Site-specific nutrient management for intensive rice cropping systems in Asia

Field Crops Research 74 (2002) 37±66

Site-speci®c nutrient management for intensive rice cropping systems in Asia A. Dobermanna,b,*, C. Witta, D. Dawea, S. Abdulrachmanc, H.C. Ginesd, R. Nagarajane,f, S. Satawathananontg, T.T. Sonh, P.S. Tani, G.H. Wangj, N.V. Chienh, V.T.K. Thoah, C.V. Phungi, P. Stalink, P. Muthukrishnank, V. Ravie, M. Babue, S. Chatupornl, J. Sookthongsal, Q. Sunm, R. Fun, G.C. Simbahana, M.A.A. Advientoa a

International Rice Research Institute (IRRI), Los BanÄos, Philippines b University of Nebraska-Lincoln, Lincoln, NE, USA c Research Institute for Rice (RIR), Sukamandi, Indonesia d Philippine Rice Research Institute (PhilRice), Maligaya, Nueva Ecija, Philippines e Soil and Water Management Research Institute (SWMRI), Thanjavur, Tamil Nadu, India f Agricultural College and Research Institute, Killikulam, Vallanad, Tamil Nadu, India g Pathum Thani Rice Research Center (PTRRC), Pathum Thani, Thailand h National Institute for Soils and Fertilizers (NISF), Hanoi, Viet Nam i Cuu Long Delta Rice Research Institute (CLRRI), Omon, Cantho, Viet Nam j Zhejiang University (ZU), Hangzhou, PR China k Tamil Nadu Rice Research Institute (TNRRI), Aduthurai, Tamil Nadu, India l Department of Agriculture, Rice Research Institute, Suphan Buri Rice Experiment Station, Thailand m Agricultural Research Station, Jinhua, Zhejiang, PR China n Agricultural Technology Extension Station, Jinhua, Zhejiang, PR China Received 26 October 2001; accepted 4 November 2001

Abstract Irrigated rice (Oryza sativa L.) yield increases in Asia have slowed down in recent years. Further, yield increases are likely to occur in smaller increments through ®ne-tuning of crop management. On-farm experiments at 179 sites in eight key irrigated rice domains of Asia were conducted from 1997 to 1999 to evaluate a new approach for site-speci®c nutrient management (SSNM). Large variation in initial soil fertility characteristics and indigenous supply of N, P, and K was observed among the eight intensive rice domains as well as among farms within each domain. Field- and season-speci®c NPK applications were calculated by accounting for the indigenous nutrient supply, yield targets, and nutrient demand as a function of the interactions

Abbreviations: AEN, agronomic ef®ciency of applied fertilizer N; CM, crop management quality score; DAS, days after seeding; DAT, days after transplanting; FFP, farmers' fertilizer practice; GRF, gross return above fertilizer cost; HYS, high-yielding season; IKS, indigenous K supply; INS, indigenous N supply; IPS, indigenous P supply; LYS, low-yielding season; PEN, physiological ef®ciency of applied fertilizer N; PFPN, partial factor productivity of applied N; PI, panicle initiation; REN, recovery ef®ciency of applied N; SSNM, site-speci®c nutrient management; TFC, total fertilizer cost * Corresponding author. Present address: Department of Agronomy and Horticulture, University of Nebraska-Lincoln, PO Box 830915, Lincoln, NE 68583-0915, USA. E-mail address: [email protected] (A. Dobermann). 0378-4290/02/$ ± see front matter # 2002 Elsevier Science B.V. All rights reserved. PII: S 0 3 7 8 - 4 2 9 0 ( 0 1 ) 0 0 1 9 7 - 6

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A. Dobermann et al. / Field Crops Research 74 (2002) 37±66

between N, P, and K. Nitrogen applications were ®ne-tuned based on season-speci®c rules and ®eld-speci®c monitoring of crop N status. The performance of SSNM was tested for four successive rice crops. Average grain yield in the SSNM increased by 0.36 Mg ha 1 (7%) compared to the current farmers' fertilizer practice (FFP) measured in the same cropping seasons or 0.54 Mg ha 1 (11%) compared to the baseline FFP yield before intervention. Average nutrient uptake under SSNM increased by about 10% in the same seasons or by 13% (N) and 21% (P, K) compared to the baseline data. Yield increases were associated with a 4% decrease in the average N rate, but larger amounts of fertilizer-K at sites where the previous K use was low. Average N use ef®ciencies increased by 30±40%, mainly through the use of improved in-season N management schemes. Across all sites and four successive rice crops, pro®tability increased by US$ 46 ha 1 per crop or 12% of the total average net return. The performance of SSNM did not differ signi®cantly between high-yielding and low-yielding climatic seasons, but improved over time with larger bene®ts observed in the second year. Average pro®tability increased from US$ 32 ha 1 pre crop in the ®rst year to US$ 61 ha 1 pre crop in the second year due to improvements in the SSNM approach and re-capitalization of P and K applied in the ®rst year. SSNM required little extra credit for ®nancing, and remained pro®table even if rice prices are somewhat lower than current levels. Further, scope for improvement exists at many sites by alleviating other crop management constraints to nutrient use ef®ciency. Pro®t increases ranged from US$ 4 to 82 ha 1 per crop among eight rice domains. However, pro®t decreases occurred in about 25% of all cases, indicating that a certain minimum level of crop care is required for SSNM to be pro®table. Yields at sites with labor-saving direct-seeding of larger ®elds were about 1 Mg ha 1 lower than those achieved at sites with labor-intensive transplanting and good management, raising concern about future trends in rice production. SSNM has potential for improving yields and nutrient ef®ciency in irrigated rice to close existing yield gaps. The major challenge for SSNM will be to retain the success of the approach while reducing the complexity of the technology as it is disseminated to farmers. The nature of the approach will need to be tailored to speci®c circumstances in different countries. In some areas, SSNM may be ®eld or farm speci®c, but in many areas it is likely to be just region and season-speci®c. # 2002 Elsevier Science B.V. All rights reserved. Keywords: Rice; Site-speci®c nutrient management; Nitrogen use ef®ciency; Indigenous nutrient supply; Pro®tability

1. Introduction During the Green Revolution, rice (Oryza sativa L.) yields in Asia increased at an average rate of 2.5% per year from 1967 to 1984. However, average yield growth rates dropped to 1.2% from 1984 to 1996 (Dawe and Dobermann, 1999). Of particular concern are declining yield growth rates in double- and triplecrop rice monocropping systems where farmers were early adopters of modern high-yielding irrigated rice production technologies (Cassman and Dobermann, 2001). This slowdown was partly due to lower rice prices and the slowdown in demand growth due to secular trends in population and per capita consumption of rice (Dawe, 1998), but concern was also raised about resource degradation and declining or stagnating input use ef®ciency (Cassman and Pingali, 1995; Cassman et al., 1997; FAO, 1994a,b, 1997). Recent on-farm studies suggest that there is little compelling evidence that fertilizer-N ef®ciency in rice has increased during the past 30 years. The average plant recovery ef®ciency of fertilizer N is only about 30% (Dobermann, 2000), although much knowledge has been gained about the nitrogen cycle in lowland rice

environments (Cassman et al., 1998). Application of other macronutrients such as potassium (K) has lagged behind, leading to imbalanced plant nutrition and negative potassium K input±output balances in parts of Asia (Dobermann et al., 1998). The current average yield of irrigated rice of about 5.3 Mg ha 1 per crop is equivalent to about 60% of the climate-adjusted yield potential across different geographical regions and climatic seasons in Asia (Matthews et al., 1995). Although, strategic research is ongoing to develop new rice germplasm with increased yield potential, improved host plant resistance to pests, or N-®xation characteristics (Ladha and Reddy, 2000; Peng et al., 1999; Sheehy et al., 2000), it is uncertain whether any of these efforts will have measurable impact on increasing rice yields in the near future. At issue is whether existing yield gaps can be further exploited through improved nutrient and crop management and whether better nutrient management might be able to restore some momentum to rice yield growth in coming decades. Reductions in N use may be possible in some areas without any sacri®ce in yields (Wang et al., 2001). This would improve farm pro®tability to some extent (lower input costs), perhaps even a

A. Dobermann et al. / Field Crops Research 74 (2002) 37±66

large extent in areas where N fertilizer use is very high, as in China. In general, however, a given percentage increase in yield will do much more for pro®tability than a similar percentage reduction in N use, because the ratio of N costs to gross revenue from paddy is typically 8% or less (Dawe, 2000). Reduced use of N fertilizer might also generate off-farm environmental bene®ts. Recent research conducted in different countries has demonstrated limitations of currently widespread blanket fertilizer recommendations established for large rice-growing areas. Various on-farm studies suggested large and potentially manageable variability in soil nutrient supply and crop response to nutrients among rice farms or small rice ®elds (Adhikari et al., 1999; Angus et al., 1990; Cassman et al., 1996b; Dobermann and OberthuÈr, 1997; Olk et al., 1999; Wopereis et al., 1999). It was found that the indigenous N supply was variable among ®elds and seasons and not related to soil organic matter content (Cassman et al., 1996a,b) and that optimal N rates for rice varied widely from year to year (Dawe and Moya, 1999). Plant-based strategies for real-time N management appeared therefore promising to increase yields and N use ef®ciency (Balasubramaniam et al., 2000; Peng et al., 1996a,b). Research on P and K concluded that nutrient imbalances may limit yield and N use ef®ciency and that existing soil test methods had limited applicability to lowland rice (Dobermann et al., 1996a,b,c). We, therefore, hypothesized that rice yields, pro®t, plant nutrient uptake, and N use ef®ciencies can be signi®cantly increased by applying fertilizers on a ®eld-speci®c and cropping season-speci®c basis, i.e., through more knowledge-intensive, site-speci®c approaches of nutrient management. We de®ned site-speci®c nutrient management (SSNM) as the dynamic, ®eld-speci®c management of nutrients in a particular cropping season to optimize the supply and demand of nutrients according to their differences in cycling through soil±plant systems (Dobermann and White, 1999). This form of SSNM attempts to account for: (i) regional and seasonal differences in the climatic yield potential and crop nutrient demand, (ii) between ®eld spatial variability in indigenous nutrient supply, (iii) ®eld-speci®c within-season dynamics of crop N demand, and (iv) location-speci®c cropping systems and crop management practices. It ads important regional and real-time components

39

to de®nitions of SSNM commonly used in large-scale farming, which mainly focus on managing spatial variability of nutrients within large production ®elds (Pierce and Nowak, 1999). Another, distinct characteristic of the SSNM approach developed for rice is the use of crop-based estimates of the indigenous nutrient supply instead of relying on soil tests. Spatial variability of soil nutrients and rice growth may also be large within small rice ®elds (Dobermann, 1994; Dobermann et al., 1995, 1997), but, at present, the potential for managing this variability appears limited. A multinational project on Reversing Trends of Declining Productivity in Intensive Irrigated Rice Systems (RTDP) was initiated in 1994 in key irrigated rice domains in Asia to: (1) quantify the variation in biophysical and socioeconomic characteristics of rice production, (2) monitor trends in total factor productivity at the farm level, (3) understand biophysical controls of soil nutrient supply, and (4) develop a new approach for SSNM for different production environments. The speci®c objectives of this paper are to: (a) evaluate the agronomic performance of SSNM and compare it to the current practice of fertilizer use in on-farm experiments conducted at 179 sites, (b) assess the costs and bene®ts of SSNM, and (c) draw implications for the technology adoption process. 2. Materials and methods Detailed descriptions of the various sites, experimental approaches, and other results obtained since 1994 can be found elsewhere (Dobermann et al., 2002; Moya et al., 2002; Olk et al., 1999; Wang et al., 2001; Witt et al., 1999). Here, we will only provide the information needed to discuss results of a ®rst phase of SSNM research conducted from 1996 to 1999. During an initial monitoring period, baseline data were collected to obtain understanding of major cropping practices and develop information for working out SSNM recommendations for each ®eld. This was then followed by a comparison of SSNM with the prevailing farmers' practice over four consecutive crops. 2.1. General site characteristics On-farm experiments were conducted to develop and test a new SSNM approach for eight key irrigated

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A. Dobermann et al. / Field Crops Research 74 (2002) 37±66

Table 1 Experimental sites Site Location, region, countrya

Number of farms

Cropping systemb

Crop establishmentc

Soilsd

MA Maligaya (158430 N, 1208540 E), Central Luzon, Philippines SB Suphan Buri (148280 N, 1008100 E), Central Plain, Thailand OM Omon (10870 N, 1058380 E), Mekong Delta, Vietnam SU Sukamandi (68210 N, 1078400 E), West Java, Indonesia AD Aduthurai (11810 N, 798290 E), Old Cauvery Delta, Tamil Nadu, India TH Thanjavur (108470 N, 798100 E), New Cauvery Delta, Tamil Nadu, India HA Hanoi (21810 N, 1058530 E), Red River Delta, Vietnam JI Jinhua (29850 N, 1198470 E), Jin-Qu Basin, Zhejiang, China

