Do maize models capture the impacts of heat and drought stresses on yield? Using algorithm ensembles to identify successful approaches

Root proliferation adaptation strategy improved maize productivity in the US Great Plains: Insights from crop simulation model under future climate change

Adaptation measures are essential for reducing the impact of future climate risks on agricultural production systems. The present study focuses on implementing an adaptation strategy to mitigate the impact of future climate change on rainfed maize production in the Eastern Kansas River Basin (EKSRB), an important rainfed maize-producing region in the US Great Plains, which faces potential challenges of future climate risks due to a significant east-to-west aridity gradient. We used a calibrated CERES-Maize crop model to evaluate the impacts of baseline climate conditions (1985-2014), late-term future climate scenarios (under the SSP245 emission pathway and CMIP6 models), and a novel root proliferation adaptation strategy on regional maize yield and rainfall productivity. Changes in the plant root system by increasing the root density could lead to yield benefits, especially under drought conditions. Therefore, we modified the governing equation of soil root growth in the CERES-Maize model to reflect the genetic influence of a maize cultivar to improve root density by proliferation. Under baseline conditions, maize yield values ranged from 6522 to 12,849 kgha-1, with a regional average value of 9270 kgha-1. Projections for the late-term scenario indicate a substantial decline in maize yield (36 % to 50 %) and rainfall productivity (25 % to 42 %). Introducing a hypothetical maize cultivar by employing root proliferation as an adaptation strategy resulted in a 27 % increase in regional maize yield, and a 28 % increase in rainfall productivity compared to the reference cultivar without adaptation. We observed an indication of spatial dependency of maize yield and rainfall productivity on the regional precipitation gradient, with counties towards the east having an implicit advantage over those in the west. These findings offer valuable insights for the US Great Plains maize growers and breeders, guiding strategic decisions to adapt rainfed maize production to the region's impending challenges posed by climate change.

Improving resilience to high temperature in drought: water replenishment enhances sucrose and amino acid metabolisms in maize grain

Heat stress poses a significant threat to maize, especially when combined with drought. Recent research highlights the potential of water replenishment to ameliorate grain weight loss. However, the mitigating mechanisms of heat in drought stress, especially during the crucial early grain-filling stage, remain poorly understood. We investigated the mechanism for mitigating heat in drought stress by water replenishment from the 12th to the 32nd days after silking in a controlled greenhouse experiment (Exp. I) and field trial (Exp. II). A significant reduction in grain weight was observed in heat stress compared to normal conditions. When water replenishment was applied to increase soil water content (SWC) under heat stress, the grain yield exhibited a notable increase ranging from 28.4 to 76.9%. XY335 variety was used for transcriptome sequencing to analyze starch biosynthesis and amino acid metabolisms in Exp. I. With water replenishment, the transcripts of genes responsible for trehalose 6-phosphate phosphates (TPP), alpha-trehalase (TRE), ADP-glcpyrophosphorylase, and starch synthase activity were stimulated. Additionally, the expression of genes encoding TPP and TRE contributed to an enhanced conversion of trehalose to glucose. This led to the conversion of sucrose from glucose-1-phosphate to ADP-glucose and ADP-glucose to amylopectin, ultimately increasing starch production by 45.1%. Water replenishment to boost SWC during heat stress also elevated the levels of essential amino acids in maize, including arginine, serine, tyrosine, leucine, glutamic acid, and methionine, providing valuable support to maize plants in adversity. Field trials further validated the positive impact of water replenishment on SWC, resulting in a notable increase in grain yield ranging from 7.1 to 9.2%. This study highlights the vital importance of adapting to abiotic stress and underscores the necessity of developing strategies to counteract its adverse effects on crop yield.

Spatial and temporal variability of climate change impacts on ecosystem services in small agricultural catchments using the Soil and Water Assessment Tool (SWAT)

