Climate and agronomy, not genetics, underpin recent maize yield gains in favorable environments.

Quantitative understanding of factors driving yield increases of major food crops is essential for effective prioritization of research and development. Yet previous estimates had limitations in distinguishing among contributing factors such as changing climate and new agronomic and genetic technologies. Here, we distinguished the separate contribution of these factors to yield advance using an extensive database collected from the largest irrigated maize-production domain in the world located in Nebraska (United States) during the 2005-to-2018 period. We found that 48% of the yield gain was associated with a decadal climate trend, 39% with agronomic improvements, and, by difference, only 13% with improvement in genetic yield potential. The fact that these findings were so different from most previous studies, which gave much-greater weight to genetic yield potential improvement, gives urgency to the need to reevaluate contributions to yield advances for all major food crops to help guide future investments in research and development to achieve sustainable global food security. If genetic progress in yield potential is also slowing in other environments and crops, future crop-yield gains will increasingly rely on improved agronomic practices.

Filling the agronomic data gap through a minimum data collection approach.

Context: Agronomic data such as applied inputs, management practices, and crop yields are needed for assessing productivity, nutrient balances, resource use efficiency, as well as other aspects of environmental and economic performance of cropping systems. In many instances, however, these data are only available at a coarse level of aggregation or simply do not exist. Objectives: Here we developed an approach that identifies sites for agronomic data collection for a given crop and country, seeking a balance between minimizing data collection efforts and proper representation of the main crop producing areas. Methods: The developed approach followed a stratified sampling method based on a spatial framework that delineates major climate zones and crop area distribution maps, which guides selection of sampling areas (SA) until half of the national harvested area is covered. We provided proof of concept about the robustness of the approach using three rich databases including data on fertilizer application rates for maize, wheat, and soybean in Argentina, soybean in the USA, and maize in Kenya, which were collected via local experts (Argentina) and field surveys (USA and Kenya). For validation purposes, fertilizer rates per crop and nutrient derived at (sub-) national level following our approach were compared against those derived using all data collected from the whole country. Results: Application of the approach in Argentina, USA, and Kenya resulted in selection of 12, 28, and 10 SAs, respectively. For each SA, three experts or 20 fields were sufficient to give a robust estimate of average fertilizer rates applied by farmers. Average rates at national level derived from our approach compared well with those derived using the whole database ( ± 10 kg N, ± 2 kg P, ± 1 kg S, and ± 5 kg K per ha) requiring less than one third of the observations. Conclusions: The developed minimum crop data collection approach can fill the agronomic data gaps in a cost-effective way for major crop systems both in large- and small-scale systems. Significance: The proposed approach is generic enough to be applied to any crop-country combination to guide collection of key agricultural data at national and subnational levels with modest investment especially for countries that do not currently collect data.

Decoupling between leaf nitrogen and radiation use efficiency in vegetative and early reproductive stages in high-yielding soybean.

Ontogenic changes in soybean radiation use efficiency (RUE) have been attributed to variation in specific leaf nitrogen (SLN) based only on data collected during seed filling. We evaluated this hypothesis using data on leaf area, absorbed radiation (ARAD), aboveground dry matter (ADM), and plant nitrogen (N) concentration collected during the entire crop season from seven field experiments conducted in a stress-free environment. Each experiment included a full-N treatment that received ample N fertilizer and a zero-N treatment that relied on N fixation and soil N mineralization. We estimated RUE based on changes in ADM between sampling times and associated ARAD, accounting for changes in biomass composition. The RUE and SLN exhibited different seasonal patterns: a bell-shaped pattern with a peak around the beginning of seed filling, and a convex pattern followed by an abrupt decline during late seed filling, respectively. Changes in SLN explained the decline in RUE during seed filling but failed to predict changes in RUE in earlier stages and underestimated the maximum RUE observed during pod setting. Comparison between observed and simulated RUE using a process-based crop simulation model revealed similar discrepancies. The decoupling between RUE and SLN during early crop stages suggests that leaf N is above that needed to maximize crop growth but may play a role in storing N that can be used in later reproductive stages to meet the large seed N demand associated with high-yielding crops.