26

Rice (DS)±rice (WS)

DSR, TPR

24

DSR

20

Rice (DS)±rice (WS)±rice (WS) Rice (DS)±rice (WS)±rice (WS) Rice (WS)±rice (DS)

TPR

Vertisols, Inceptisols (CL to C) Alfisols, Vertisols, Ultisols (sCL to C) Entisols, Inceptisols (siC to C) Ultisols, Inceptisols (C)

25

Rice (DS)±rice (WS)±pulses

TPR

15

Rice (DS)±rice (WS)±pulses

TPR

24

Rice (ER)±rice (LR)±maize

TPR

21

Rice (ER)±rice (LR)

TPR

24

DSR

Vertisols, Inceptisols (CL to C) Alfisols (sL) Entisols, Alfisols, Ultisols (CL to siC) Entisols, Inceptisols, Alfisols (CL to siC)

a

Geographic coordinates given are those of the research station located centrally within each domain. Crops grown per year (DS: dry season, WS: wet season, ER: early/spring rice, LR: late/summer rice). Crops in italic are seasons with the highest climatic rice yield potential at each site. c Dominating crop establishment method for rice (DSR: direct-broadcast seeded rice, TPR: transplanted rice). d Dominating soil orders in the experimental domain. Common ranges of soil texture are given in parenthesis. b

rice production domains of Asia located in six countries (Table 1), each representing a large (100,000 ha) area with similar soils and cropping systems. Six domains, Maligaya (MA), Suphan Buri (SB), Omon (OM), Sukamandi (SU), Aduthurai (AD), and Thanjavur (TH), represent rice monoculture systems of the humid or subhumid tropics with annual rainfall ranging from 1000 to 1800 mm. There, rice is typically grown in two distinct seasons, a dry season with low rainfall and high solar radiation and a wet (monsoon) season with lower yield potential due to cloudy conditions and high rainfall. At Jinhua (JI), humid warm continental climate with 1300 to 1500 mm rain allows growing two rice crops from early May to October. At Hanoi (HA), rice is grown during the humidsemihot spring and summer seasons, whereas winter crops such as maize are grown during the cooler period from November to February. Annual rainfall averages 1700 mm. All domains were located in large inland plains and basins (MA, SB, SU, JI) or river deltas (OM, HA, AD, TH) with generally ¯at topography. Soil types included Entisols, Inceptisols, Al®sols, Vertisols and Ultisols (Soil Survey Staff, 1999). At all sites, irrigated rice has been grown for a long time

and early adoption of intensive cropping had occurred. At least two crops of rice are grown annually, but cropping intensities of three crops per year were common in three domains (OM, HA, and SB). At HA, rice cultivation dates back several thousand years and this domain probably represents some of the most intensively used riceland in Asia. Surface water from rivers or reservoirs was the main source of irrigation at most sites. In each domain, on-farm experiments were conducted in 15 to 26 farms located within a radius of typically 15 to 25 km around a research station, resulting in a total number of 179 farms. Farms were typically clustered into several villages and selected to represent different socioeconomic conditions and farm sizes and the most common soil types, cropping systems, and farm management practices in the region. Farm sizes, varieties, cropping technologies, and input use varied widely among sites, but also among farms within each site (Moya et al., 2002). Average farm sizes ranged from 0.3 ha in North Vietnam (HA) to more than 4 ha in Tamil Nadu (AD, TH) and Thailand (SB). At each farm, a single rice ®eld served as the principal experimental unit.

A. Dobermann et al. / Field Crops Research 74 (2002) 37±66

2.2. Experimental details All experiments followed a standardized experimental protocol with minor location-speci®c modi®cations to account for differences in climatic seasons and crop establishment techniques (Dobermann et al., 2002). Treatments in the on-farm experiments included. 2.2.1. Farmers' fertilizer practice (FFP) (1994/1997±1999) A single rice ®eld (0.1 to >1 ha) in which all crop and fertilizer management was done by the farmer. Detailed biophysical and socioeconomic monitoring of FFP ®elds started in 1994 at AD, OM, MA, SB, and SU and in 1997 at HA, JI, and TH. 2.2.2. Nitrogen omission plot (0-N) (1994/1997±1999) Strip plots (40±100 m2) or two 6 m  6 m plots embedded in the FFP, to which either no fertilizer (AD, MA, OM, SB, SU, TH) or 30 and 50 kg K ha 1 (HA, JI) was applied. This treatment was included in all crops grown (at least six consecutive crops at each site) and used to estimate: (a) N use ef®ciencies using the difference method and (b) the indigenous N supply used as input parameter for SSNM (see below). Research conducted during 1994±1996 demonstrated that differences in grain yield and N uptake between plots with no fertilizer and plots to which only P and K was applied were negligible so that either one could be used to estimate (a) and (b) at the study sites. 2.2.3. Phosphorus omission plot (0-P) (1997±1998) Strip plots (40±100 m2) or two 6 m  6 m plots embedded in the FFP, to which only N and K were applied to ensure that macronutrients other than P did not limit plant P uptake from indigenous sources. Depending on the site and climatic season, the N rates varied from 120 to 180 kg N ha 1; K rates varied from 100 to 150 kg K ha 1. The 0-P plots were used to estimate the indigenous P supply for working out ®eldspeci®c fertilizer recommendations. This treatment was discontinued after four successive crops had been grown from 1997 to 1998. 2.2.4. Potassium omission plot (0-K) (1997±1998) Strip plots (40±100 m2) or two 6 m  6 m plots embedded in the FFP, to which only N and P were

41

applied to ensure that macronutrients other than K did not limit plant K uptake from indigenous sources. Depending on the site and climatic season, the N rates varied from 120 to 180 kg N ha 1; P rates varied from 25 to 40 kg P ha 1. The 0-K plots were used to estimate the indigenous K supply for working out ®eldspeci®c fertilizer recommendations. This treatment was discontinued after four successive crops had been grown from 1997 to 1998. 2.2.5. SSNM (1997±1999) A larger plot (300±1000 m2) permanently located within the farmer's ®eld (FFP), in which nutrient applications were prescribed on a ®eld- and cropspeci®c basis following the SSNM approach described below. This treatment started in 1997 (AD, MA, OM, SB, SU) or 1998 (HA, JI, TH) and continued for four successive rice crops at each site. The 0-N, 0-P, and 0-K plots were separated from the surrounding ®eld by bunds and moved to a different location after each crop to avoid residual effects due to nutrient depletion. The SSNM plot remained at the same location. Each treatment contained two replicate sampling plots per farm. Depending on initial soil test data, blanket doses of other nutrients were applied to all treatments at selected sites to prevent de®ciencies other than N, P, or K. This included application of Zn at MA, AD, and TH and a single Mg application at some HA sites. Varieties grown were the same in all treatments and chosen by the farmer. In general, only, high-yielding inbred rice varieties with a harvest index of 0.45±0.5 were grown. The only exception was the JI site in China, where most farmers grew hybrid rices during the late rice season (Wang et al., 2001). Transplanting with hill densities ranging from about 20 to 50 hills m2 was the predominant form of crop establishment at ®ve sites (AD, HA, JI, SU, TH), whereas most farmers at MA, OM, and SB used direct broadcast seeding with high seed rates (typically 100± 200 kg ha 1). Farmers did all water management and pest control in both FFP and SSNM plots following the commonly adopted methods. Only where problems were suspected or observed, measures to either control them in advance (prophylactic) or correct them were implemented under the guidance of the researchers. The main principle was to minimize possible negative effects of factors other than nutrients so that the

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A. Dobermann et al. / Field Crops Research 74 (2002) 37±66

performance of SSNM could be assessed properly. However, these measures did not signi®cantly confound the comparison between SSNM and FFP treatments because they affected both treatments similarly. Where applicable, deviations from this are discussed below. At all sites, crops were generally grown with good irrigation water supply. 2.3. SSNM Details of the SSNM approach used are provided elsewhere (Dobermann et al., 2002; Dobermann and White, 1999; Witt et al., 1999). The SSNM approach focused on predicting ®eld-speci®c fertilizer rates and implementation of a site-speci®c N management scheme that accounted for real-time variation in crop N demand at major growth stages of rice. Newly collected information was applied in succeeding rice crops to gradually ®ne-tune the SSNM to local conditions. We used a modi®cation of the QUEFTS model (Janssen et al., 1990) to work out ®eld-speci®c NPK recommendations for each site at the beginning of each season. Information needed to estimate the optimal amount of N, P, and K included: (1) climatic yield potential, (2) yield goal, (3) de®nition of the relationship between grain yield and plant nutrient accumulation, (4) ®eld-speci®c estimates of the indigenous N, P, and K supplies, and (5) estimated recovery ef®ciencies of fertilizer N, P, and K. The potential yield was de®ned as grain yield limited by climate and genotype only, with all other factors not limiting crop growth. Typically, in the subhumid to humid subtropical and tropical regions of Asia the potential yield is around 9±10 t ha 1 in the high-yielding season (HYS) and 6±8 t ha 1 in the lowyielding season (LYS), when solar radiation is lower due to greater cloud cover. Estimates of the potential yield for each site and climatic season were obtained from previously conducted crop modeling studies (Matthews et al., 1995; Zheng et al., 1997) or recorded maximum yields. Across all domains and seasons, potential yield estimates ranged from 6.5 t ha 1 (SU dry season) to 10.5 t ha 1 (MA dry season). Seasonspeci®c yield goals were set in the range of 70±80% of the potential yield because beyond that level internal ef®ciencies of nutrients in the plant decline (Witt et al., 1999). Practical experience indicates that yields of about 80% of the potential yield seem to represent a

ceiling for what can be achieved by the best farmers under ®eld conditions (Cassman and Harwood, 1995). Typically, yield goals within a season and domain varied little among farms (CV 2±4%, range <1 t ha 1). However, in ®elds with low indigenous supply of one or more nutrients, the yield goal was lowered to ®rst slowly build up soil fertility and raise the yield goal over time. The relationship between grain yield and nutrient accumulation was described as a function of the climatic yield potential and the supply of N, P, and K (Janssen et al., 1990), using the boundary lines describing the maximum accumulation and maximum dilution of N, P, and K in rice (Witt et al., 1999). Optimal crop nutrients requirements assumed a linear relation between grain yield and nutrient uptake at yield levels below 70±80% of the potential yield because nutrient uptake is at its maximum under conditions of limited nutrient supply. When yields approach the potential yield, the internal ef®ciency of a nutrient (kg grain per kg plant nutrient accumulation) decreases. The potential supplies of N, P, and K from soil and other indigenous sources (Janssen et al., 1990) were estimated for each ®eld as total plant N accumulation at maturity in 0-N plots (INS), total plant P accumulation at maturity in 0-P plots (IPS), and total plant K accumulation at maturity in 0-K plots (IKS). In irrigated rice systems, this de®nition of indigenous supply mainly includes nutrient contributions from soil, decomposing crop residues, asymbiotic biological N2 ®xation, irrigation water, and rainfall. Values of INS, IPS, and IKS for each SSNM crop grown were regularly adjusted by: (a) incorporating new data collected from the previous crop omission plots, and (b) estimating the P and K input±output balance of the previous crop. The latter provided small adjustments of IPS and IKS in cases where the actual yield was much below the yield goal so that signi®cant amounts of residual fertilizers remained in the soil. Adjusted INS, IPS, and IKS values were then used as model input for making the ®eld-speci®c fertilizer recommendation for the subsequently grown rice crop. First-crop recovery fractions of fertilizer N, P and K used in the fertilizer rate calculations ranged from 0.4 to 0.6 kg kg±1 (N), 0.2 to 0.3 kg kg±1 (P), and 0.4 to 0.5 kg kg±1 (K). These numbers were based on values measured at each site using the difference

