Although relationships among soybean (Glycine max [L.] Merr) seed yield, nitrogen (N) uptake, biological N 2 fixation (BNF), and response to N fertilization have received considerable coverage in the scientific literature, a comprehensive summary and interpretation of these interactions with specific emphasis on high yield environments is lacking. Six hundred and thirty-seven data sets (site-year-treatment combinations) were analyzed from field studies that had examined these variables and had been published in refereed journals from 1966 to 2006. A mean linear increase of 0.013 Mg soybean seed yield per kg increase in N accumulation in above-ground biomass was evident in these data. The lower (maximum N accumulation) and upper (maximum N dilution) boundaries for this relationship had slopes of 0.0064 and 0.0188 Mg grain kg −1 N, respectively. On an average, 50-60% of soybean N demand was met by biological N 2 fixation. In most situations the amount of N fixed was not sufficient to replace N export from the field in harvested seed. The partial N balance (fixed N in above-ground biomass − N in seeds) was negative in 80% of all data sets, with a mean net soil N mining of −40 kg N ha −1 . However, when an average estimated below-ground N contribution of 24% of total plant N was included, the average N balance was close to neutral (−4 kg N ha −1 ). The gap between crop N uptake and N supplied by BNF tended to increase at higher seed yields for which the associated crop N demand is higher. Soybean yield was more likely to respond to N fertilization in high-yield (>4.5 Mg ha −1 ) environments. A negative exponential relationship was observed between N fertilizer rate and N 2 fixation when N was applied on the surface or incorporated in the topmost soil layers. Deep placement of slow-release fertilizer below the nodulation zone, or late N applications during reproductive stages, may be promising alternatives for achieving a yield response to N fertilization in high-yielding environments. The results from many N fertilization studies are often confounded by insufficiently optimized BNF or other management factors that may have precluded achieving BNF-mediated yields near the yield potential ceiling. More studies will be needed to fully understand the extent to which the N requirements of soybean grown at potential yields levels can be met by optimizing BNF alone as opposed to supplementing BNF with applied N. Such optimization will require evaluating new inoculant technologies, greater temporal precision in crop and soil management, and most importantly, detailed measurements of the contributions of soil N, BNF, and the efficiency of fertilizer N uptake throughout the crop cycle. Such information is required to develop more reliable guidelines for managing both BNF and fertilizer N in high-yielding environments, and also to improve soybean simulation models.

INTRODUCTIONThe focus of this paper is on nitrogen-use efficiency (NUE) in cereal production systems because maize (Zea mays L.), rice (Oryza sativa L.), and wheat (Triticum aestivum L.) provide more than 60% of human dietary calories either as cereals for direct human consumption or embodied in livestock products produced from animals fed with feed grains and their by-products (http:/apps.fao.org/, agricultural production). It is likely that these same cereal crops will continue to account for the bulk of the future human food supply because they produce greater yields of human-edible food, are easily grown, stored, and transported, and require less fuel and labor for processing and cooking than other food crops. Our analysis will examine the NUE of these primary cereals in the world's major cropping systems, which also account for the majority of global N fertilizer use. We define the NUE of a cropping system as the proportion of all N inputs that are removed in harvested crop biomass, contained in recycled crop residues, and incorporated into soil organic matter and inorganic N pools. Nitrogen not recovered in these N sinks is lost from the cropping system and thus contributes to the reactive N (Nr) (1) load that cascades through environments external to the agroecosystem.Our evaluation will focus on NUE in on-farm settings because estimates of NUE from experimental plots do not accurately represent the efficiencies achieved in farmers' fields. This lack of agreement results from differences in the scale of farming operations and differences in N-management practices-some of which are only feasible in small research plots. The effect of scale not only influences N fertilizer application, but all other management operations such as tillage, seeding, weed and pest management, irrigation, and harvest, which also affect efficiency. As a result, N-fertilizer efficiency in well-managed research experiments is generally greater than the efficiency of the same practices applied by farmers in production fields. For example, the average N-fertilizer uptake efficiency (defined as the percent-

Agriculture is a resource-intensive enterprise. The manner in which food production systems utilize resources has a large influence on environmental quality. To evaluate prospects for conserving natural resources while meeting increased demand for cereals, we interpret recent trends and future trajectories in crop yields, land and nitrogen fertilizer use, carbon sequestration, and greenhouse gas emissions to identify key issues and challenges. Based on this assessment, we conclude that avoiding expansion of cultivation into natural ecosystems, increased nitrogen use efficiency, and improved soil quality are pivotal components of a sustainable agriculture that meets human needs and protects natural resources. To achieve this outcome will depend on raising the yield potential and closing existing yield gaps of the major cereal crops to avoid yield stagnation in some of the world's most productive systems. Recent trends suggest, however, that increasing crop yield potential is a formidable scientific challenge that has proven to be an elusive goal.