Luck versus Skill: Is Nitrogen Balance in Irrigated Maize Fields Driven by Persistent or Random Factors?

The nitrogen (N) balance (i.e., the difference between N inputs and grain N removal) provides an indication of potential N losses to the environment. The magnitude of the N balance in a given year reflects the influence of random (e.g., climate, pest outbreak) and/or persistent (e.g., producer skills, soil type) factors over time. We assessed here the degree to which variation in magnitude of N balance across irrigated maize fields in the US Corn Belt was explained by persistent factors and identified the underlying drivers. Fields with large N balance were identified in specific ("ranking") years, and these same fields were assessed in other ("nonranking") years. Persistent factors explained up to half of the variation in N balance, with 70% of fields with N surplus in a given year also exhibiting surplus in other years. Persistence in large N balance was associated with fields growing maize continuously and applying higher N inputs without any yield advantage compared with other fields. There was also a relationship between N balance and mismatch between producer actual and recommended N rate. These findings highlight available room to reduce N excess in producer fields via improved management, providing a starting point to set priorities and inform policy.

Insufficient nitrogen supply from symbiotic fixation reduces seasonal crop growth and nitrogen mobilization to seed in highly productive soybean crops.

Nitrogen (N) supply can limit the yields of soybean [Glycine max (L.) Merr.] in highly productive environments. To explore the physiological mechanisms underlying this limitation, seasonal changes in N dynamics, aboveground dry matter (ADM) accumulation, leaf area index (LAI) and fraction of absorbed radiation (fAPAR) were compared in crops relying only on biological N2 fixation and available soil N (zero-N treatment) versus crops receiving N fertilizer (full-N treatment). Experiments were conducted in seven high-yield environments without water limitation, where crops received optimal management. In the zero-N treatment, biological N2 fixation was not sufficient to meet the N demand of the growing crop from early in the season up to beginning of seed filling. As a result, crop LAI, growth, N accumulation, radiation-use efficiency and fAPAR were consistently higher in the full-N than in the zero-N treatment, leading to improved seed set and yield. Similarly, plants in the full-N treatment had heavier seeds with higher N concentration because of greater N mobilization from vegetative organs to seeds. Future yield gains in high-yield soybean production systems will require an increase in biological N2 fixation, greater supply of N from soil or fertilizer, or alleviation of the trade-off between these two sources of N in order to meet the plant demand.

Can sub-Saharan Africa feed itself?

Although global food demand is expected to increase 60% by 2050 compared with 2005/2007, the rise will be much greater in sub-Saharan Africa (SSA). Indeed, SSA is the region at greatest food security risk because by 2050 its population will increase 2.5-fold and demand for cereals approximately triple, whereas current levels of cereal consumption already depend on substantial imports. At issue is whether SSA can meet this vast increase in cereal demand without greater reliance on cereal imports or major expansion of agricultural area and associated biodiversity loss and greenhouse gas emissions. Recent studies indicate that the global increase in food demand by 2050 can be met through closing the gap between current farm yield and yield potential on existing cropland. Here, however, we estimate it will not be feasible to meet future SSA cereal demand on existing production area by yield gap closure alone. Our agronomically robust yield gap analysis for 10 countries in SSA using location-specific data and a spatial upscaling approach reveals that, in addition to yield gap closure, other more complex and uncertain components of intensification are also needed, i.e., increasing cropping intensity (the number of crops grown per 12 mo on the same field) and sustainable expansion of irrigated production area. If intensification is not successful and massive cropland land expansion is to be avoided, SSA will depend much more on imports of cereals than it does today.

Can ratoon cropping improve resource use efficiencies and profitability of rice in central China?