A. Dobermann et al. / Field Crops Research 74 (2002) 37±66

method during the initial crops grown from 1994 to 1996 (AD, MA, OM, SB, SU) or in 1997 (HA, JI, TH) as well as data obtained from accompanying longterm experiments. For N, we mostly assumed recovery fractions of 0.5 kg kg 1 because this level is achievable with a plant-based N management strategy. Estimates for P and K varied slightly among sites to account for major variations in soil types and cropping practices. A linear optimization procedure was used to ®nd the best combination of N, P, and K fertilizer rates to achieve the yield goal under the constraint of optimizing the internal ef®ciencies of N, P, and K in the plant. The model was constrained to arrive at a solution close to the situation of most balanced nutrition, i.e., where the ratio between predicted uptake and potential supply of each macronutrient was close to 0.95 (Janssen, 1998). Other constraints included upper and lower possible limits of fertilizer rates. In the case of N, an upper limit (e.g., typically 180±200 kg N ha 1 in a dry season) was set to avoid excessive N rates that could cause pest problems or lodging. Upper limits of 40±45 kg P ha 1 and 150±200 kg K ha 1 were set so that fertilizer rates applied remained within economically reasonable ranges. In cases where the model did not arrive at an acceptable solution within these limits, the yield goal was reduced until an optimal solution was found. This approach follows the concept that crop yields on soils with moderate to high P or K status cannot be fully matched on comparable soils of low P or K status even when large amounts of fertilizers are applied (Johnston et al., 1986). Lower limits of 10 kg P ha 1 and 30 kg K ha 1 were set as the minimum amount to be applied to replenish the net removal from the ®eld. Fertilizer sources used in the SSNM treatments at all sites were urea, single or triple superphosphate, and muriate of potash. All P fertilizer and 50% of the total K rate were incorporated into the soil before transplanting or surface-broadcast within 14 days after sowing at sites with direct seeding. The remaining 50% K was applied at panicle initiation (PI) stage of rice. Because actual growth conditions may signi®cantly deviate from average conditions assumed in an empirical fertilizer recommendation model, in-season adjustment of N was done to further increase N use ef®ciency. Strategies for splitting and timing of N applications differed among sites to account for major

43

differences in: (1) climatic seasons, (2) varieties and growth duration, (3) crop establishment methods, (4) water management, and (5) possible pest problems (Dobermann and Fairhurst, 2000). Pre-plant N was applied in few cases, most noticeably at all sites in China where hybrid rice was grown or in the early rice crop in North Vietnam. At other sites, pre-plant N was only applied on soils with very low INS (typically if INS was less than 40 kg N ha 1). A chlorophyll meter (SPAD 502, Minolta, Ramsey, NJ) was used for making decisions on top-dressed N applications, but the decision criteria were empirically changed over time based on the experience accumulated in the previous cropping seasons and other ®eld research conducted at each site. Table 2 shows two examples of the ®nal site-speci®c N management strategies. At JI, SSNM in the ®rst two crops (1998) followed existing best N management practices. Nitrogen was applied in ®xed split applications at pre-set growth stages (40% incorporated before transplanting, 20% top-dressed at 7±14

Table 2 Examples of site-speci®c N management strategies at two sites N application

Growth stage

N rate (kg N ha 1)

JI, China (transplanted rice), 1999 early and late rice crops N1 Pre-plant 40% of model-predicted N rate 20% of model-predicted N rate N2 7±14 DATa N3 35±45 DAT (PI) 30 (if SPAD >36) 40 (if SPAD 33±36) 50 (if SPAD <33) N4 50±55 DAT 0 (if SPAD >36) (heading) 20 (if SPAD <36) OM, Vietnam (direct-seeded rice), 1999 dry season 30 N1 10 DASb N2 20±25 DAS 0 (if SPAD >37) (tillering) 20 (if SPAD 35±37) 30 (if SPAD 33±35) 40 (if SPAD <33) N3 40±45 DAS (PI) 40 (if SPAD >35) 50 (if SPAD 33±35) 60 (if SPAD <33) N4 60 DAS (heading 0 (if SPAD >33) to first flowering) 20 (if SPAD <33) a b

Days after transplanting. Days after sowing.

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A. Dobermann et al. / Field Crops Research 74 (2002) 37±66

DAT, and 40% top-dressed at PI stage. Weekly, chlorophyll meter readings were collected during this period to gain understanding about location-speci®c SPAD ranges. Beginning in 1999, plant N status was monitored at pre-set critical growth stages at which N must be applied, but the amount of N was varied based on the actual SPAD value (Table 2). This strategy accounted for ®eld-speci®c variation in INS during early growth and variation in late-season N demand depending on the actual growth conditions. Late N at 55 DAT was only applied in cases with good crop stand to support the extra yield potential by adding more N for grain ®lling (Perez et al., 1996). For comparison, farmers in the JI domain typically applied all N in two splits of about 40% pre-plant and 60% within 7±10 DAT and only few applied a third dose at later growth stages (Wang et al., 2001). At OM, the ®nal sitespeci®c N management strategy was tailored to the needs of direct-seeded rice and included no pre-plant application. Instead, a uniform initial N dose was broadcast on the soil surface within the ®rst 2 weeks after sowing, shortly before the ®elds became permanently ¯ooded. From then on, decisions about two to three more top-dressed applications were based on SPAD readings using lower thresholds than those used for transplanted rice (Balasubramaniam et al., 2000; Dobermann and Fairhurst, 2000). 2.4. Measurements Initial soil samples for determination of general soil properties in 0±0.15 m depth were collected in spring 1997 and analyzed following standard procedures (van Reeuwijk, 1992). Plant sampling procedures followed a standard protocol at all experimental sites (Dobermann and Fairhurst, 2000). Two 6 m  6 m sampling areas were randomly selected in each treatment for replicated plant sampling. A 12-hill plant sample (or two 0.5 m2 samples in direct-seeded rice) was collected at physiological maturity for determination of yield components, harvest index, and nutrient concentrations in plant tissue. Nitrogen concentrations in grain and straw were measured by micro-Kjeldahl digestion, distillation, and titration (Bremner and Mulvaney, 1982), tissue-P by the molybdenum-blue colorimetric method, and tissue-K by atomic adsorption spectrophotometer after wet digestion (Walinga et al., 1995). Grain yields were obtained from a central

5 m2 harvest area in each sampling plot at harvestable maturity, and are reported at a standard moisture content of 0.14 g H2O g 1 fresh weight. Grain and straw sub-samples from the 12-hill sample were dried to constant weight at 70 8C. Straw yields were estimated from the oven-dry grain yield of the 5 m2 harvest area, and the grain to straw ratio of the 12-hill sample. Other measurements included chlorophyll meter readings of the uppermost fully expanded leaf in the SSNM and FFP treatments. Beginning at 20 DAT, 20 leaf readings per plot were averaged and measurements continued in 7±10 days intervals until about 10 days after ¯owering. Crop management quality was scored subjectively for each rice crop grown at each site. Five categories, namely: (a) land preparation, (b) water supply and management, (c) occurrence of weeds, rats, or snails, (d) occurrence of insect pests or diseases, and (e) other problems (lodging, seed quality, typhoons), were scored in categories of 0 (very good, no problem), 1 (moderate, some problems), and 2 (poor, severe problems). Using the ®eld scores given for the different crop management factors, an overall score was assigned to each farm  crop data set. The average score (S) was computed as the average of the ®ve individual scores (a±e, above) and compared with the maximum score of any individual factor (Sm). Based on this, an overall crop management quality score (CM) was then de®ned as CM ˆ 0 (high) CM ˆ 1 (medium) CM ˆ 2 (low)

if S < 0:5 and Sm < 2; if 0:5
S  1:0 and Sm < 2; if S
 1:0 or Sm ˆ 2.

2.5. Data analysis Nitrogen use ef®ciencies were estimated using the differences between N-fertilized treatments and the 0-N plots, as described by Cassman et al. (1998). Terms used are AEN: agronomic ef®ciency of applied N (kg grain yield increase per kg N applied), REN: apparent recovery ef®ciency of applied N (kg N taken up per kg N applied), PEN: physiological ef®ciency of N (kg grain yield increase over 0-N per kg increase in plant N accumulation over 0-N), and PFPN: partial factor productivity of applied N (kg grain per kg N applied).

A. Dobermann et al. / Field Crops Research 74 (2002) 37±66

Economic calculations were made on a per crop basis using US$ as standard currency TFC ˆ PN FN ‡ PP FP ‡ PK FK GRF ˆ PR YR

(1)

TFC

(2) 1

where TFC is the total fertilizer cost ($ ha ), GRF the gross return above fertilizer cost ($ ha 1), YR the rice yield (kg ha 1), PR the price of rice ($0.15 kg 1 paddy), PN the price of N fertilizer ($0.35 kg 1 N), PP the price of P fertilizer ($1.10 kg 1 P), and PK the price of K fertilizer ($0.30 kg 1 K). All prices used were average regional retail prices surveyed at all sites from 1997 to 1999. The use of average regional prices allows to focus on differences across sites that are due to the technology itself. National policies affecting rice prices and input prices also affect the pro®tability of a new technology, and both factors are in principle important for technology adoption. In order to focus on the technology itself (SSNM), however, it is better to avoid a discussion of idiosyncratic national pricing policies, especially since the effect of different national policies on pro®tability is not large. Because data on land rental costs were not easily available and because of the dif®culties in imputing costs to family labor, it was not possible to calculate the absolute level of pro®t with and without SSNM. This is not a major drawback, since the absolute level of pro®ts is less important than the change in pro®ts due to adoption of the technology. The incremental pro®tability of SSNM (DGRF, which can be positive or negative) was therefore measured as the difference in gross returns above fertilizer costs in the two treatments (SSNM and FFP): DGRF ˆ GRFSSNM

GRFFFP

(3)

Pairwise treatment differences between SSNM and FFP measured during 1997±1999 were subjected to analysis of variance (ANOVA). PROC GLM of SAS (SAS, 1988) was used to perform ANOVA on the differences between SSNM and FFP …D ˆ SSNM FFP† measured at each farm for four consecutive rice crops grown. For testing the hypothesis that the performance of SSNM improved with time we divided the data set into year 1 (ˆ crops 1 and 2) and year 2 (ˆcrops 3 and 4). For testing the hypothesis that the performance of SSNM differed between seasons with different climatic yield potential, the two main rice

45

seasons at each site were classi®ed as either highyielding seasons (HYS) or low-yielding seasons (LYS). At the tropical sites, the HYS is usually the dry season (DS) and the LYS is the wet season (WS). SU was the only exception to this rule because DS yields at this site tend to be lower than WS yields. For the two subtropical sites, the HYS is the late rice crop in Zhejiang and the early (spring) rice crop in northern Vietnam. A ®xed-effects ANOVA model was used to analyze the data Site (S) Farm within site (F) Crop (C) Year 1 vs. year 2 HYS vs. LYS Year  season Site  crop …S  C† Residual

d.f. d.f.

7 171

d.f. d.f. d.f. d.f. d.f.

1 1 1 21 514

Crop was partitioned into three orthogonal components (i.e., year, season and year  season interaction), which allows for the speci®c testing of year or season main effects and the year  season interaction effect. All effects, except site were tested against the residual. Site effect was tested against farm within site as error term. For variables with missing observations, the denominator mean square was adjusted using the Satterthwaite approximation (Satterthwaite, 1946). Stepwise-forward multiple regression analysis was conducted to explore the contribution of crop management quality, indigenous nutrient supplies, fertilizer rates, number of N applications, and plant nutrient accumulation to yield increases achieved by SSNM. All regressions were based on averages over four consecutive SSNM and FFP crops for a speci®c farmer (ˆ179 data sets). All regression equations shown only include independent variables with signi®cant …P < 0:01† effects on the dependent variable of interest. 2.6. Speci®c considerations for evaluating the performance of SSNM Our studies did not include an unaffected control group of farmers so that there is some uncertainty to what extent the statistical comparison of SSNM with FFP may be biased towards ®nding smaller