Climate change and anthropogenic activities alter the ecosystem which affects the ecosystem services (ES) associated with it. Therefore, the objective in this study is to quantify the impact of climate change on different regulation and provisioning ecosystem services. For this, we propose a modelling framework to simulate the impact of climate change on streamflow, nitrate loads, erosion, and crop yield in terms of ES indices for two agricultural catchments (Schwesnitz and Schwabach) located in Bavaria, Germany. The agro-hydrologic model Soil and Water Assessment Tool (SWAT) is used to simulate the considered ES in past (1990-2019), near future (2030-2059) and far future (2070-2099) climatic conditions. Three different bias-corrected (Representative Concentration Pathway, RCP 2.6, 4.5, and 8.5) climate projections from five different climate models retrieved from the Bavarian State Office for Environment (∼5 km) are used in this research to simulate the impact of climate change on ES. The developed SWAT models were calibrated for the major crops (1995 to 2018) present in the respective watersheds as well as for daily streamflow (1995 to 2008), which gave promising results with good PBIAS and Kling-Gupta Efficiency. The impact of climate change on erosion regulation, food and feed provisioning, and water quantity and water quality regulation were quantified in terms of indices. When using the ensemble of the five climate models, no significant impact on ES was seen due to climate change. Furthermore, the impact of climate change on different ES services from the two catchment is different. The findings of this study will be valuable for devising suitable management practices for sustainable water management at the catchment level to cope with climate change.

Future temperature extremes threaten land vertebrates

The frequency, duration, and intensity of extreme thermal events are increasing and are projected to further increase by the end of the century^1,2. Despite the considerable consequences of temperature extremes on biological systems^3-8, we do not know which species and locations are most exposed worldwide. Here we provide a global assessment of land vertebrates' exposures to future extreme thermal events. We use daily maximum temperature data from 1950 to 2099 to quantify future exposure to high frequency, duration, and intensity of extreme thermal events to land vertebrates. Under a high greenhouse gas emission scenario (Shared Socioeconomic Pathway 5-8.5 (SSP5-8.5); 4.4 °C warmer world), 41.0% of all land vertebrates (31.1% mammals, 25.8% birds, 55.5% amphibians and 51.0% reptiles) will be exposed to extreme thermal events beyond their historical levels in at least half their distribution by 2099. Under intermediate-high (SSP3-7.0; 3.6 °C warmer world) and intermediate (SSP2-4.5; 2.7 °C warmer world) emission scenarios, estimates for all vertebrates are 28.8% and 15.1%, respectively. Importantly, a low-emission future (SSP1-2.6, 1.8 °C warmer world) will greatly reduce the overall exposure of vertebrates (6.1% of species) and can fully prevent exposure in many species assemblages. Mid-latitude assemblages (desert, shrubland, and grassland biomes), rather than tropics^9,10, will face the most severe exposure to future extreme thermal events. By 2099, under SSP5-8.5, on average 3,773 species of land vertebrates (11.2%) will face extreme thermal events for more than half a year period. Overall, future extreme thermal events will force many species and assemblages into constant severe thermal stress. Deep greenhouse gas emissions cuts are urgently needed to limit species' exposure to thermal extremes.

Temporal Variation and Component Allocation Characteristics of Geometric and Physical Parameters of Maize Canopy for the Entire Growing Season

The accurate monitoring of crop parameters is important for crop yield prediction and canopy parameter inversion from remote sensing. Process-based and semi-empirical crop models are the main approaches to modeling the temporal changes in crop parameters. However, the former requires too many input parameters and the latter has the problem of poor portability. In this study, new semi-empirical geometric and physical parameters of the maize canopy model (GPMCM) crop model adapted to northeast China were proposed based on a time-series field datasets collected from 11 sites in the Nong’an and Changling Counties of Jilin Province, China, during DOY (day of year) 163 to DOY 278 in 2021. The allocation characteristics of and correlations between each maize canopy parameter were investigated for the whole growing season using the 22 algorithms of crop parameters, and the following conclusions were obtained. (1) The high correlation coefficient (R mean = 0.79) of LAI with other canopy parameters indicated that it was a good indicator for predicting other parameters. (2) Better performance was achieved by the regression method based on the two-stage simulation. The root-mean-squared error (RMSE) of geometric parameters including maize height, stem long radius, and short radius were 12.91 cm, 0.74 mm, and 0.73 mm, respectively, and the RMSE of the physical parameters including the FAGB, AGB, VWC, and RWC of the stems and leaves, ranged from 0.05 kg/m2 to 4.24 kg/m2 (2.0% to 12.9% for mean absolute percentage error (MAPE)). (3) The extension of the field-scale GPMCM to the 500 m MODIS-scale still provided a good accuracy (MAPE: 11% to 18.5%) and confirmed the feasibility of the large-scale application of the GPMCM. The proposed CPMCM can predict the temporal dynamics of maize geometric and physical parameters, and it is helpful to establish the forward and reverse models of remote sensing and improve the inversion accuracy of crop parameters.