INTRODUCTIONThe focus of this paper is on nitrogen-use efficiency (NUE) in cereal production systems because maize (Zea mays L.), rice (Oryza sativa L.), and wheat (Triticum aestivum L.) provide more than 60% of human dietary calories either as cereals for direct human consumption or embodied in livestock products produced from animals fed with feed grains and their by-products (http:/apps.fao.org/, agricultural production). It is likely that these same cereal crops will continue to account for the bulk of the future human food supply because they produce greater yields of human-edible food, are easily grown, stored, and transported, and require less fuel and labor for processing and cooking than other food crops. Our analysis will examine the NUE of these primary cereals in the world's major cropping systems, which also account for the majority of global N fertilizer use. We define the NUE of a cropping system as the proportion of all N inputs that are removed in harvested crop biomass, contained in recycled crop residues, and incorporated into soil organic matter and inorganic N pools. Nitrogen not recovered in these N sinks is lost from the cropping system and thus contributes to the reactive N (Nr) (1) load that cascades through environments external to the agroecosystem.Our evaluation will focus on NUE in on-farm settings because estimates of NUE from experimental plots do not accurately represent the efficiencies achieved in farmers' fields. This lack of agreement results from differences in the scale of farming operations and differences in N-management practices-some of which are only feasible in small research plots. The effect of scale not only influences N fertilizer application, but all other management operations such as tillage, seeding, weed and pest management, irrigation, and harvest, which also affect efficiency. As a result, N-fertilizer efficiency in well-managed research experiments is generally greater than the efficiency of the same practices applied by farmers in production fields. For example, the average N-fertilizer uptake efficiency (defined as the percent-

Data from farmer-managed fields have not been used previously to disentangle the impacts of daily minimum and maximum temperatures and solar radiation on rice yields in tropical/subtropical Asia. We used a multiple regression model to analyze data from 227 intensively managed irrigated rice farms in six important riceproducing countries. The farm-level detail, observed over multiple growing seasons, enabled us to construct farm-specific weather variables, control for unobserved factors that either were unique to each farm but did not vary over time or were common to all farms at a given site but varied by season and year, and obtain more precise estimates by including farm-and site-specific economic variables. Temperature and radiation had statistically significant impacts during both the vegetative and ripening phases of the rice plant. Higher minimum temperature reduced yield, whereas higher maximum temperature raised it; radiation impact varied by growth phase. Combined, these effects imply that yield at most sites would have grown more rapidly during the high-yielding season but less rapidly during the low-yielding season if observed temperature and radiation trends at the end of the 20th century had not occurred, with temperature trends being more influential. Looking ahead, they imply a net negative impact on yield from moderate warming in coming decades. Beyond that, the impact would likely become more negative, because prior research indicates that the impact of maximum temperature becomes negative at higher levels. Diurnal temperature variation must be considered when investigating the impacts of climate change on irrigated rice in Asia.T he impacts of temperature and solar radiation on rice yield remain imperfectly understood, despite decades of agronomic research. Current knowledge is based primarily on field trials and greenhouse experiments. These experimental studies indicate that increased temperature (1-4) and decreased radiation (1, 3, 5) can reduce yield, with the impacts varying across the plant's three growth phases (vegetative, establishment to panicle initiation; reproductive, panicle initiation to flowering; ripening, flowering to mature grain). Unresolved issues remain with respect to the relative impacts of temperature during daytime (T max ) vs. nighttime (T min ), potentially confounding impacts of temperature and radiation, and the magnitude of impacts in nonexperimental settings. Here, we investigate these issues by analyzing data from the largest farm-level rice study conducted in Asia since the mid-1980s. We use disaggregated data from farmer-managed fields to disentangle the impacts of T min , T max , and solar radiation on rice yield.With few exceptions (6, 7), most statistical studies on temperature and rice yield have focused on the impact of daily mean temperature (T ave ), despite evidence that that the effects of T min and T max on crop phenological development and physiological processes differ (4). It is well-established that extremely high levels of T max during flowering...

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A new maize (Zea mays L.) simulation model, Hybrid-Maize, was developed by combining the strengths of two modeling approaches: the growth and development functions in maize-specific models represented by CE-RES-Maize, and the mechanistic formulation of photosynthesis and respiration in generic crop models such as INTERCOM and WOFOST. It features temperature-driven maize phenological development, vertical canopy integration of photosynthesis, organ-specific growth respiration, and temperature-sensitive maintenance respiration. The inclusion of gross assimilation, growth respiration and maintenance respiration makes the Hybrid-Maize model potentially more responsive to changes in environmental conditions than models such as CERES-Maize. Hybrid-Maize also requires fewer genotype-specific parameters without sacrificing prediction accuracy. A linear relationship between growing degree-days (GDD) from emergence to silking and GDD from emergence to physiological maturity was used for prediction of day of silking when the former is not available. The total GDD is readily available for most commercial maize hybrids. Preliminary field evaluations at two locations under high-yielding growth conditions indicated close agreement between simulated and measured values for leaf area, dry matter accumulation, final grain and stover yields, and harvest index (HI). Key areas for further model improvement include LAI prediction at high plant density, and biomass partitioning, carbohydrate translocation, and maintenance respiration in response to above-average temperature, especially during reproductive growth. The model has not been evaluated under conditions of water and/or nutrient stress, and efforts are currently in progress to develop and validate water and nitrogen balance components for the Hybrid-Maize model.