46

A. Dobermann et al. / Field Crops Research 74 (2002) 37±66

differences. Imperfect control over ®eld management and competition are common problems that may cause bias in conducting on-farm experiments. Farmers may partially copy improved practices such as SSNM in their own FFP plots and they may not report such changes in their management. For example, inaccuracy of weights reported and non-uniform distribution of fertilizer within different parcels managed by the same farmer may cause some uncertainties about rates of N, P, and K applied by the farmers. Although, there was evidence for sporadic changes in fertilizer rates and timing of N applications, the large sample size of 179 farms ensured that the overall effect of such potential bias in the FFP on the comparison of treatment means was small. For each site, FFP data sets for two to four consecutive crops were available for the period before (baseline data) and after establishing an SSNM plot. Therefore, as a cross-check for potential changes in farmers' management, we also compared the performance of SSNM with the FFP during both periods (Fig. 1). Attributing meaning to the calculation of DGRF (Eq. (3)) also assumes that the only difference in crop management between SSNM and FFP is different quantities of nutrients and different timing of a certain constant number of applications, so that all other management practices and quantities of input use are held constant. Generally, speaking, this assumption holds, but there are three exceptions worth noting. First, across all sites and crops, the average number of fertilizer applications per crop in the SSNM treatment was 3.8, compared to 2.7 in the FFP …P < 0:01†. In theory, it should be possible to attribute a labor cost to the time spent in applying an extra dose of fertilizer and incorporate this ®gure into the pro®tability calculation. This ®gure is small relative to the pro®tability of SSNM, however, since it takes only about 6 h ha 1 to apply a dose of fertilizer at most sites. No explicit correction was made for this effect. The second exception concerns plant spacing at JI for the late rice crop in 1999. Plant density was 19 hills m 2 in the FFP treatment, but 22 hills m 2 in the SSNM treatment, a difference of 18% that implies a commensurate percentage increase in seed costs and labor costs for transplanting that amount to approximately US$ 25 ha 1. This compares to a DGRF of US$ 120 ha 1 for this particular crop at this site. Because pro®tability for this speci®c crop is still substantial even after accounting for this cost, and

because the average pro®tability across all 32 crops at eight sites is affected only slightly, this cost adjustment is not incorporated into the estimates reported below. The third important exception was unique to the HA site where substantially more farmyard manure was applied to the FFP treatments (on average 12 t ha 1 per crop) than to the SSNM treatments (on average 8.5 t ha 1 per crop). It is dif®cult to adjust the pro®tability calculations for this effect, however. The ®nancial cost of obtaining manure in this area is zero, since it is obtained on-farm. The opportunity cost of manure is also zero since there is no market on which farmers can sell it. Application of manure utilized on average nearly 20 person-days ha 1 per crop, compared to total labor use of 260 person-days ha 1. This represents nearly 8% of the total labor use at those sites, but the opportunity cost of labor is probably close to zero since the manure is applied using family labor. Thus, no adjustment was made for the additional manure used in the FFP plots in northern Vietnam. If an adjustment were made, it would increase the pro®tability of SSNM relative to FFP. The ®nancial pro®tability calculations also implicitly assume that the FFP treatment is representative of farmers' practices in the area. This was generally true, but not always. For example, at MA and OM, certi®ed seed was used in both SSNM and FFP plots based on the judgment of researchers that seed quality was a problem in these areas. Farmers in these areas typically do not use certi®ed seed, primarily because the reliability of the current seed marketing systems is suspect. While there is no strong evidence that SSNM performs substantially better with certi®ed seed, future trials of SSNM should take more consideration of farmers' ability to implement the recommended strategy. Nutrient omission plots (0-N, 0-P, 0-K) were also used in farmers' ®elds to gather data for the implementation of SSNM. The combined size of these plots for an individual ®eld was small, however (typically 0.01± 0.02 ha), and would not be a major constraint on adoption in terms of foregone yield. Construction of these plots may, however, be a burden in terms of complexity. 2.7. Performance of SSNM across all sites 2.7.1. Spatial variation in soil nutrient supply Initial soil analysis indicated large variation in soil fertility characteristics among the eight intensive rice

A. Dobermann et al. / Field Crops Research 74 (2002) 37±66

47

Fig. 1. Grain yield, plant nutrient accumulation, and N use ef®ciency in the FFP and SSNM plots at eight sites (means and standard errors of 179 ®elds). Baseline data refer to two successive rice crops monitored before intervention (1995±1997), crops 1±4 show the four rice crops grown thereafter (1997±1999).

domains as well as among farms within each domain (Table 3). For example, site averages of extractable K ranged from 0.16 cmolc kg 1 on intensively cropped alluvial and degraded soils at HA to 0.58 cmolc kg 1 on mostly Vertisols at AD. Coef®cients of variation

(CV) within a domain were generally largest for extractable K (range of site CVs 13±58%) and extractable P (range of site CVs 16±66%). Two- to three-fold ranges of INS, IPS and IKS among ®elds were found within all domains, with CVs mostly in the 15±20%

48

A. Dobermann et al. / Field Crops Research 74 (2002) 37±66

Table 3 Variation of initial soil fertility characteristics and potential indigenous nutrient supplies at eight sites in Asiaa Site

Organic C (g kg 1)

pHb

CECc (cmolc kg 1)

Extractable Kd (cmolc kg 1)

Extractable Pe (mg kg 1)

INSf (kg ha per crop)

MA SB OM SU AD TH HA JI

12.7 16.9 18.1 12.5 10.0 7.2 14.7 19.1

6.2 5.4 4.9 5.3 7.2 7.1 4.9 5.4

25.4 19.9 17.4 27.8 31.0 9.1 8.2 11.1

0.22 0.34 0.24 0.41 0.58 0.27 0.16 0.26

26.3 25.3 2.5 6.3 26.3 31.5 9.6 21.4

66 73 50 67 58 44 64 83

(17) (21) (21) (28) (14) (9) (34) (10)

(5) (7) (4) (10) (5) (10) (5) (8)

(24) (40) (13) (38) (12) (26) (41) (24)

(42) (31) (19) (54) (13) (21) (33) (58)

(25) (33) (41)g (53) (25) (16) (41) (66)

(18) (19) (12) (32) (19) (23) (15) (15)

1

IPSf (kg ha per crop) 16 16 11 14 19 19 17 24

(19) (22) (19) (19) (16) (15) (16) (12)

1

IKSf (kg ha per crop) 89 74 82 109 95 66 82 118

1

(20) (25) (21) (14) (16) (15) (16) (12)

a

Values shown are site means and coef®cients of variation (CV (%), in parenthesis) among 15±26 rice ®elds within each domain. pH in 1:1 H2O. c Cation exchange capacity. d 1 N NH4-acetate extractable K at pH 7. e 0.5 M NaHCO3 extractable P (Olsen-P). f Potential indigenous supplies of N (INS), P (IPS), and K (IKS). Values shown are based on ®eld averages of the largest two measurements of plant nutrient accumulation in omission plots during three to four successive rice crops grown in each ®eld from 1997 to 1998 to re¯ect the average potential supply under favorable growth conditions. g Bray P was used at OM. b

range (Table 3). At most sites, rice yield without applying N ranged from <2 to >6 Mg ha 1. Similar ranges were found in other studies (Bouldin, 1986; Cassman et al., 1996b; Wopereis et al., 1999). Across all 179 ®elds, median potential indigenous supplies were 64 kg N, 17 kg P, and 90 kg K ha 1 per crop. Average grain yields in nutrient omission plots increased in the order 0-N (4.54) < 0-K (5.66) < 0-P (5.76 Mg ha 1). These data con®rm that N de®ciency is a general feature of irrigated rice systems, whereas P and K supply are equally limiting factors in some parts of irrigated rice areas in south and southeast Asia. Depletion of the median IKS to levels below present average yields of irrigated rice has not yet occurred widely, but, considering the widespread negative K input±output balances in these systems (Dobermann et al., 1998), this appears to be only a matter of time. Although, our data cannot provide a conclusive historical comparison, we hypothesize that the variation in soil nutrient supply among Asian rice domains and within each domain has mostly accrued from small differences in input use and yields during the past few decades of intensive cropping. Of the eight domains studied, JI in southeast China had by far the largest indigenous supplies of all three macronutrients (Table 3). This re¯ects a past history of farmyard and

green manure use at this site as well as high rates of P and K fertilizer use promoted in recent years (Wang et al., 2001). In contrast, IKS was low on soils with similar inherent fertility potential at SB in Thailand, where most farmers have not applied any K during the past 30 years. Management-induced spatial variation among small rice®elds presents new challenges for nutrient management because it appears to be less related to permanent soil or landscape features (Dobermann and OberthuÈr, 1997; OberthuÈr et al., 1996), thereby limiting the usefulness of broader fertilizer recommendations. 2.7.2. Grain yield and nutrient uptake Across all sites and crops, average grain yields in the SSNM increased by 0.36 Mg ha 1 (7%, P < 0:001) as compared to the FFP measured in the same year (Table 4) or 0.54 Mg ha 1 (11%) compared to the baseline FFP yield before intervention (Fig. 1). Yield increases over FFP were signi®cant in all crops grown after the ®rst crop (Fig. 1) and the yield difference increased from 0.31 (year 1) to 0.41 Mg ha 1 (year 2) (crop-year effect, P ˆ 0:016). This is evidence for an improvement in the performance of SSNM over time as the concept and its practical application improved. Yield increases were similar in HYS (0.39 Mg ha 1) and LYS (0.34 Mg ha 1; crop-season effect n.s.).

A. Dobermann et al. / Field Crops Research 74 (2002) 37±66

49

Table 4 Performance of SSNM in irrigated rice ®elds of Asia (means of 179 farm sites, 1997±1999) Levelsa

Treatmentb SSNM

1

Dc

P > jtjc

Effectsd

P > jFjd

<0.001 0.002 <0.001 <0.001 0.001

Site Crop-year Crop-season Crop-year  season Site  crop

<0.001 0.016 0.466 0.217 <0.001

FFP

Grain yield (Mg ha )

All Year 1 Year 2 HYS LYS

Plant N uptake (kg ha 1)

All Year 1 Year 2 HYS LYS

99.2 99.2 99.0 106.4 91.9

89.8 91.2 88.3 97.1 82.4

9.4 8.0 10.7 9.3 9.5

<0.001 <0.001 <0.001 <0.001 <0.001

Site Crop-year Crop-season Crop-year  season Site  crop

<0.001 0.029 0.854 0.596 <0.001

Plant P uptake (kg ha 1)

All Year 1 Year 2 HYS LYS

19.4 19.8 19.1 20.7 18.1

17.4 18.0 16.9 19.0 15.7

2.0 1.8 2.2 1.7 2.4

<0.001 <0.001 <0.001 <0.001 <0.001

Site Crop-year Crop-season Crop-year  season Site  crop

<0.001 0.002 <0.001 <0.001 <0.001

Plant K uptake (kg ha 1)

All Year 1 Year 2 HYS LYS

102.4 100.8 103.7 110.6 94.0

94.0 93.7 94.2 102.0 85.9

8.4 7.1 9.5 8.6 8.1

<0.001 0.006 <0.001 <0.001 <0.001

Site Crop-year Crop-season Crop-year  season Site  crop

<0.001 0.092 0.801 0.807 0.611

N Fertilizer (kg ha 1)

All Year 1 Year 2 HYS LYS

112.3 115.1 109.5 116.0 108.6

116.9 116.8 116.9 121.7 112.1

4.6 1.7 7.4 5.7 3.5

0.013 0.520 0.004 0.025 0.182

Site Crop-year Crop-season Crop-year  season Site  crop

<0.001 0.017 0.298 0.857 <0.001

P fertilizer (kg ha 1)

All Year 1 Year 2 HYS LYS

19.3 22.3 16.4 20.7 17.9

17.9 17.6 18.3 18.5 17.3

1.4 4.7 1.9 2.2 0.6

0.015 <0.001 0.009 0.005 0.497

Site Crop-year Crop-season Crop-year  season Site  crop

<0.001 <0.001 0.148 0.180 <0.001

K fertilizer (kg ha 1)

All Year 1 Year 2 HYS LYS

58.2 65.6 51.0 59.9 56.5

30.5 30.5 30.5 33.1 27.9

27.7 35.1 20.5 26.8 28.6

<0.001 <0.001 <0.001 <0.001 <0.001

Site Crop-year Crop-season Crop-year  season Site  crop

<0.001 <0.001 0.035 <0.001 <0.001

Agronomic efficiency of N (kg grain kg N 1)

All Year 1 Year 2 HYS LYS

14.8 14.6 15.0 16.2 13.4

11.5 11.8 11.2 12.7 10.3

3.3 2.8 3.8 3.5 3.1

<0.001 <0.001 <0.001 <0.001 <0.001

Site Crop-year Crop-season Crop-year  season Site  crop

0.003 0.063 0.552 0.217 <0.001

Recovery efficiency of N (kg N kg N 1)

All Year 1 Year 2 HYS LYS

0.09 0.07 0.11 0.08 0.09

<0.001 <0.001 <0.001 <0.001 <0.001

Site Crop-year Crop-season Crop-year  season Site  crop

<0.001 0.028 0.975 0.940 <0.001

5.54 5.56 5.53 6.02 5.08

0.40 0.40 0.40 0.44 0.37

5.18 5.25 5.12 5.63 4.74

0.31 0.33 0.29 0.36 0.28

0.36 0.31 0.41 0.39 0.34

50

A. Dobermann et al. / Field Crops Research 74 (2002) 37±66

Table 4 (Continued ) Levelsa

Treatmentb SSNM

Dc

P > jtjc

Effectsd

P > jFjd

FFP

Physiological efficiency of N (kg grain kg N 1)

All Year 1 Year 2 HYS LYS

37.2 35.9 38.6 37.6 36.9

36.3 35.6 37.2 36.1 36.6

0.9 0.3 1.4 1.5 0.3

0.320 0.804 0.247 0.226 0.809

Site Crop-year Crop-season Crop-year  season Site  crop

0.028 0.614 0.778 0.015 0.134

Partial productivity of N (kg grain kg N 1)

All Year 1 Year 2 HYS LYS

52.2 51.8 52.6 54.5 49.8

49.2 49.2 49.3 50.7 47.6

3.0 2.6 3.3 3.8 2.2

0.006 0.109 0.022 0.004 0.207

Site Crop-year Crop-season Crop-year  season Site  crop

<0.001 0.679 0.535 0.166 <0.001

Total fertilizer costs (US$ ha 1 per crop)

All Year 1 Year 2 HYS LYS

78.0 84.5 71.7 81.3 74.7

69.8 69.4 70.3 72.8 66.7

8.2 15.1 1.5 8.5 8.0

<0.001 <0.001 <0.367 <0.001 <0.001

Site Crop-year Crop-season Crop-year  season Site  crop

0.001 0.001 0.636 0.018 <0.001

Gross returns above fertilizer costs (US$ ha 1 per crop)

All Year 1 Year 2 HYS LYS

46.2 31.6 60.6 49.9 42.5

<0.001 0.032 <0.001 <0.001 0.005

Site crop-year Crop-season Crop-year  season Site  crop

<0.001 <0.001 0.404 0.523 <0.001

754 750 758 821 686

708 718 697 771 644

a All: all four successive rice crops grown from 1997 to 1999; year 1: crops 1 and 2; year 2: crops 3 and 4; HYS: high yielding season; LYS: low-yielding season. b FFP: farmers' fertilizer practice; SSNM: site-speci®c nutrient management. c D ˆ SSNM FFP; P > jtj: probability of a signi®cant mean difference between SSNM and FFP. d Source of variation of ANOVA of the difference between SSNM and FFP by site; P > jFj: probability of a signi®cant F-value. DF: site 7, crop-year 1, crop-season 1, crop-year±season 1, site±crop 21.