Quantifying Agricultural Drought Severity for Spring Wheat Based on Response of Leaf Photosynthetic Features to Progressive Soil Drying

Agricultural drought definition focuses on the effect of water deficit during the crop growth period on the final crop yield. However, it is difficult to quantify the dynamic process for agricultural drought precisely during the crop growing season and then relate its impact to the final crop yield. This study was conducted to quantify agricultural drought severity for spring wheat (Triticum aestivum L.) at the jointing stage based on the response of leaf physiological parameters to progressive soil drying. The leaf potential and gas exchange parameters were observed daily using a DewPoint Potential Meter (WP4) and portable photosynthetic apparatus (LI-6400) at the jointing stage of spring wheat for two different water treatments: well water supply and natural drought, respectively. The results showed that the leaf photosynthetic features’ response to available soil water could be classified into five main stages, as the available soil water thresholds were at 0.41, 0.2, 0.12, and 0.04, respectively. We defined those five stages as no agricultural drought, mild agricultural drought, moderate agricultural drought, severe agricultural drought, and extremely severe agricultural drought based on the different mechanisms of the net photosynthesis rate’s response to progressive soil drying. The parameters of three stomatal conductance models, i.e., Ball–Berry, Leuning, and Medlyn, had two apparently different groups of values divided by moderate agricultural drought. This study combined atmosphere–soil–crop as a unit to quantify agricultural drought severity during the crop growth period could be used to model crop growth and development under water deficit conditions and calculate agricultural drought indices in drought research and management.

Environment Characterization in Sorghum (Sorghum bicolor L.) by Modeling Water-Deficit and Heat Patterns in the Great Plains Region, United States

Environmental characterization for defining the target population of environments (TPE) is critical to improve the efficiency of breeding programs in crops, such as sorghum (Sorghum bicolor L.). The aim of this study was to characterize the spatial and temporal variation for a TPE for sorghum within the United States. APSIM-sorghum, included in the Agricultural Production Systems sIMulator software platform, was used to quantify water-deficit and heat patterns for 15 sites in the sorghum belt. Historical weather data (∼35 years) was used to identify water (WSP) and heat (HSP) stress patterns to develop water-heat clusters. Four WSPs were identified with large differences in the timing of onset, intensity, and duration of the stress. In the western region of Kansas, Oklahoma, and Texas, the most frequent WSP (∼35%) was stress during grain filling with late recovery. For northeast Kansas, WSP frequencies were more evenly distributed, suggesting large temporal variation. Three HSPs were defined, with the low HSP being most frequent (∼68%). Field data from Kansas State University sorghum hybrid yield performance trials (2006-2013 period, 6 hybrids, 10 sites, 46 site × year combinations) were classified into the previously defined WSP and HSP clusters. As the intensity of the environmental stress increased, there was a clear reduction on grain yield. Both simulated and observed yield data showed similar yield trends when the level of heat or water stressed increased. Field yield data clearly separated contrasting clusters for both water and heat patterns (with vs. without stress). Thus, the patterns were regrouped into four categories, which account for the observed genotype by environment interaction (GxE) and can be applied in a breeding program. A better definition of TPE to improve predictability of GxE could accelerate genetic gains and help bridge the gap between breeders, agronomists, and farmers.

USA Crop Yield Estimation with MODIS NDVI: Are Remotely Sensed Models Better than Simple Trend Analyses?

Crop yield forecasting is performed monthly during the growing season by the United States Department of Agriculture’s National Agricultural Statistics Service. The underpinnings are long-established probability surveys reliant on farmers’ feedback in parallel with biophysical measurements. Over the last decade though, satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been used to corroborate the survey information. This is facilitated through the Global Inventory Modeling and Mapping Studies/Global Agricultural Monitoring system, which provides open access to pertinent real-time normalized difference vegetation index (NDVI) data. Hence, two relatively straightforward MODIS-based modeling methods are employed operationally. The first model constitutes mid-season timing based on the maximum peak NDVI value, while the second is reflective of late-season timing by integrating accumulated NDVI over a threshold value. Corn model results nationally show the peak NDVI method provides a R2 of 0.88 and a coefficient of variation (CV) of 3.5%. The accumulated method, using an optimally derived 0.58 NDVI threshold, improves the performance to 0.93 and 2.7%, respectively. Both these models outperform simple trend analysis, which is 0.48 and 7.4%, correspondingly. For soybeans the R2 results of the peak NDVI model are 0.62, and 0.73 for the accumulated using a 0.56 threshold. CVs are 6.8% and 5.7%, respectively. Spring wheat’s R2 performance with the accumulated NDVI model is 0.60 but just 0.40 with peak NDVI. The soybean and spring wheat models perform similarly to trend analysis. Winter wheat and upland cotton show poor model performance, regardless of method. Ultimately, corn yield forecasting derived from MODIS imagery is robust, and there are circumstances when forecasts for soybeans and spring wheat have merit too.