Identifying cropping systems with small global warming potential (GWP) per unit of productivity is important to ensure food security while minimizing environmental footprint. During recent decades, double-season rice (DR) systems in central China have progressively shifted into single-crop, middle-season rice (MR) due to high costs and labor requirements of double-season rice. Ratoon rice (RR) has been proposed as an alternative system that reconciliates both high annual productivity and relatively low costs and labor requirements. Here we used on-farm data collected from 240 farmer fields planted with rice in 2016 to evaluate annual energy balance, environmental impact, and net profit of MR, DR, and RR cropping systems in central China. Energy factors, emission values, and commodity prices obtained from literature and official statistics were used to estimate energy balance, GWP, and economic profit. Average annual yield was 7.7, 15.3. and 13.2 Mg ha-1 for MR, DR, and RR systems, respectively. Average total annual energy input (36 GJ ha-1), GWP (9783 kg ha-1), and production cost (3057 $ ha-1) of RR were 35-48% higher than those of MR. However, RR achieved 72-129% higher annual grain yield (13.2 Mg ha-1), net energy yield (159 GJ ha-1), and net economic return (2330 $ ha-1) than MR. Compared with DR, RR produced statistically similar net energy yield while doubling the net economic return, with 32-42% lower energy input, production costs, and GWP. Consequently, RR exhibited significantly higher net energy ratio and benefit-to-cost ratio, and substantially lower yield-scaled GWP than the other two cropping systems. In the context of DR being replaced by MR, our analysis indicated that RR can be a viable option to achieve both high annual productivity and large positive energy balance and profit, while reducing the environmental impact.

Water productivity of rainfed maize and wheat: A local to global perspective.

Water productivity (WP) is a robust benchmark for crop production in relation to available water supply across spatial scales. Quantifying water-limited potential (WPw) and actual on-farm (WPa) WP to estimate WP gaps is an essential first step to identify the most sensitive factors influencing production capacity with limited water supply. This study combines local weather, soil, and agronomic data, and crop modeling in a spatial framework to determine WPw and WPa at local and regional levels for rainfed cropping systems in 17 (maize) and 18 (wheat) major grain-producing countries representing a wide range of cropping systems, from intensive, high-yield maize in north America and wheat in west Europe to low-input, low-yield maize systems in sub-Saharan Africa and south Asia. WP was calculated as the quotient of either water-limited yield potential or actual yield, and simulated crop evapotranspiration. Estimated WPw upper limits compared well with maximum WP reported for field-grown crops. However, there was large WPw variation across regions with different climate and soil (CV = 29% for maize and 27% for wheat), which cautions against the use of generic WPw benchmarks and highlights the need for region-specific WPw. Differences in simulated evaporative demand, crop evapotranspiration after flowering, soil evaporation, and intensity of water stress around flowering collectively explained two thirds of the variation in WPw. Average WP gaps were 13 (maize) and 10 (wheat) kg ha-1 mm-1, equivalent to about half of their respective WPw. We found that non-water related factors (i.e., management deficiencies, biotic and abiotic stresses, and their interactions) constrained yield more than water supply in ca. half of the regions. These findings highlight the opportunity to produce more food with same amount of water, provided limiting factors other than water supply can be identified and alleviated with improved management practices. Our study provides a consistent protocol for estimating WP at local to regional scale, which can be used to understand WP gaps and their mitigation.

Estimating yield gaps at the cropping system level.