The probability of occurrence of a yield increase due to SSNM was 73% (Fig. 2) with no difference between HYS and LYS crops. Yield increases of >1 Mg ha 1 were observed in 13% of all cases. In 1996±1997, only 22% of the farmers achieved yields greater than 6 Mg ha 1 in the FFP plots. This proportion increased to 42% in the SSNM plots in 1997±1999 (27% in FFP). The proportion of yields >8 Mg ha 1 increased to 12% as compared to 2% in the baseline data. In 41 out of the 179 farms, average actual yields of four successive rice crops grown were within 10% of the yield goal. Increases in grain yield were mainly associated with an increase in the sink size. Compared to the FFP in the same year, the average number of spikelets per square meter increased by 6.5%, whereas differences in harvest index and other yield components were not signi®cant (data not shown). The average straw yield increased by 0.28 Mg ha 1 (6%) compared to the FFP in the same season.

Increases in total plant accumulation of N, P, and K were larger than increases in grain yield. Average nutrient uptake under SSNM increased by 9±11% compared to the FFP in the same years (Table 4) or by 13% (N) and 21% (P, K) compared to the baseline FFP data collected before the start of an SSNM plot (Fig. 1). Increases in plant uptake were signi®cant in most crops grown (Fig. 1) and also signi®cantly improved from year 1 to year 2 for all three nutrients. The latter is particularly remarkable because rates of N, P, and K fertilizer declined from year 1 to year 2. Only in the case of P was the increase in plant uptake in HYS larger than in LYS (crop-season effect, P < 0:001). 2.7.3. Fertilizer use SSNM and FFP treatments differed in the amounts of N, P, and K applied. On average, 4.6 kg ha 1 less N was applied in SSNM plots than in FFP (4% less,

A. Dobermann et al. / Field Crops Research 74 (2002) 37±66

51

the SSNM (58 kg K ha 1 per crop) were generally about 90% larger than in the FFP (31 kg K ha 1 per crop). However, the difference between the two treatments declined from 35 (year 1) to 21 kg K ha 1 (year 2) (crop-year effect, P  0:001). Greater P and K rates in the ®rst SSNM year were mainly caused by insuf®cient levels of IPS and IKS at many sites. The decreasing rates in the second year were due to: (i) more accurately measured values of IPS and IKS that had become available, (ii) small increases in IPS and IKS estimated from the actual input±output balance of the previous crops, and (iii) setting more realistic yield goals. Average P use in the FFP remained the same (18 kg P ha 1 per crop) as for the period before introduction of SSNM, but farmers at three sites (TH, MA, and OM) increased their K use in FFP plots. The latter two sites represent direct-seeded rice systems in which plant symptoms of K de®ciency were ®rst observed in 1997. For example, at OM (Mekong Delta), average K use in the FFP increased from 9 (1995±1996) to 21 kg K ha 1 per crop (1997±1999). During the same period, farmers at SU responded to the economic crisis in their country by cutting their K use from an average of 16 to less than 5 kg K ha 1 per crop.

Fig. 2. Histogram of differences in grain yield and agronomic ef®ciency of fertilizer-N (AEN, kg yield increase per kg N applied) between SSNM and the FFP. Frequencies shown are based on four consecutive rice crops grown in 179 ®elds during 1997±1999.

P ˆ 0:013, Table 4). Moreover, this difference increased from 1.7 kg N ha 1 per crop in the ®rst year (n.s.) to 7.4 kg N ha 1 in the second year (6% less, P ˆ 0:004). Similarly, the average P use in SSNM exceeded that in FFP by 4.7 kg P ha 1 per crop in year 1, but this difference was reversed to 2 kg P ha 1 in year 2. Whereas the farmers differentiated little between HYS and LYS in their P use, P rates under SSNM were about 3 kg P ha 1 larger in HYS than in LYS to account for the differences in the climatic yield potential and yield goals. Average fertilizer-K rates in

2.7.4. Nitrogen use ef®ciency SSNM led to signi®cant increases in N use ef®ciency. Average AEN under SSNM was 15 kg kg 1, REN 0.40 kg kg 1, and PFPN 52 kg kg 1. Compared to the FFP in the same season, average AEN and REN increased by almost 30%, PFPN by 6% (Table 4). Average N use ef®ciencies in the FFP during 1997± 1999 were similar to those measured for the baseline data set before intervention (Fig. 1), con®rming that possible changes in farmers' N management due to the presence of an SSNM plot were not widespread. Increases in AEN and REN due to SSNM were consistent in all crops grown (Fig. 1) and similar in high-yielding and low-yielding climatic seasons (nonsigni®cant crop-season effects, Table 4). However, treatment differences in AEN and REN signi®cantly increased from 21±24% in year 1 to 34±38% in year 2 (crop-year effect), re¯ecting the gradual improvements in dynamic N management algorithms made over time. SSNM had no signi®cant effect on PEN. In both SSNM and FFP treatments average PEN remained at suboptimal levels of 36±37 kg kg 1,

52

A. Dobermann et al. / Field Crops Research 74 (2002) 37±66

although this represents a 6±9% increase over the baseline data set. The probability of occurrence of an increase in AEN (Fig. 2) and REN due to SSNM was 72 and 74%, respectively. Large increases in AEN of >5 kg kg 1 were observed in 38% of all cases. In 15% of all cases, AEN increases exceeded 10 kg kg 1, which is equivalent to doubling the current average AEN achieved by rice farmers in Asia. The proportion of ®elds with a high AEN (>20 kg kg 1) increased from 19 (1995± 1997) (FFP baseline) to 30% (1997±1999) (SSNM), whereas it decreased to 14% in the FFP during the same period. Before the introduction of SSNM, only 22% of the farmers achieved high REN of >0.5 kg kg 1. This number decreased to 15% in the FFP during 1997±1999, but increased to 35% under SSNM. Very high REN of >0.7 kg kg 1, which is comparable to that at maximum rates of N uptake (Peng and Cassman, 1998), was measured in 16% of all cases. Differences in N management between SSNM and FFP are further illustrated by analyzing the individual split applications of N. Across all sites and crops, the average number of N applications per crop in the SSNM treatment was 3.1, compared to 2.6 in the FFP …P < 0:01†. Most farmers applied high doses early in the season, when the capacity for crop uptake was small (Peng et al., 1996b). Under SSNM, N applications were typically delayed by 5±6 days compared to the farmers' practice and individual doses of pre-plant or top-dressed N were commonly about 10 kg N ha 1 smaller than in FFP (Fig. 3). These on-farm data con®rm that, when grown under optimal crop management, rice is capable of utilizing fertilizer-N very ef®ciently (Peng and Cassman, 1998). The gains in N use ef®ciency were all achieved with top-dressed applications of prilled urea and no major changes in other cropping practices, i.e., without using expensive slow release fertilizers or laborintensive deep-placement techniques. Spreading N applications more evenly throughout the growing season was probably the major factor for the increases in N use ef®ciency and also reduces the risk for environmental pollution associated with gaseous N losses or losses through runoff or leaching after a heavy fertilizer application. A more balanced NPK nutrition practiced in the SSNM may have contributed to increases in AEN and REN through more vigorous plant growth and greater resistance to diseases.

Fig. 3. Nitrogen applications in the FFP and with SSNM. Values shown represent all individual pre-plant or top-dressed N applications made in 179 ®elds at different dates.

2.7.5. Pro®tability On average, across all sites and for all four crops, there was an increase in pro®tability of US$ 46.2 ha 1 per crop through the use of SSNM (Table 4). This increase in pro®tability compares to an average net return (total value of production minus total costs) of

A. Dobermann et al. / Field Crops Research 74 (2002) 37±66

53

prices reduces pro®ts by 30% in the ®rst year to US$ 22 ha 1 per crop. Second year pro®ts would fall to US$ 48 ha 1 per crop, a decline of 20.5%, consistent with the fact that fertilizer costs under SSNM were virtually identical to those under FFP in the second year. Thus, lower paddy prices reduce the incentives for farmers to adopt SSNM, especially in the ®rst year, but the technology still remains pro®table across a wide range of prices. Changes in N prices, even if quite large, have small effects on pro®tability because N costs are a small share of gross revenue.

Fig. 4. Financial pro®tability of SSNM. Values shown are means and standard errors of the differences in gross return above fertilizer cost (GRF) and total fertilizer cost (TFC) between SSNM and the FFP treatments of four crops for each site (1997±1999).

about US$ 400 ha 1 per crop (Moya et al., 2002). Thus, on average, SSNM would increase the returns accruing to land and farmers management by about 12%. Pro®tability increased over time (Fig. 4), as also shown by the statistical signi®cance of the crop-year effect for DGRF (Table 4). Average pro®tability was US$ 32 ha 1 per crop in the ®rst year, followed by US$ 61 ha 1 per crop in the second year. The increase in pro®tability over time was due to two main factors. First, the technology was gradually improved and more effective at increasing grain yields in the second year. Second, the SSNM strategy involved re-capitalizing soil P and K applied in the ®rst year. Total fertilizer costs in SSNM plots exceeded those in FFP plots by US$ 15 ha 1 per crop in year 1, despite slightly lower N use in SSNM treatments (Table 4). By the second year, however, fertilizer costs in the two treatments were nearly identical. The increased pro®tability in the second year due to lower differential fertilizer costs of SSNM indicates that pro®tability will increase in the longer term as the bene®ts of soil re-capitalization are felt and the technology is improved. Sensitivity analysis was also conducted to investigate the effects of different prices for paddy and N on ®nancial pro®tability. For example, if paddy prices were to decline, this would lower the value of the higher yields produced by SSNM. A 20% fall in paddy

3. Differential performance and adoption potential 3.1. Differential performance of SSNM among domains Signi®cant site and site±crop effects in Table 4 illustrated variation among the rice-growing domains with regard to increases in yield, nutrient uptake, fertilizer use, N use ef®ciency and pro®t increase achieved through SSNM. Table 5 further details such performance differences by sites. Achievement of the yield goal varied from an average of about 95% at HA to less than 75% in Central Luzon and West Java (Fig. 5a) because the control of factors others than N, P, and K varied. However, instead of judging SSNM by how closely it achieves ``perfection'' as measured by a yield goal, it is perhaps more appropriate to judge it on its ability to improve on current practices. This is a different standard, and SSNM may be quite successful at increasing pro®ts even in situations where the yield goal is not achieved. For example, pro®tability increased substantially at MA and OM (Fig. 5b), despite achieving on average only 75±80% of the yield goal. SSNM was most pro®table at the sites in China, southern India, and the Philippines, ranging from US$ 57 to 82 ha 1 per crop. The sites in Vietnam exhibited intermediate levels of pro®tability at US$ 38±39 ha 1 per crop. If use of manure had been the same in SSNM and FFP treatments, the HA site would have exhibited higher levels of pro®tability on a par with those in the ®rst group. Thus, generally speaking, SSNM was quite pro®table in six of the eight domains where on-farm trials were conducted. The two sites where SSNM generated only small increases in pro®ts

