated Systems, Los Baños, July International Rice Research Institute (IRRI): Los Baños, Philippines. Dawe D, Tiongco M The economics of potassium use in Asian rice systems. Paper presented at the IPI Golden Jubilee Congress 'Feed the soil to feed the people - the role of potash in sustainable agriculture', 8-10 October International Potash Institute (IPI): Basel, Switzerland. DGBAS National Statistics [online]. Available at (last update 2002; accessed 09 Nov. 2002). Taipeh, Taiwan (China): Council of Agriculture, Directorate- General of Budget, Accounting and Statistics (DGBAS). Dobermann A, Cassman KG Nutrient efficiency in irrigated rice cultivation. In:Plant Nutrition in Tours, France: IFA Agro-Economics Comittee Conference, June Dobermann A, Cassman KG, Mamaril CP, Sheehy SE Management of phosphorus, potassium, and sulfur in intensive, irrigated lowland rice. Field Crops Res. 56: Dobermann A, Dawe D How much fertilizer is needed for irrigated rice in Asia? Paper presented at the annual meeting of the American Society of Agronomy (ASA), 5-9 November 2000, Minneapolis, MN. 20