Using Hybrid Artificial Intelligence and Evolutionary Optimization Algorithms for Estimating Soybean Yield and Fresh Biomass Using Hyperspectral Vegetation Indices

Recent advanced high-throughput field phenotyping combined with sophisticated big data analysis methods have provided plant breeders with unprecedented tools for a better prediction of important agronomic traits, such as yield and fresh biomass (FBIO), at early growth stages. This study aimed to demonstrate the potential use of 35 selected hyperspectral vegetation indices (HVI), collected at the R5 growth stage, for predicting soybean seed yield and FBIO. Two artificial intelligence algorithms, ensemble-bagging (EB) and deep neural network (DNN), were used to predict soybean seed yield and FBIO using HVI. Considering HVI as input variables, the coefficients of determination (R2) of 0.76 and 0.77 for yield and 0.91 and 0.89 for FBIO were obtained using DNN and EB, respectively. In this study, we also used hybrid DNN-SPEA2 to estimate the optimum HVI values in soybeans with maximized yield and FBIO productions. In addition, to identify the most informative HVI in predicting yield and FBIO, the feature recursive elimination wrapper method was used and the top ranking HVI were determined to be associated with red, 670 nm and near-infrared, 800 nm, regions. Overall, this study introduced hybrid DNN-SPEA2 as a robust mathematical tool for optimizing and using informative HVI for estimating soybean seed yield and FBIO at early growth stages, which can be employed by soybean breeders for discriminating superior genotypes in large breeding populations.

Machine Learning-Based Modeling of Spatio-Temporally Varying Responses of Rainfed Corn Yield to Climate, Soil, and Management in the U.S. Corn Belt

Better understanding the variabilities in crop yield and production is critical to assessing the vulnerability and resilience of food production systems. Both environmental (climatic and edaphic) conditions and management factors affect the variabilities of crop yield. In this study, we conducted a comprehensive data-driven analysis in the U.S. Corn Belt to understand and model how rainfed corn yield is affected by climate variability and extremes, soil properties (soil available water capacity, soil organic matter), and management practices (planting date and fertilizer applications). Exploratory data analyses revealed that corn yield responds non-linearly to temperature, while the negative vapor pressure deficit (VPD) effect on corn yield is monotonic and more prominent. Higher mean yield and inter-annual yield variability are found associated with high soil available water capacity, while lower inter-annual yield variability is associated with high soil organic matter (SOM). We also identified region-dependent relationships between planting date and yield and a strong correlation between planting date and the April weather condition (temperature and rainfall). Next, we built machine learning models using the random forest and LASSO algorithms, respectively, to predict corn yield with all climatic, soil properties, and management factors. The random forest model achieved a high prediction accuracy for annual yield at county level as early as in July (R² = 0.781) and outperformed LASSO. The gained insights from this study lead to improved understanding of how corn yield responds to climate variability and projected change in the U.S. Corn Belt and globally.

Non-stationary response of rain-fed spring wheat yield to future climate change in northern latitudes