Table 5 Agronomic performance of SSNM at eight sites in Asiaa Siteb Grain yield

Fertilizer N

Fertilizer P

Fertilizer-K

AEN

PEN

SSNM D (Mg ha 1) SSNM D (kg ha 1) SSNM D (kg ha 1) SSNM D (kg ha 1) SSNM D (kg kg 1) SSNM (Mg ha 1) (kg ha 1) (kg ha 1) (kg ha 1) (kg kg 1) (kg kg 1) MA SB OM SU AD TH HA JI

5.26 4.90 4.77 4.52 6.45 5.64 6.24 6.35

0.51 0.10 0.33 0.22 0.49 0.63 0.19 0.45

N uptake

111 111 98 103 127 129 93 133

1 2 13 21 15 34 11 35

P uptake

19 18 22 19 26 18 16 15

4 3 3 11 2 2 4 4

49 45 62 53 70 80 53 60

Panicles per m2

K uptake

SSNM D (kg ha 1) SSNM D (kg ha 1) SSNM D (kg ha 1) SSNM (kg ha 1) (kg ha 1) (kg ha 1) MA SB OM SU AD TH HA JI a

107 98 82 102 104 96 96 106

17.6 5.8 5.9 8.9 11.6 17.2 1.5 7.7

19 18 18 14 23 24 18 21

2.9 1.1 2.3 1.5 2.5 4.9 0.6 2.4

101 81 102 93 109 79 109 130

27 43 42 49 32 45 9 5

9.2 10.5 17.8 2.5 10.5 12.5 6.0 11.8

554 360 506 226 473 526 364 320

15 9 20 13 16 15 18 11

3.0 1.6 5.0 3.8 2.1 1.4 4.0 5.0

Spikelets per m2

D (kg ha 1) SSNM D (1000) (1000) 18 21 17 15 16 16 3 12

32 21 24 24 34 44 31 26

1.6 0.5 1.4 2.3 2.5 4.6 1.0 1.4

34 33 46 29 35 31 46 40

REN D (kg kg 1) SSNM D (kg kg 1) (kg kg 1) 2.9 2.9 0.8 0.1 2.2 3.1 2.9 3.1

0.46 0.29 0.44 0.46 0.43 0.46 0.39 0.29

0.14 0.07 0.10 0.15 0.04 0.01 0.06 0.11

Filled Spikelets

1000 grains

SSNM (%) D (%)

SSNM (g) D (g)

76 72 76 81 87 82 84 80

21.9 26.1 24.6 22.3 19.2 18.0 22.7 26.3

0.5 1.0 0.2 0.6 0.2 0.5 0.1 0.3

0.0 0.1 0.0 0.1 0.0 0.1 0.2 0.0

Values shown are SSNM treatment means and mean differences between SSNM and the FFP (D) for four successive rice crops grown at each site. MA: Maligaya, Central Luzon (Philippines); SB: Suphan Buri, Central Plain (Thailand); OM: Omon, Mekong Delta (Vietnam); SU: Sukamandi, West Java (Indonesia); AD: Aduthurai, Old Cauvery Delta, Tamil Nadu (India); TH: Thanjavur, New Cauvery Delta, Tamil Nadu (India); HA: Hanoi, Red River Delta (Vietnam); JI: Jinhua, Zhejiang (China). b

A. Dobermann et al. / Field Crops Research 74 (2002) 37±66

Fig. 5. Achievement of the yield goal in the SSNM treatment (a) and differences in the ®nancial pro®tability of SSNM among sites (b). The mean and standard error of the actual yield (% of the yield goal) for all four crops grown at each site are shows in (a). White numbers within the bars show the yield goal achievement (%) in the ®rst and second experimental year at each site. In (b), values shown are means and standard errors of the differences in gross return above fertilizer cost (DGRF) between SSNM and the FFP treatments of four crops for each site (1997±1999).

were SU in Indonesia (US$ 14 ha 1 per crop) and SB in Thailand (US$ 4 ha 1 per crop). Site by crop effects were also signi®cant for pro®tability (Table 4), indicating SSNM achieved very high levels of pro®tability in certain sites and seasons, but much lower pro®tability in other instances. While the pro®tability varied signi®cantly, it was nearly always positive for any given site and season. The only exceptions in 32 cases (four seasons in each of eight sites) were the second and fourth seasons at SB, and the ®rst season at both SU and HA.

55

Uncontrollable climatic events, initial problems in designing an appropriate SSNM scheme for each domain, and variation in the quality of crop management explain these differences among the domains studied. Because the experimental period included an El NinÄo±La NinÄa cycle, climate affected crop yields and the performance of SSNM at several sites. The actual yield reductions were dif®cult to quantify. Increased solar radiation during an El NinÄo period can increase the yield potential at certain sites, whereas greater than normal temperatures may cause increased spikelet sterility and respiration losses. Greater rainfall and more cloudy conditions associated with the La NinÄa period mostly decrease the yield potential due to less solar radiation, but also due to poor spikelet fertility if heavy rains occur at ¯owering stage. A crop modeling analysis using the ORYZA1 model (Kropff et al., 1993) in combination with actual planting dates, varieties, and weather data was conducted for the 26 Philippine farm sites (MA) to quantify yield reductions due to climatic factors (Simbahan, 2000). Simulated WS yield potential in 1997 and 1998 averaged 8.3 Mg ha 1, whereas average DS yield potentials were 9.8 Mg ha 1 in 1998 and 8.7 Mg ha 1 in 1999. Using the same model in combination with historical average climate data, rice yield potential for Central Luzon was estimated in earlier studies at about 10 Mg ha 1 for the WS and 13 Mg ha 1 for DS crops (Matthews et al., 1995). Dry seasons yields of up to 11 Mg ha 1 have been measured at this site in well-controlled ®eld experiments (Peng et al., 1999). Climate was obviously responsible for a signi®cant proportion of the failure to achieve the pre-season yield goal at this site. Yield limitations due to the El NinÄo±La NinÄa cycle probably also occurred at other sites (OM, SB, SU) where similar climatic effects also affected factors such as timing of water release into the irrigation system, planting dates, or pest dynamics. Differences in crop management practices that affected the performance of SSNM can be summarized as follows. 3.1.1. Group 1: high yields of transplanted rice with reduced use of N, P, and K fertilizers This group includes 45 rice farms near HA in the Red River Delta of North Vietnam (HA) and at JI in Zhejiang province, China (Table 5). In both domains,

56

A. Dobermann et al. / Field Crops Research 74 (2002) 37±66

two transplanted rice crops (spring and summer rice) are grown in very small ®elds (®eld sizes <0.3 ha) under subtropical climate. Increasing yield is of primary concern for farmers at these sites because land is scarce. All farmers at HA grow an upland winter crop and many at JI used to do this in the past. Labor input ranged from about 100 (JI) to 230 days ha 1 (HA) (8 h mandays) because much time is spent with crop care, including intensive use of pesticides and, at HA, applying farmyard manure. Due to a history of collective farming and the promotion of mineral fertilizers, NPK use was high even before introduction of SSNM, including average rates of 40±60 kg K ha 1 per crop. SSNM in these farms was characterized by: (i) high average rice yields of 6.2±6.4 Mg ha 1, (ii) reduction in mineral fertilizer use compared to the FFP, (iii) large relative increases in AEN, PEN, and REN due to plant-based N management, and (iv) good quality of crop management. Yield increases over FFP were small at HA because much greater amounts of farmyard manure were applied in the FFP treatment than in SSNM, whereas large increases in yield were achieved at JI. A greater degree of achieving the predicted yield goal (Fig. 5a) and site averages that were close to the line describing the optimal relationship between grain yield and plant N accumulation for balanced NPK nutrition (Fig. 6) con®rm the good cropping conditions and crop management of these farmers. At JI, however, N use ef®ciency remained moderate in absolute terms and we hypothesize than it can be further improved by synchronizing N with water management (Wang et al., 2001). 3.1.2. Group 2: high yields of transplanted rice with increased use of N, P, and K fertilizers This group includes 40 rice farms in the Old (AD) and New (TH) Cauvery Deltas of Tamil Nadu (Table 5). Transplanted rice is grown in medium size ®elds (0.5 to >1 ha) under tropical climate. Farmers often prefer ®ne-grained modern varieties with a 1000-grain weight of less than 20 g. At both sites, labor input was high (80±150 days ha 1) but pesticide use was low. This group is characterized by: (i) high average rice yields in the SSNM (5.6±6.5 Mg ha 1) and large yield increases over FFP, (ii) increase in the use of N, P, and K fertilizer, (iii) high AEN, PEN, and REN due to plant-based N management, and (iv) moderate to excellent quality of crop management.

Fig. 6. Relationship between grain yield or total above-ground dry matter and N accumulation of rice. The dashed lines shown the envelope of maximum dilution (YND) and maximum accumulation (YNA) of N in the plant. The solid line shows the relationship for balanced nutrition of N, P, and K as simulated by the QUEFTS model (Witt et al., 1999). Circles indicate measured average values in SSNM plots at eight sites (1997±1999, site means of four rice crops). (MA) Maligaya, Central Luzon (Philippines); (SB) Suphan Buri, Central Plain (Thailand); (OM) Omon, Mekong Delta (Vietnam); (SU) Sukamandi, West Java (Indonesia); (AD) Aduthurai, Old Cauvery Delta, Tamil Nadu (India); (TH) Thanjavur, New Cauvery Delta, Tamil Nadu (India); (HA) Hanoi, Red River Delta (Vietnam); (JI) Jinhua, Zhejiang (China).

Increased N uptake (13±22%) was probably the major cause of yield increases. At AD, average yield was high and close to the optimal line describing the relationship between grain yield and plant N accumulation (Fig. 6). Yield losses observed there were mainly caused by insects. Average yield increases of 13% were achieved at TH, but the yield goal achievement was lower and the internal N use ef®ciency (amount of grain produced per unit N taken up) was suboptimal (Fig. 6) due to somewhat larger stresses such as occasional water shortages or insect damage in some ®elds.

A. Dobermann et al. / Field Crops Research 74 (2002) 37±66

3.1.3. Group 3: low yields of transplanted rice due to factors other than N, P, and K supply This group includes the 20 rice farms in West Java (SU, Table 5). Transplanted rice is grown in dry and wet seasons in small ®elds (<0.5 ha) under tropical climate. Labor input averages 150 days ha 1 and pesticide use is moderate. The performance of SSNM in this domain was negatively affected by unfavorable climate as well as pest infestations at certain sites so that yield goals were rarely achieved (Fig. 5a). Average yields were the lowest among all domains (4.5 Mg ha 1), although SSNM caused a large increase in N use ef®ciency. For example, REN increased by almost 50% from 0.31 under FFP to 0.46 kg kg 1 under SSNM, but the extra N taken up was not converted into grain yield. The average internal ef®ciency of N was close to the line of maximum accumulation on both grain yield or total dry matter basis (Fig. 6), demonstrating that stresses occurred during all stages of growth. Untimely, water release into the irrigation system due to the El NinÄo cycle, rats, weeds, diseases (sheath blight) and insects (brown planthopper, stemborers) were among the problems observed. Initial problems such as unrealistically high yield goals and a poor N-management scheme caused poor performance of SSNM during the ®rst year. However, for the third and fourth crops, pro®tability at SU was much improved at US$ 33 ha 1 per crop. 3.1.4. Group 4: direct-seeded rice This group includes 74 rice farms in Central Luzon (MA), Central Thailand (SB), and the Mekong Delta (OM) (Table 5). Broadcast, direct-seeded rice is grown in dry and wet seasons in small to medium size ®elds (<0.5±1 ha) under tropical climate. High seed rates (100±200 kg ha 1) are predominant and labor input is 15±60 days ha 1. Pesticide use varies, but farmers generally use herbicides for weed-control. Farmers applied little K, although at the OM and SB sites triple cropping of rice was common. Straw was mostly burned in the ®eld. The performance of SSNM in this group was characterized by: (i) low to moderate average rice yields of 4.8±5.3 Mg ha 1 and widely varying yield increases over FFP, (ii) low grain ®lling percentage of about 75%, (iii) large increases in the use of K fertilizer compared to the FFP, (iv) small to large increases in AEN and REN, and (v) widely

57

varying quality of crop management. Yield increases were signi®cant at OM and MA, but not at the SB site, where crop management problems were largest and the labor input was small. At all three sites, the average rice yield per unit plant N was about 1±1.4 Mg ha 1 below that achievable with optimal internal N ef®ciency (Fig. 6). When total dry matter was plotted versus N uptake, the difference between actual and optimal values diminished (Fig. 6). We interpret this as evidence for yield losses that were mainly caused by abiotic or biotic stresses during reproductive growth. This is also supported by the low grain ®lling percentage at all three sites and earlier studies showing an inferior ability of direct-seeded rice to convert high biomass production into grain yield compared to transplanted rice (Schnier et al., 1990). Unfavorable climate (see above), water management, poor seed quality, weeds, insect pests, and diseases were problems in these direct-seeded rice areas, particularly in wet season crops. Considering that many farmers in Asia have recently shifted from transplanting to direct-seeding, improving the management of direct-seeded rice presents a particular challenge. Direct-seeded rice can yield as much as transplanted rice provided that both crops are managed differently to their potential (Peng et al., 1996a). However, our data raise concern that a move from labor-intensive, small-scale, transplanted rice farming to labor-saving direct-seeding of larger ®elds may be associated with lower yields due to imperfect control of the factors affecting resource use ef®ciency. Average rice yields at the sites with direct-seeding were about 1 Mg ha 1 lower than those achieved at the four sites with transplanted rice and good management. In Thailand, the pro®tability of SSNM was consistently low, and was negative for two of the four crops. There were problems with weeds, insects, and diseases in the direct-seeded rice ®elds in this domain. Also, the Thai farmers used low amounts of labor for crop care and had low levels of education (Moya et al., 2002). It is not known to what extent these factors contributed to the poor performance of SSNM. However, if such a general tendency is true this may pose a serious threat for further increases in rice production at regional or even global scales. It remains to be clari®ed whether signi®cantly different crop management strategies are required for improving SSNM in direct-seeded rice.