Yield gap analyses of individual crops have been used to estimate opportunities for increasing crop production at local to global scales, thus providing information crucial to food security. However, increases in crop production can also be achieved by improving cropping system yield through modification of spatial and temporal arrangement of individual crops. In this paper we define the cropping system yield potential as the output from the combination of crops that gives the highest energy yield per unit of land and time, and the cropping system yield gap as the difference between actual energy yield of an existing cropping system and the cropping system yield potential. Then, we provide a framework to identify alternative cropping systems which can be evaluated against the current ones. A proof-of-concept is provided with irrigated rice-maize systems at four locations in Bangladesh that represent a range of climatic conditions in that country. The proposed framework identified (i) realistic alternative cropping systems at each location, and (ii) two locations where expected improvements in crop production from changes in cropping intensity (number of crops per year) were 43% to 64% higher than from improving the management of individual crops within the current cropping systems. The proposed framework provides a tool to help assess food production capacity of new systems (e.g. with increased cropping intensity) arising from climate change, and assess resource requirements (water and N) and associated environmental footprint per unit of land and production of these new systems. By expanding yield gap analysis from individual crops to the cropping system level and applying it to new systems, this framework could also be helpful to bridge the gap between yield gap analysis and cropping/farming system design.

Prospects for Increasing Sugarcane and Bioethanol Production on Existing Crop Area in Brazil.

This article assesses sugarcane yield gaps (YG) in Brazil to determine the degree to which production can be increased without land expansion. In our scenario assessments, we evaluated how much of the projected sugarcane demand to 2024 (for both sugar and bioethanol) can be satisfied through YG closure. The current national average yield is 62% of yield potential estimated for rainfed conditions (i.e., a YG of 38%). Continuing the historical rate of yield gain is not sufficient to meet the projected demand without an area expansion by 5% and 45% for low- and high-demand scenarios, respectively. Closing the exploitable YG to 80% of potential yield would meet future sugarcane demand, with an 18% reduction in sugarcane area for the low-demand scenario or a 13% expansion for the high-demand scenario. A focus on accelerating yield gains to close current exploitable YG is a high priority for meeting future demand while minimizing pressure on additional land requirements.

High-yield maize with large net energy yield and small global warming intensity.

Addressing concerns about future food supply and climate change requires management practices that maximize productivity per unit of arable land while reducing negative environmental impact. On-farm data were evaluated to assess energy balance and greenhouse gas (GHG) emissions of irrigated maize in Nebraska that received large nitrogen (N) fertilizer (183 kg of N · ha(-1)) and irrigation water inputs (272 mm or 2,720 m(3) ha(-1)). Although energy inputs (30 GJ · ha(-1)) were larger than those reported for US maize systems in previous studies, irrigated maize in central Nebraska achieved higher grain and net energy yields (13.2 Mg · ha(-1) and 159 GJ · ha(-1), respectively) and lower GHG-emission intensity (231 kg of CO(2)e · Mg(-1) of grain). Greater input-use efficiencies, especially for N fertilizer, were responsible for better performance of these irrigated systems, compared with much lower-yielding, mostly rainfed maize systems in previous studies. Large variation in energy inputs and GHG emissions across irrigated fields in the present study resulted from differences in applied irrigation water amount and imbalances between applied N inputs and crop N demand, indicating potential to further improve environmental performance through better management of these inputs. Observed variation in N-use efficiency, at any level of applied N inputs, suggests that an N-balance approach may be more appropriate for estimating soil N(2)O emissions than the Intergovernmental Panel on Climate Change approach based on a fixed proportion of applied N. Negative correlation between GHG-emission intensity and net energy yield supports the proposition that achieving high yields, large positive energy balance, and low GHG emissions in intensive cropping systems are not conflicting goals.

How do various maize crop models vary in their responses to climate change factors?

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per °C. Doubling [CO2 ] from 360 to 720 µmol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2 ] among models. Model responses to temperature and [CO2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.

Intensifying rice production to reduce imports and land conversion in Africa.

Africa produces around 60% of the rice the continent consumes, relying heavily on rice imports to fulfill the rest of the domestic demand. Over the past 10 years, the rice-agricultural area increased nearly 40%, while average yield remained stagnant. Here we used a process-based crop simulation modelling approach combined with local weather, soil, and management datasets to evaluate the potential to increase rice production on existing cropland area in Africa and assess cropland expansion and rice imports by year 2050 for different scenarios of yield intensification. We find that Africa can avoid further increases in rice imports, and even reduce them, through a combination of cropland expansion following the historical trend together with closure of the current exploitable yield gap by half or more. Without substantial increase in rice yields, meeting future rice demand will require larger rice imports and/or land conversion than now.