The non-stationary response of crop growth to changes in hydro-climatic variables makes yield projection uncertain and the design and implementation of adaptation strategies debatable. This study simulated the time-varying behavior of the underlying cause-and-effect mechanisms affecting spring wheat yield (SWY) under various climate change and nitrogen (N) application scenarios in the Red Deer River basin in agricultural lands of the western Canadian Prairies. A calibrated and validated Soil and Water Assessment Tool and Analysis of Variance decomposition methods were utilized to assess the contribution of crop growth parameters, Global Climate Models, Representative Concentration Pathways, and downscaling techniques to the total SWY variance for the 2040-2064 period. The results showed that the cause-and-effect mechanisms, driving crop yield, shifted from water stress (W-stress) dominated (27 days of W-stress days) during the historical period to nitrogen stress (N-stress) dominated (27 to 35 N-stress days) in the future period. It was shown that while higher precipitation, warmer weather, and early snowmelts, along with elevated CO₂ may favor SWY in cold regions in the future (up to 50% more yields in some sub-basins), the yield potentials may be limited by N-stress (only up to 0.7% yield increase in some sub-basins). The N-stress might be partially related to the N deficiency in the soil, which can be compensated by N fertilizer application. However, inadequate N uptake due to limited evapotranspiration under elevated atmospheric CO₂ might pose restrictions to SWY potentials even in the least N deficient regions. This study uncovers important information on the understanding of spatiotemporal variability of hydrogeochemical processes driving crop yields and the non-stationary response of yields to changing climate. The results also underscore spatiotemporal variability of N-stress due to N deficiency in the soil or N uptake by crops, both of which may restrain SWY by changes in atmospheric CO₂ concentrations in the future.

Changes in the drought sensitivity of US maize yields

As climate change leads to increased frequency and severity of drought in many agricultural regions, a prominent adaptation goal is to reduce the drought sensitivity of crop yields. Yet many of the sources of average yield gains are more effective in good weather, leading to heightened drought sensitivity. Here we consider two empirical strategies for detecting changes in drought sensitivity and apply them to maize in the United States, a crop that has experienced myriad management changes including recent adoption of drought-tolerant varieties. We show that a strategy that utilizes weather-driven temporal variations in drought exposure is inconclusive because of the infrequent occurrence of substantial drought. In contrast, a strategy that exploits within-county spatial variability in drought exposure, driven primarily by differences in soil water storage capacity, reveals robust trends over time. Yield sensitivity to soil water storage increased by 55% on average across the US Corn Belt since 1999, with larger increases in drier states. Although yields have been increasing under all conditions, the cost of drought relative to good weather has also risen. These results highlight the difficulty of simultaneously raising average yields and lowering drought sensitivity.

Potassium Application Improves Grain Yield and Alleviates Drought Susceptibility in Diverse Maize Hybrids

Maize (Zea mays L.) is an important component of global food security but its production is threatened by abiotic stresses in climate change scenarios, especially drought stress. Many multinational companies have introduced maize hybrids worldwide which have variable performance under diverse environmental conditions. The maize production is likely to be affected by a future water crisis. Potassium (K) is a well-known macronutrient which improves the performance of cereals under abiotic stresses. In this field experiment, we assessed the influence of soil applied K on the productivity of diverse maize hybrids grown under well-watered and drought stress conditions. The study consisted of three K levels viz., control (no KCl), KCl at 50 kg ha⁻¹, and KCI at 75 kg ha⁻¹ factorally combined with two irrigation levels (i.e., normal recommended irrigation, well-watered condition, and half of the recommended irrigation, drought stress condition) and eight maize hybrids. Irrigation was kept in main plots, potassium in subplot, and maize hybrids in sub-subplots. The results revealed that performance of the maize hybrids was significantly influenced by all three factors, and the interaction of irrigation with potassium and irrigation with hybrids was significant; results being non-significant for all other interactions. Potassium application improved yield traits and water productivity under both normal and water stress conditions but effect was more prominent under water stress conditions than normal conditions. Potassium application also alleviated drought susceptibility of all maize hybrids. In all cases, the performance of maize hybrids was maximum under potassium application at 75 kg ha⁻¹.

Response of maize biomass and soil water fluxes on elevated CO2 and drought-From field experiments to process-based simulations

The rising concentration of atmospheric carbon dioxide (CO₂ ) is known to increase the total aboveground biomass of several C3 crops, whereas C4 crops are reported to be hardly affected when water supply is sufficient. However, a free-air carbon enrichment (FACE) experiment in Braunschweig, Germany, in 2007 and 2008 resulted in a 25% increased biomass of the C4 crop maize under restricted water conditions and elevated CO₂ (550 ppm). To project future yields of maize under climate change, an accurate representation of the effects of eCO₂ and drought on biomass and soil water conditions is essential. Current crop growth models reveal limitations in simulations of maize biomass under eCO₂ and limited water supply. We use the coupled process-based hydrological-plant growth model Catchment Modeling Framework-Plant growth Modeling Framework to overcome this limitation. We apply the coupled model to the maize-based FACE experiment in Braunschweig that provides robust data for the investigation of combined CO₂ and drought effects. We approve hypothesis I that CO₂ enrichment has a small direct-fertilizing effect with regard to the total aboveground biomass of maize and hypothesis II that CO₂ enrichment decreases water stress and leads to higher yields of maize under restricted water conditions. Hypothesis III could partly be approved showing that CO₂ enrichment decreases the transpiration of maize, but does not raise soil moisture, while increasing evaporation. We emphasize the importance of plant-specific CO₂ response factors derived by use of comprehensive FACE data. By now, only one FACE experiment on maize is accomplished applying different water levels. For the rigorous testing of plant growth models and their applicability in climate change studies, we call for datasets that go beyond single criteria (only yield response) and single effects (only elevated CO₂ ).

Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States

Increasing drought and extreme rainfall are major threats to maize production in the United States. However, compared to drought impact, the impact of excessive rainfall on crop yield remains unresolved. Here, we present observational evidence from crop yield and insurance data that excessive rainfall can reduce maize yield up to -34% (-17 ± 3% on average) in the United States relative to the expected yield from the long-term trend, comparable to the up to -37% loss by extreme drought (-32 ± 2% on average) from 1981 to 2016. Drought consistently decreases maize yield due to water deficiency and concurrent heat, with greater yield loss for rainfed maize in wetter areas. Excessive rainfall can have either negative or positive impact on crop yield, and its sign varies regionally. Excessive rainfall decreases maize yield significantly in cooler areas in conjunction with poorly drained soils, and such yield loss gets exacerbated under the condition of high preseason soil water storage. Current process-based crop models cannot capture the yield loss from excessive rainfall and overestimate yield under wet conditions. Our results highlight the need for improved understanding and modeling of the excessive rainfall impact on crop yield.

Dissecting the nonlinear response of maize yield to high temperature stress with model-data integration

Evidence suggests that global maize yield declines with a warming climate, particularly with extreme heat events. However, the degree to which important maize processes such as biomass growth rate, growing season length (GSL) and grain formation are impacted by an increase in temperature is uncertain. Such knowledge is necessary to understand yield responses and develop crop adaptation strategies under warmer climate. Here crop models, satellite observations, survey, and field data were integrated to investigate how high temperature stress influences maize yield in the U.S. Midwest. We showed that both observational evidence and crop model ensemble mean (MEM) suggests the nonlinear sensitivity in yield was driven by the intensified sensitivity of harvest index (HI), but MEM underestimated the warming effects through HI and overstated the effects through GSL. Further analysis showed that the intensified sensitivity in HI mainly results from a greater sensitivity of yield to high temperature stress during the grain filling period, which explained more than half of the yield reduction. When warming effects were decomposed into direct heat stress and indirect water stress (WS), observational data suggest that yield is more reduced by direct heat stress (-4.6 ± 1.0%/°C) than by WS (-1.7 ± 0.65%/°C), whereas MEM gives opposite results. This discrepancy implies that yield reduction by heat stress is underestimated, whereas the yield benefit of increasing atmospheric CO₂ might be overestimated in crop models, because elevated CO₂ brings yield benefit through water conservation effect but produces limited benefit over heat stress. Our analysis through integrating data and crop models suggests that future adaptation strategies should be targeted at the heat stress during grain formation and changes in agricultural management need to be better accounted for to adequately estimate the effects of heat stress.

Projected climate and agronomic implications for corn production in the Northeastern United States

Corn has been a pillar of American agriculture for decades and continues to receive much attention from the scientific community for its potential to meet the food, feed and fuel needs of a growing human population in a changing climate. By midcentury, global temperature increase is expected to exceed 2°C where local effects on heat, cold and precipitation extremes will vary. The Northeast United States is a major dairy producer, corn consumer, and is cited as the fastest warming region in the contiguous U.S. It is important to understand how key agronomic climate variables affect corn growth and development so that adaptation strategies can be tailored to local climate changes. We analyzed potential local effects of climate change on corn growth and development at three major dairy locations in the Northeast (Syracuse, New York; State College, Pennsylvania and Landisville, Pennsylvania) using downscaled projected climate data (2000-2100) from nine Global Climate Models under two emission pathways (Representative Concentration Pathways (RCP) 4.5 and 8.5). Our analysis indicates that corn near the end of the 21st century will experience fewer spring and fall freezes, faster rate of growing degree day accumulation with a reduction in time required to reach maturity, greater frequencies of daily high temperature ≥35°C during key growth stages such as silking-anthesis and greater water deficit during reproductive (R1-R6) stages. These agronomic anomalies differ between the three locations, illustrating varying impacts of climate change in the more northern regions vs. the southern regions of the Northeast. Management strategies such as shifting the planting dates based on last spring freeze and irrigation during the greatest water deficit stages (R1-R6) will partially offset the projected increase in heat and drought stress. Future research should focus on understanding the effects of global warming at local levels and determining adaptation strategies that meet local needs.