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A. Dobermann et al. / Field Crops Research 74 (2002) 37±66

3.2. Differential performance of SSNM among farms While SSNM is on average pro®table for most farmers at all sites, important questions relate to differential performance across farmers. What changes in nutrient management contribute most to yield increase? Does SSNM perform better on soils with high soil fertility status? Does SSNM raise yields more for farmers with below average yields? Does it increase pro®ts on average for all farmers after several crops have been grown? The experimental design used in the on-farm studies does not allow a clear separation of the cause±effect relationships between different macronutrients and yield increases due to SSNM, mainly because the planting dates and timing of N applications differed widely among farms. However, regression analysis allows drawing some preliminary conclusions. In a ®rst stepwise multiple regression, grain yield (GY, kg ha 1) was described as a function of INS, IPS, IKS, rates of N (FN), P (FP), and K (FK) applied, the number of N applications per crop (Napp), and the crop management quality score (CM). In the SSNM treatment, 76% of the variation in average grain yield among farms was explained by INS, IPS, Napp, and CM (all signi®cant at P < 0:001) GYSSNM ˆ 1306 ‡ 16INS ‡ 78IPS ‡ 797Napp 446CM;

R2 ˆ 0:762

(4)

Napp had the greatest positive partial correlation coef®cient …r p ˆ 0:65†, whereas poor crop management quality contributed signi®cantly to lower yields …r p ˆ 0:48†. Rates of N, P and K applied did not explain additional yield variation in the SSNM treatment because the calculation approach used for ®eldspeci®c fertilizer prescriptions was based on the indigenous nutrient supplies measured in each farm. However, in the FFP treatment, rates of fertilizer use had signi®cantly negative (FN) or positive (FP, FK) effects on yield across the 179 farms GYFFP ˆ 2335 ‡ 18INS ‡ 72IPS ‡ 8FK ‡ 333Napp 2

R ˆ 0:662

4FN ‡ 18FP

250CM; (5)

In summary, grain yields in both SSNM and FFP were largest in farms with (i) high indigenous supply of N

and P, (ii) large number of N applications per crop, and (iii) high quality of crop management. Average pairwise differences in grain yield (DGY …kg ha 1 † ˆ GYSSNM GYFFP ) were described as a function of CM, INS, IPS, IKS and the pairwise differences in fertilizer use of N (DFN, kg ha 1), P (DFP, kg ha 1), and K (DFK, kg ha 1) and the recovery ef®ciency of applied N (DREN, kg ha 1). Only ®ve of these independent variables had statistically signi®cant effects on DGY, explaining 41% of its variation among farms DGY ˆ 170

6:2INS ‡ 30:6IPS ‡ 6:8 DFN

‡ 2247 DREN

108CM;

R2 ˆ 0:407 (6)

Eq. (6) suggests that yield increases over current fertilizer practices were largest at sites with lower INS and higher IPS, increased N use in association with high N use ef®ciency, and high quality of crop management, whereas the role of potassium appeared less clear. Without DREN, the R2 value of the regression dropped to just 0.139, indicating that understanding REN is the key for understanding why SSNM worked on some farms and not in others. However, regression analysis to explain DREN as a function of initial conditions (INS, IPS, IKS) and variables that express changeable conditions via recommendations (DFN, DFP, DFK, DNapp) did not provide conclusive results. We interpret this as indirect evidence of the importance of timing of N applications for DREN and DGY rather than just differences in N rates or the number of N applications, but a better experimental and statistical approach must be developed to analyze and con®rm this. The role of K for increasing rice yields in the SSNM and its interaction with plant N nutrition at different yield levels requires further clari®cation. Both IKS and increased use of fertilizer-K in the SSNM had nonsigni®cant effects on the variation in DGY among farms, probably because the average IKS level was suf®cient to meet crop K needs at the yield levels achieved at most sites. The average grain yield in 0-K plots (5.66 Mg ha 1) was larger than the average grain yield in FFP (5.18 Mg ha 1) and SSNM (5.54 Mg ha 1). However, differences in N uptake between SSNM and FFP were closely associated with differences in P (DUP, kg ha 1) and K uptake

A. Dobermann et al. / Field Crops Research 74 (2002) 37±66

(DUK, kg ha 1) and the difference in the number of N applications (DNapp) DUN ˆ 0:97 ‡ 2:38 DUP ‡ 0:19 DUK ‡ 3:51 DNapp; R2 ˆ 0:568

(7)

Therefore, once other constraints to yields are eliminated to allow greater utilization of plant N, knowing and managing the IKS is likely to be as important as improving the timing of N applications for achieving higher yields than the averages obtained in our studies. A priori, it is reasonable to suppose that SSNM would increase yields more for farmers whose yields are usually lower than other members of the group, the implicit assumption being that these lower yields are due at least in part to poor nutrient management. A regression of DGY for a given farmer against that farmers' average yield consistently gave a negative coef®cient on the independent variable at all sites. However, the magnitude of that coef®cient was typically small, with the P-value not allowing for a rejection of the null hypothesis of no effect (data not shown). The main exception was AD, where the coef®cient was statistically signi®cant at the 5% level and an average yield that was lower by 1 Mg ha 1 was associated with a larger yield increase due to SSNM of 0.39 Mg ha 1. At most of the other sites, however, an average yield that was lower by 1 Mg ha 1 was associated with a yield increase due to SSNM of less than 0.05 Mg ha 1. In order to better understand the consistency of increased pro®tability among different farmers, two histograms were compared (Fig. 7). One is a histogram of the absolute increase in pro®ts, using one crop for a speci®c farmer as the unit of observation (Fig. 7a, crop-speci®c histogram). The other is a histogram of the absolute increase in pro®ts using the average level of pro®ts over four crops for a speci®c farmer as the unit of observation (Fig. 7b, farmer-speci®c histogram). This second histogram gives a sense of how often SSNM is not pro®table even when averaged over four crops. When considering speci®c crops, SSNM reduced pro®tability 28% of the time. When considering the average of four crops for individual farmers, SSNM reduced pro®tability less frequently (because pro®tability does not always decline for the same farmers), but the frequency of reduced pro®ts fell only slightly to 22%. About one-third of the farms where

59

SSNM was not pro®table occurred at SB, but the rest of the cases were relatively evenly distributed across sites. These results suggest that SSNM did not work for certain small groups of farmers, and this is an important avenue for future research. One hypothesis is that a certain minimum level of crop care is required for SSNM to be pro®table. The other implication is that for the farms where SSNM increases pro®tability on average, it appears to do so consistently from crop to crop. This consistency in performance may positively in¯uence farmers' perception of the technology's usefulness. 3.3. Attainable levels of productivity and nutrient use ef®ciency What are realistic goals for increasing productivity and resource use ef®ciency at the farm level with the currently available germplasm? Table 6 lists selected agronomic characteristics for roughly 25% of all cases in which the actual yield in the SSNM treatment was within 10% of the pre-season yield goal. This subset represents situations where the model assumptions for prescribing ®eld-speci®c fertilizer rates were met, i.e., where the response to N, P, and K was not much limited by other crop management factors. Note that this data set contained similar numbers of cases for seasons with low and high climatic yield potential. Average rice yield in this subset was 6.84 Mg ha 1, which exceeded the overall SSNM mean by 23% and was equivalent to about 82% of the simulated average climatic yield potential of rice in Asia (Matthews et al., 1995). This elevated yield level was associated with high grain ®lling percentage (85%), reduced use of N and P fertilizer, but greatly increased addition of fertilizer-K. Remarkably, the average N rate for achieving such yields was only 110 kg N ha 1 per crop because good crop management resulted in high internal N use ef®ciency of 61 kg grain per kg N uptake, which was close to optimal values speci®ed for such yield levels of rice (Witt et al., 1999). Agronomic and recovery ef®ciency of N were about 40% greater than with the current farmers' management. Another way to look at this is to select farmers who consistently performed better than average, i.e., based on achieving the yield goal in all four crops grown. In 41 out of 179 farms, average yields of four rice crops grown were within 10% of the yield goal.

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Fig. 7. Histograms of: (a) crop-speci®c and (b) farm-speci®c differences in ®nancial pro®tability. Relative frequencies shown are based on differences in gross return above fertilizer cost (DGRF) between SSNM and the FFP treatments. The crop-speci®c histogram is derived from data on 675 crops (179 different farms at eight different sites). The farm-speci®c histogram is derived from averages of three or four crops for 179 different farms at eight different sites. Table 6 Agronomic performance of SSNM in plots that yielded within 10% of the yield goala Factor

SSNM

Grain yield (Mg ha 1) Total number of spikelets per meter square (1000) Filled spikelets (%) N fertilizer (kg ha 1) P fertilizer (kg ha 1) K fertilizer (kg ha 1) Plant N uptake (kg ha 1) Plant P uptake (kg ha 1) Plant K uptake (kg ha 1) Agronomic efficiency of N (AEN, kg kg 1) Recovery efficiency of N (REN, kg kg 1) Partial productivity of N (PFPN, kg kg 1)

6.84 32.2 84.6 110.0 15.0 53.4 112.9 23.4 113.1 18.4 0.45 59.7

a b

Db 0.44 (7%) 1.9 (6%) 0.4 (0.5%) 9.4 ( 8%) 1.7 ( 10%) 23.2 (77%) 2.6 (13%) 3.1 (15%) 9.1 (9%) 5.2 (39%) 0.13 (41%) 8.5 (14%)

Values shown are medians of 157 cases (1997±1999) and include farms in all experimental domains and climatic seasons. Median of the differences between SSNM and the FFP and percentage difference between SSNM and FFP.