Adopting yield-improving practices to meet maize demand in Sub-Saharan Africa without cropland expansion.

Maize demand in Sub-Saharan Africa is expected to increase 2.3 times during the next 30 years driven by demographic and dietary changes. Over the past two decades, the area cropped with maize has expanded by 17 million hectares in the region, with limited yield increase. Following this trend could potentially result in further maize cropland expansion and the need for imports to satisfy domestic demand. Here, we use data collected from 14,773 smallholder fields in the region to identify agronomic practices that can improve farm yield gains. We find that agronomic practices related to cultivar selection, and nutrient, pest, and crop management can double on-farm yields and provide an additional 82 million tons of maize within current cropped area. Research and development investments should be oriented towards agricultural practices with proven capacity to raise maize yields in the region.

Impact of derived global weather data on simulated crop yields.

Crop simulation models can be used to estimate impact of current and future climates on crop yields and food security, but require long-term historical daily weather data to obtain robust simulations. In many regions where crops are grown, daily weather data are not available. Alternatively, gridded weather databases (GWD) with complete terrestrial coverage are available, typically derived from: (i) global circulation computer models; (ii) interpolated weather station data; or (iii) remotely sensed surface data from satellites. The present study's objective is to evaluate capacity of GWDs to simulate crop yield potential (Yp) or water-limited yield potential (Yw), which can serve as benchmarks to assess impact of climate change scenarios on crop productivity and land use change. Three GWDs (CRU, NCEP/DOE, and NASA POWER data) were evaluated for their ability to simulate Yp and Yw of rice in China, USA maize, and wheat in Germany. Simulations of Yp and Yw based on recorded daily data from well-maintained weather stations were taken as the control weather data (CWD). Agreement between simulations of Yp or Yw based on CWD and those based on GWD was poor with the latter having strong bias and large root mean square errors (RMSEs) that were 26-72% of absolute mean yield across locations and years. In contrast, simulated Yp or Yw using observed daily weather data from stations in the NOAA database combined with solar radiation from the NASA-POWER database were in much better agreement with Yp and Yw simulated with CWD (i.e. little bias and an RMSE of 12-19% of the absolute mean). We conclude that results from studies that rely on GWD to simulate agricultural productivity in current and future climates are highly uncertain. An alternative approach would impose a climate scenario on location-specific observed daily weather databases combined with an appropriate upscaling method.

Impact of urbanization trends on production of key staple crops.

Urbanization has appropriated millions of hectares of cropland, and this trend will persist as cities continue to expand. We estimate the impact of this conversion as the amount of land needed elsewhere to give the same yield potential as determined by differences in climate and soil properties. Robust spatial upscaling techniques, well-validated crop simulation models, and soil, climate, and cropping system databases are employed with a focus on populous countries with high rates of land conversion. We find that converted cropland is 30-40% more productive than new cropland, which means that projection of food production potential must account for expected cropland loss to urbanization. Policies that protect existing farmland from urbanization would help relieve pressure on expansion of agriculture into natural ecosystems.

Climate Change and Management Impacts on Soybean N Fixation, Soil N Mineralization, N2O Emissions, and Seed Yield.

