We're being warned of future grain failures—not by the dreams of a biblical Pharaoh, but by modern computer model predictions. Climate science forecasts rising temperatures, changing rainfall patterns, and episodes of increasingly extreme weather, which will harm crop yields at a time when the world's growing population can ill afford declines, especially in its most productive areas, such as the US Midwest. In order to adequately prepare, we call for the establishment of a new field research network across the US Midwest to fully integrate all methods for improving cropping systems and leveraging big data (agronomic, economic, environmental, and genomic) to facilitate adaptation and mitigation. Such a network, placed in one of the most important grain-producing areas in the world, would provide the set of experimental facilities, linked to farm settings, needed to explore and test the adaptation and mitigation strategies that already are needed globally.

David Lobell’s recent research indicates that negative impacts to the global agriculture system are much more likely, more severe and wider-ranging in the face of human-caused climate change. Temperature increases are the main drier behind these far-reaching impacts.. There are several pathways toward adaptation, though none of them appears to completely offset the losses. Research highlighted in this brief offers insights for institutions and decisionmakers concerned with protecting food security and international stability throughout the coming decades.

Climate change can reduce crop yields and thereby threaten food security. The current measures used to adapt to climate change involve avoiding crops yield decrease, however, the limitations of such measures due to water and other resources scarcity have not been well understood. Here, we quantify how the sensitivity of maize to water availability has increased because of the shift toward longer-maturing varieties during last three decades in the Chinese Maize Belt (CMB). We report that modern, longer-maturing varieties have extended the growing period by an average of 8 days and have significantly offset the negative impacts of climate change on yield. However, the sensitivity of maize production to water has increased: maize yield across the CMB was 5% lower with rainfed than with irrigated maize in the 1980s and was 10% lower (and even >20% lower in some areas) in the 2000s because of both warming and the increased requirement for water by the longer-maturing varieties. Of the maize area in China, 40% now fails to receive the precipitation required to attain the full yield potential. Opportunities for water saving in maize systems exist, but water scarcity in China remains a serious problem.

In this paper we discuss the scope of the adaptation challenge facing world agriculture in the coming decades. Due to rising temperatures throughout the tropics, pressures for adaptation will be greatest in some of the poorest parts of the world where the adaptive capacity is least abundant. We discuss both autonomous (market driven) and planned adaptations, distinguishing: (a) those that can be undertaken with existing technology, (b) those that involve development of new technologies, and (c) those that involve institutional/market and policy reforms. The paper then proceeds to identify which of these adaptations are currently modeled in integrated assessment studies and related analyses at global scale. This, in turn, gives rise to recommendations about how these models should be modified in order to more effectively capture climate change adaptation in the farm and food sector. In general, we find that existing integrated assessment models are better suited to analyzing adaptation by relatively well-endowed producers operating in market-integrated, developed countries. They likely understate climate impacts on agriculture in developing countries, while overstating the potential adaptations. This is troubling, since the need for adaptation will be greatest amongst the lower income producers in the poorest tropical countries. This is also where policies and public investments are likely to have the highest payoff. We conclude with a discussion of opportunities for improving the empirical foundations of integrated assessment modeling with an emphasis on the poorest countries.

We assess the benefits of climate change mitigation for global maize and wheat production over the 21st century by comparing outcomes under RCP4.5 and RCP8.5 as simulated by two large initial condition ensembles from NCAR’s Community Earth System Model. We use models of the relation between climate variables, CO2 concentrations, and yields built on observations and then project this relation on the basis of simulated future temperature and precipitation and CO2 trajectories under the two scenarios, for short (2021–2040), medium (2041–2060) and long (2061–2080) time horizons. We focus on projected mean yield impacts, chances of significant slowdowns in yield, and exposure to damaging heat during critical periods of the growing seasons, the last of which is not explicitly considered in yield impacts by most models, including ours. We find that substantial benefits from mitigation would be achieved throughout the 21st century for maize, in terms of reducing (1) the size of average yield impacts, with mean losses for maize under RCP8.5 reduced under RCP4.5 by about 25 %, 40 % and 50 % as the time horizon lengthens over the 21st century; (2) the risk of major slowdowns over a 10 or 20 year period, with maize chances under RCP4.5 being reduced up to ~75 % by the end of the century compared to those estimated under RCP8.5; and (3) exposure to critical or “lethal” heat extremes, with the number of extremely hot days under RCP8.5 roughly triple current levels by end of century, compared to a doubling for RCP4.5. For wheat, we project small or occasionally negative effects of mitigation for projected yields, because of stronger CO2 fertilization effects than in maize, but substantial benefits of mitigation remain in terms of exposure to extremely high temperatures.