A. Dobermann et al. / Field Crops Research 74 (2002) 37±66

The average agronomic characteristics of this sample were similar to those shown in Table 6 (data not shown), even though this sample of farms was somewhat biased towards few sites with transplanted rice and high quality of crop management (HA, JI, AD). 3.4. Other considerations for technology adoption Besides pro®tability, other considerations include the observability of the technology's bene®ts, risk, and credit requirements. While SSNM was on average pro®table for most farmers, there remains a question of how visible the increased pro®tability will be to farmers. This is especially important if extension to a larger domain is anticipated, because not all of these farmers will have the opportunity to interact closely with researchers or have access to records of controlled experiments that compare SSNM with FFP. Under these circumstances, the advantages of SSNM will need to be obvious even to casual observation. Large improvements in yield might constitute one such signal to farmers. On average, SSNM generated a yield gain of at least 0.5 Mg ha 1 in approximately 35% of the on-farm trials (Fig. 2). It is not clear what standard is relevant for judging 35% to be large or small, but it seems large enough to make a substantial share of farmers notice the change. If yield variability is substantially larger under SSNM than under current fertilizer management practices, this risk might constrain adoption. The evidence from the on-farm trials conducted so far strongly suggest this is not an issue, as yield variability is approximately the same under SSNM as it is under FFP. The standard deviation of grain yield for all four crops at all sites under SSNM was 1.35 Mg ha 1, compared to 1.29 kg ha 1 for FFP. The coef®cient of variation (CV) was lower for SSNM (24%) than for FFP (25%), however, because SSNM yields were higher than FFP yields by 7% on average. Regardless of which measure is used, the difference in variability is not substantial in terms of its effect on farmers. Many investments, both inside and outside agriculture, are pro®table but cannot be adopted due to large costs of investment that must be incurred up-front. This is particularly a constraint for poor farmers who have less working capital at their disposal. Because the SSNM strategy depends to some extent on re-capitalization of soil K (and P) at sites where the

61

soil supply of these nutrients is low, it does require some commitment of capital by farmers. For the ®rst year of SSNM, when a large part of soil re-capitalization was done, the average increase in fertilizer costs in SSNM relative to those in FFP was US$ 15.1 ha 1 per crop across all sites. This declined to just US$ 1.5 ha 1 per crop in the second year (Table 4, Fig. 4). The ®rst year costs of US$ 15.1 ha 1 per crop would constitute about a 5% increase in total paid out costs for farmers. This is not especially large, and suggests that credit constraints should not be a major constraint to adoption in the intensive irrigated areas. Furthermore, it should also be noted that a survey of farmers conducted earlier in the project showed that only 7% of farmers mentioned that the availability of cash or credit in¯uenced their fertilizer management decisions (Moya, 1998). 3.5. Simpli®cation and dissemination of SSNM Widespread adoption of new technologies will be greatly facilitated if they have large ®nancial advantages for farmers, are relatively simple to implement at the farm level, or both (Pingali et al., 1998). The Green Revolution package of seeds and fertilizers was widely adopted in many countries in a short time because it combined both of these features. SSNM will not have the same impact as the Green Revolution, but it does increase yields and farmer pro®ts. Yet probably the most important factor that will determine whether it is widely adopted in the future will be how easy it is for farmers to implement without a major commitment of time. The SSNM approach tested in our studies should be viewed as an umbrella concept for deriving simpli®ed forms tailored to the biophysical characteristics and socioeconomic needs of different domains and farms within them. The question we must ask is whether nutrient management decisions need to be made speci®cally for each farm, or even for particular parcels within a farm, or can they be generalized across farms within a particular area. In some of the domains studied here (e.g., HA, JI), small ®eld sizes present the most serious challenge to implement such an approach with great detail, whereas the existing experience with crop care and balanced fertilizer use as well as centralized extension structures may be of advantage for introducing more ®ne-tuned

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technologies. Mass communication of major principles and rules, demonstration in large, contiguous areas, and simple decision tools are probably priorities in such areas. On the other hand, at sites with numerous yield constraints other than macronutrient supply (e.g., SU, SB), those issues must be addressed before there is much scope for a site-speci®c approach to nutrient management. In some areas, SSNM may take the form of improved region-speci®c recommendations that, e.g., simply stress the importance of re-capitalizing soil K. In an effort to simplify SSNM for wider dissemination to both extension workers and interested farmers, researchers are currently developing a pocket fertilizer guide to aid in developing better fertilizer recommendations that implicitly or explicitly take into account season, soil quality, and key cropping features. Key technical components to simplify include the need for nutrient omission plots to estimate indigenous nutrient supplies, site-speci®c schemes for N management, and simpli®ed P and K management scenarios. Grain yield measurement in omission plots instead of measuring plant nutrient accumulation or soil nutrient pools is a promising alternative for estimating INS, IPS, and IKS, at least within a few broad categories. Considering the spatial and temporal variability in indigenous nutrient supplies and other uncertainties associated with recovery ef®ciencies and the actual ability to accurately apply fertilizer, such rough estimates appear suf®cient for a simpli®ed SSNM approach. Better algorithms for setting a realistic pre-season yield goal must be developed to guide estimates of NPK requirements, particularly at sites where adoption of plant-based N management is less attractive. Our empirical studies suggest that a yield goal should be within the yield of the past 3±5 years plus 10±20%, but less than 80% of the inferred climatic yield potential because dif®cult to control yield-limiting factors always exist in subtropical and tropical rice environments. The model used to develop the ®eldspeci®c fertilizer prescriptions assigned the same weights to N, P, and K with regard to their effect on yield. It is, however, likely that K can be diluted relatively more in the plant than N before a signi®cant reduction in growth occurs. Possibly, yield gains similar to those in the SSNM approach tested can be achieved with well-chosen blanket doses of P and

K, but in combination with improved N management tactics. Several years of consecutive experimentation are required to develop an ``optimal'' N management scheme for a particular area that is characterized by a set of common environmental, socioeconomic and cropping characteristics. Seasonal variation is large and ®ne-tuning of N needs to be done in accordance with other crop management practices. Using a single leaf threshold value for in-season adjustment of N in rice has a sound scienti®c basis (Peng et al., 1993, 1996b). However, such an approach may not be very practical at certain locations because it requires weekly ®eld measurements, it still lacks a quantitative basis for deciding how much N to apply, and involves uncertainties associated with the measurement and the critical threshold used for different environments and genotypes. The N management strategies at the eight domains in this project were an empirical attempt to use a pre-plant fertilizer model in combination with an in-season decision aid for location-speci®c ®ne-tuning of N that also reduces risk by allowing for a more gradual adjustment of rates at critical growth stages. More research is required to (1) develop a generic, validated scienti®c basis for this (Witt et al., 2001), (2) rigorously compare this approach with other forms of N management, and (3) replace the chlorophyll meter with simpler tools such as a Leaf Color Chart (Balasubramaniam et al., 1999) or ®ned-tuned standard split application schemes. Examples for the latter, taking into account differences in yield potential (season), crop establishment method, and varieties, were recently proposed by Dobermann and Fairhurst (2000). 4. Conclusions In most irrigated rice areas in Asia, further yield increases are likely to occur in small, incremental steps that involve gradual buildup of soil fertility and ®ne-tuning of crop management. Future, strategies for nutrient management in intensive rice systems must become more site-speci®c and dynamic to manage spatially and temporally variable resources based on a quantitative understanding of the congruence between nutrient supply and crop demand. The SSNM concept has demonstrated promising agronomic and

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economic potential. It can be used for managing plant nutrients at any scale, i.e., ranging from a general recommendation for homogenous management of a larger domain to true management of between-®eld variability. Assessment of pest pro®les in FFP and SSNM plots suggests that SSNM may also reduce pest incidence, particularly diseases that are often associated with excessive N use or unbalanced plant nutrition (Sta.Cruz et al., 2001). Field-speci®c management of macronutrients increased nutrient uptake, yields, and N use ef®ciency in the majority of 179 irrigated rice farms in Asia. Depending on the basis used for comparison, yield increases in diverse environments and climatic seasons averaged 0.4±0.5 Mg ha 1 (7±11%). On a global scale, such yield increases would be suf®cient for matching about 6±10 years of annual growth in rice demand in Asia. The performance of SSNM did not differ signi®cantly between HYS and LYS crops, but improved over time. Yield increases were achieved with a decrease in the average N rate, but were associated with increased N uptake due to improved N management. Compared to current farmers' practices, N losses from fertilizer were typically reduced by 30±40% and pro®tability increased by 12% of the total average net return. SSNM requires little in the way of credit for ®nancing or complex coordination among farmers. Signi®cant performance differences among sites suggest further scope for improvement by alleviating other crop management constraints to nutrient use ef®ciency. The major challenge for SSNM will be to retain the success of the approach while reducing the complexity of the technology as it is disseminated to farmers. The nature of the approach will need to be tailored to speci®c circumstances in different countries. In some areas, SSNM may be ®eld or farm speci®c, but in many areas it is likely to be just domain- and seasonspeci®c. A simpli®ed future SSNM approach should combine decisions that are made on a ®eld-speci®c basis as well as decisions that are valid for somewhat larger recommendations domains with similar socioeconomic and biophysical conditions. Estimates that allow placing a ®eld into one of several broad categories of indigenous nutrient supply are probably suf®cient for most SSNM applications. Soil test-based approaches have many theoretical and technical limitations in irrigated rice environments of Asia,

63

whereas simple plant-based indicators of nutrient supply such as grain yield in nutrient omission plots or leaf color may be accurate enough for guiding SSNM. The location-speci®c N management strategies developed for each site need to be re®ned and tested against other forms of N management. A pre-season N recommendation may not be required at sites where farmers are interested in adopting more knowledge-intensive forms of real-time corrective N management. Emphasis was given to strategic, interdisciplinary on-farm research across a wide geographical range. This greatly enhanced the validity of empirical algorithms developed and their extension and extrapolation potential. Updates will be easier to implement because there is less reliance on soil tests, and because nutrient uptake and yield are estimated explicitly and modeled in absolute terms. Signi®cant investments should be made to further develop and simplify the SSNM approach proposed so that variants of it can be disseminated more widely as part of locally adopted integrated crop management solutions. 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 of Declining Productivity in Intensive Irrigated Rice Systems'. We particularly wish to thank G.O. Redondo, S. Serrano, A.P. Estigoy, M. Elliot, C. Espana, R. Dawang, E.M. Punzalan, R.T. Cruz, and J. Bajita (Maligaya), M. Kongchum, L. Niyomvit, S. Noksakul, W. Inthalaeng, T. Maneesang, S. Inthalaeng, and O. Theppayasirikamol (Suphan Buri), Z. Susanti, Pahim, A. Djatiharti, D. Subarja, Atim, T. Rustiati, Y. Ariyani, U. Sutaryo Suhana, and I. Juliardi (Sukamandi), S. Selvam, R. Sakunthala, V. Gnanabharathi, G. Sasikumar, S. Sridevi, S. Sujatha, and S. Antony Samy (Thanjavur), M. Sivanantham, D. Kabilar, M. Selvakumar, S. Natarajan, R. Jayaseelan, S. Arumugam, and S. Selvaganabathy (Aduthurai), He Yunfeng, Huang Xueping, Wu Jiangxiang, and Ding Xianghai (Jinhua), Tran Quang Tuyen, Tran Thi Ngoc Huan, Trinh Quang Khuong, Nguyen Thanh Hoai, Le Ngoc Diep, Ho Tri Dung, and Nguyen Xuan Lai (Omon), and A. Bautista, P.F. Moya, M. Tiongco,

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and D. Pabale (IRRI) for their contributions to conducting this research. We thank Kenneth Cassman and Daniel Olk for leading the project during 1994±1996 and Roland Buresh (IRRI) for valuable comments on an earlier draft of this paper. The Swiss Agency for Development and 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. References Adhikari, C., Bronson, K.F., Panaullah, G.M., Regmi, A.P., Saha, P.K., Dobermann, A., Olk, D.C., Hobbs, P., Pasuquin, E., 1999. On-farm soil N supply and N nutrition in the rice±wheat system of Nepal and Bangladesh. Field Crops Res. 64, 273±286. Angus, J.F., St.-Groth, C.F.D., Tasic, R.C., 1990. Between-farm variability in yield responses to inputs of fertilizers and herbicide applied to rainfed lowland rice in the Philippines. Agric. Ecosyst. Environ. 30, 219±234. Balasubramaniam, V., Morales, A.C., Cruz, R.T., Abdulrachman, S., 1999. On-farm adaptation of knowledge-intensive nitrogen management technologies for rice systems. Nutr. Cycl. Agroecosyst. 53, 59±69. Balasubramaniam, V., Morales, A.C., Thiyagarajan, T.M., Nagarajan, R., Babu, M., Abdulrachman, S., Hai, L.H., 2000. Adaption of the chlorophyll meter (SPAD) technology for real-time N management in rice: a review. Int. Rice Res. Newslett. 25, 4±8. Bouldin, D.R., 1986. The chemistry and biology of ¯ooded soils in relation to the nitrogen economy in rice ®elds. Fert. Res. 9, 1± 14. Bremner, J.M., Mulvaney, C.S., 1982. Nitrogen: total. In: Page, A.L., Miller, R.H., Keeney, D.R. (Eds.), Methods of Soil Analysis. Part 2. Chemical and Microbiological Properties. ASA, Madison, pp. 595±623. Cassman, K.G., Dobermann, A., 2001. Evolving rice production systems to meet global demand. In: Rice Research and Production in the 21st Century: Proceedings of a Symposium Honoring Robert F. Chandler Jr., 15±16 June 2000, International Rice Research Institute, Manila. Cornell University Press, Ithaca, NY, pp. 79±100. Cassman, K.G., Harwood, R.R., 1995. The nature of agricultural systemsÐfood security and environmental balance. Food Policy 20, 439±454. Cassman, K.G., Pingali, P.L., 1995. Extrapolating trends from longterm experiments to farmers ®elds: the case of irrigated rice systems in Asia. In: Barnett, V., Payne, R., Steiner, R. (Eds.), Agricultural Sustainability in Economic, Environmental, and Statistical Terms. Wiley, London, pp. 64±84. Cassman, K.G., Dobermann, A., Sta.Cruz, P.C., Gines, H.C., Samson, M.I., Descalsota, J.P., Alcantara, J.M., Dizon, M.A.,

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