Limited knowledge about how nitrogen (N) dynamics are affected by climate change, weather variability, and crop management is a major barrier to improving the productivity and environmental performance of soybean-based cropping systems. To fill this knowledge gap, we created a systems understanding of agroecosystem N dynamics and quantified the impact of controllable (management) and uncontrollable (weather, climate) factors on N fluxes and soybean yields. We performed a simulation experiment across 10 soybean production environments in the United States using the Agricultural Production Systems sIMulator (APSIM) model and future climate projections from five global circulation models. Climate change (2020-2080) increased N mineralization (24%) and N2O emissions (19%) but decreased N fixation (32%), seed N (20%), and yields (19%). Soil and crop management practices altered N fluxes at a similar magnitude as climate change but in many different directions, revealing opportunities to improve soybean systems' performance. Among many practices explored, we identified two solutions with great potential: improved residue management (short-term) and water management (long-term). Inter-annual weather variability and management practices affected soybean yield less than N fluxes, which creates opportunities to manage N fluxes without compromising yields, especially in regions with adequate to excess soil moisture. This work provides actionable results (tradeoffs, synergies, directions) to inform decision-making for adapting crop management in a changing climate to improve soybean production systems.

Southeast Asia must narrow down the yield gap to continue to be a major rice bowl.

Southeast Asia is a major rice-producing region with a high level of internal consumption and accounting for 40% of global rice exports. Limited land resources, climate change and yield stagnation during recent years have once again raised concerns about the capacity of the region to remain as a large net exporter. Here we use a modelling approach to map rice yield gaps and assess production potential and net exports by 2040. We find that the average yield gap represents 48% of the yield potential estimate for the region, but there are substantial differences among countries. Exploitable yield gaps are relatively large in Cambodia, Myanmar, Philippines and Thailand but comparably smaller in Indonesia and Vietnam. Continuation of current yield trends will not allow Indonesia and Philippines to meet their domestic rice demand. In contrast, closing the exploitable yield gap by half would drastically reduce the need for rice imports with an aggregated annual rice surplus of 54 million tons available for export. Our study provides insights for increasing regional production on existing cropland by narrowing existing yield gaps.

A machine learning interpretation of the contribution of foliar fungicides to soybean yield in the north-central United States.

Foliar fungicide usage in soybeans in the north-central United States increased steadily over the past two decades. An agronomically-interpretable machine learning framework was used to understand the importance of foliar fungicides relative to other factors associated with realized soybean yields, as reported by growers surveyed from 2014 to 2016. A database of 2738 spatially referenced fields (of which 30% had been sprayed with foliar fungicides) was fit to a random forest model explaining soybean yield. Latitude (a proxy for unmeasured agronomic factors) and sowing date were the two most important factors associated with yield. Foliar fungicides ranked 7th out of 20 factors in terms of relative importance. Pairwise interactions between latitude, sowing date and foliar fungicide use indicated more yield benefit to using foliar fungicides in late-planted fields and in lower latitudes. There was a greater yield response to foliar fungicides in higher-yield environments, but less than a 100 kg/ha yield penalty for not using foliar fungicides in such environments. Except in a few production environments, yield gains due to foliar fungicides sufficiently offset the associated costs of the intervention when soybean prices are near-to-above average but do not negate the importance of disease scouting and fungicide resistance management.

Spatial frameworks for robust estimation of yield gaps.

Food security interventions and policies need reliable estimates of crop production and the scope to enhance production on existing cropland. Here we assess the performance of two widely used 'top-down' gridded frameworks (Global Agro-ecological Zones and Agricultural Model Intercomparison and Improvement Project) versus an alternative 'bottom-up' approach (Global Yield Gap Atlas). The Global Yield Gap Atlas estimates extra production potential locally for a number of sites representing major breadbaskets and then upscales the results to larger spatial scales. We find that estimates from top-down frameworks are alarmingly unlikely, with estimated potential production being lower than current farm production at some locations. The consequences of using these coarse estimates to predict food security are illustrated by an example for sub-Saharan Africa, where using different approaches would lead to different prognoses about future cereal self-sufficiency. Our study shows that foresight about food security and associated agriculture research priority setting based on yield potential and yield gaps derived from top-down approaches are subject to a high degree of uncertainty and would benefit from incorporating estimates from bottom-up approaches.