Substantial gaps have been reported between the average farmer’s maize yield and yield potential in China, especially the North China Plain (NCP). This maize yield gap as identified by previous studies indicates large opportunities for raising yield by improving agronomy. Agronomic factors are either transient or persistent. Transient factors, which explain yield differences depending on unpredictable weather conditions, can have significantly different optimums from one year to another. While those transient factors are difficult to improve without reliable forecasts, persistent factors influence yield more consistently and therefore represent the best near-term targets for shrinking yield gaps. In this study, multi-year satellite images are used to quantify field-scale maize yield variation in Quzhou County of NCP, and this variation is then analyzed to determine the role of soil type and persistent management factors in explaining yield gaps. Results indicate that (i) remote sensing can provide reasonably reliable estimates of maize yields in this region; (ii) soil type has a clear effect on maize yields, and one that interacts strongly with growing season rainfall amounts; and (iii) on average roughly 20% of yield differences that appear within any one year are related to factors that persist in other years. Overall, the study presents a generalizable methodology of assessing yield gap as well as the proportion arising from persistent factors using satellite data. Our results suggest that the majority of yield gap is dominated by transient factors, and shrinking this gap may require high quality forecasts to make informed optimal management decisions.

Crop yield gap (Yg) can be disaggregated into two components: (i) one that is consistent across years and is, therefore, attributable to persistent factors that limit yields, and (ii) a second that varies from year to year due to inconsistent constraints on yields. Quantifying relative contributions of persistent and non-persistent factors to overall Yg, and identifying their underpinning causes, can help identify sound interventions to narrow current Yg and estimate magnitude of likely impact. The objective of this study was to apply this analytical framework to quantify the contribution of persistent factors to current Yg in high-yield irrigated maize systems in western US Corn Belt and identify some of the underpinning explanatory factors. We used a database containing producer yields collected during 10 years (2004–2013) from ca. 3000 irrigated fields in three regions of the state of Nebraska (USA). Yield potential was estimated for each region-year using a crop simulation model and actual weather and management data. Yg was calculated for each field-year as the difference between simulated yield potential and field yield. Two independent sources of field yield data were used: (i) producer-reported yields, and (ii) estimated yields using a combined satellite-crop model approach that does not rely on actual yield data. In each year (hereafter called ‘ranking years’), fields were grouped into ‘small’ and ‘large’ Yg categories. For a given category, Yg persistence was calculated by comparing mean Yg estimated for ranking years against mean Yg calculated, for the same group of fields, for the rest of the years. Explanatory factors for persistent Yg were assessed. Yg persistence ranged between ca. 30% and 50% across regions, with higher persistence in regions with heterogeneous soils. Estimates of Yg size and persistence based on producer-reported yields and satellite-model approach were in reasonable agreement, though the latter approach consistently underestimated Yg size and persistence. Small Yg category exhibited a higher frequency of fields with favorable soils and soybean-maize rotation and greater N fertilizer and irrigation inputs relative to the large Yg category. Remarkably, despite higher applied inputs, efficiencies in the use of N fertilizer, irrigation, and solar radiation were much higher in fields exhibiting small Yg. The framework implemented in this study can be applied to any cropping system for which a reasonable number of field-year yield and management data are available.