Translating High-Throughput Phenotyping into Genetic Gain

Inability to efficiently implement high-throughput field phenotyping is increasingly perceived as a key component that limits genetic gain in breeding programs. Field phenotyping must be integrated into a wider context than just choosing the correct selection traits, deployment tools, evaluation platforms, or basic data-management methods. Phenotyping means more than conducting such activities in a resource-efficient manner; it also requires appropriate trial management and spatial variability handling, definition of key constraining conditions prevalent in the target population of environments, and the development of more comprehensive data management, including crop modeling. This review will provide a wide perspective on how field phenotyping is best implemented. It will also outline how to bridge the gap between breeders and 'phenotypers' in an effective manner.

Increasing drought and diminishing benefits of elevated carbon dioxide for soybean yields across the US Midwest

Elevated atmospheric CO2 concentrations ([CO2 ]) are expected to increase C3 crop yield through the CO2 fertilization effect (CFE) by stimulating photosynthesis and by reducing stomatal conductance and transpiration. The latter effect is widely believed to lead to greater benefits in dry rather than wet conditions, although some recent experimental evidence challenges this view. Here we used a process-based crop model, the Agricultural Production Systems sIMulator (APSIM), to quantify the contemporary and future CFE on soybean in one of its primary production area of the US Midwest. APSIM accurately reproduced experimental data from the Soybean Free-Air CO2 Enrichment site showing that the CFE declined with increasing drought stress. This resulted from greater radiation use efficiency (RUE) and above-ground biomass production at elevated [CO2 ] that outpaced gains in transpiration efficiency (TE). Using an ensemble of eight climate model projections, we found that drought frequency in the US Midwest is projected to increase from once every 5 years currently to once every other year by 2050. In addition to directly driving yield loss, greater drought also significantly limited the benefit from rising [CO2 ]. This study provides a link between localized experiments and regional-scale modeling to highlight that increased drought frequency and severity pose a formidable challenge to maintaining soybean yield progress that is not offset by rising [CO2 ] as previously anticipated. Evaluating the relative sensitivity of RUE and TE to elevated [CO2 ] will be an important target for future modeling and experimental studies of climate change impacts and adaptation in C3 crops.

The combined and separate impacts of climate extremes on the current and future US rainfed maize and soybean production under elevated CO2

Heat and drought are two emerging climatic threats to the US maize and soybean production, yet their impacts on yields are collectively determined by the magnitude of climate change and rising atmospheric CO2 concentrations. This study quantifies the combined and separate impacts of high temperature, heat and drought stresses on the current and future US rainfed maize and soybean production and for the first time characterizes spatial shifts in the relative importance of individual stress. Crop yields are simulated using the Agricultural Production Systems Simulator (APSIM), driven by high-resolution (12 km) dynamically downscaled climate projections for 1995-2004 and 2085-2094. Results show that maize and soybean yield losses are prominent in the US Midwest by the late 21st century under both Representative Concentration Pathway (RCP) 4.5 and RCP8.5 scenarios, and the magnitude of loss highly depends on the current vulnerability and changes in climate extremes. Elevated atmospheric CO2 partially but not completely offsets the yield gaps caused by climate extremes, and the effect is greater in soybean than in maize. Our simulations suggest that drought will continue to be the largest threat to US rainfed maize production under RCP4.5 and soybean production under both RCP scenarios, whereas high temperature and heat stress take over the dominant stress of drought on maize under RCP8.5. We also reveal that shifts in the geographic distributions of dominant stresses are characterized by the increase in concurrent stresses, especially for the US Midwest. These findings imply the importance of considering heat and drought stresses simultaneously for future agronomic adaptation and mitigation strategies, particularly for breeding programs and crop management. The modeling framework of partitioning the total effects of climate change into individual stress impacts can be applied to the study of other crops and agriculture systems.