Evidence for a weakening strength of temperature-corn yield relation in the United States during 1980-2010

Physiological insights into sulfate and selenium interaction to improve drought tolerance in mung bean

The present study involved two pot experiments to investigate the response of mung bean to the individual or combined SO₄ ^2- and selenate application under drought stress. A marked increment in biomass and NPK accumulation was recorded in mung bean seedlings fertilized with various SO₄ ^2- sources, except for CuSO₄. Compared to other SO₄ ^2- fertilizers, ZnSO₄ application resulted in the highest increase in growth attributes and shoot nutrient content. Further, the combined S and Se application (S + Se) significantly enhanced relative water content (16%), SPAD value (72%), photosynthetic rate (80%) and activities of catalase (79%), guaiacol peroxidase (53%) and superoxide dismutase (58%) in the leaves of water-stressed mung bean plants. Consequently, the grain yield of mung bean was markedly increased by 105% under water stress conditions. Furthermore, S + Se application considerably increased the concentrations of P (47%), K (75%), S (80%), Zn (160%), and Fe (15%) in mung bean seeds under drought stress conditions. These findings indicate that S + Se application potentially increases the nutritional quality of grain legumes by stimulating photosynthetic apparatus and antioxidative machinery under water deficit conditions. Our results could provide the basis for further experiments on cross-talk between S and Se regulatory pathways to improve the nutritional quality of food crops.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12298-021-00992-6.

Pepper CaMLO6 Negatively Regulates Ralstonia solanacearum Resistance and Positively Regulates High Temperature and High Humidity Responses

Plant mildew-resistance locus O (MLO) proteins influence susceptibility to powdery mildew. However, their roles in plant responses to other pathogens and heat stress remain unclear. Here, we showed that CaMLO6, a pepper (Capsicum annuum) member of MLO clade V, is a protein targeted to plasma membrane and probably endoplasmic reticulum. The transcript expression level of CaMLO6 was upregulated in the roots and leaves of pepper plants challenged with high temperature and high humidity (HTHH) and was upregulated in leaves but downregulated in roots of plants infected with the bacterial pathogen Ralstonia solanacearum. CaMLO6 was also directly upregulated by CaWRKY40 upon HTHH but downregulated by CaWRKY40 upon R. solanacearum infection. Virus-induced gene silencing of CaMLO6 significantly decreased pepper HTHH tolerance and R. solanacearum susceptibility. Moreover, CaMLO6 overexpression enhanced the susceptibility of Nicotiana benthamiana and pepper plants to R. solanacearum and their tolerance to HTHH, effects that were associated with the expression of immunity- and thermotolerance-associated marker genes, respectively. These results suggest that CaMLO6 acts as a positive regulator in response to HTHH but a negative regulator in response to R. solanacearum. Moreover, CaMLO6 is transcriptionally affected by R. solanacearum and HTHH; these transcriptional responses are at least partially regulated by CaWRKY40.

Predicting spatial and temporal variability in crop yields: an inter-comparison of machine learning, regression and process-based models

Pervious assessments of crop yield response to climate change are mainly aided with either process-based models or statistical models, with a focus on predicting the changes in average yields, whilst there is growing interest in yield variability and extremes. In this study, we simulate US maize yield using process-based models, traditional regression model and a machine-learning algorithm, and importantly, identify the weakness and strength of each method in simulating the average, variability and extremes of maize yield across the country. We show that both regression and machine learning models can well reproduce the observed pattern of yield averages, while large bias is found for process-based crop models even fed with harmonized parameters. As for the probability distribution of yields, machine learning shows the best skill, followed by regression model and process-based models. For the country as a whole, machine learning can explain 93% of observed yield variability, followed by regression model (51%) and process-based models (42%). Based on the improved capability of the machine learning algorithm, we estimate that US maize yield is projected to decrease by 13.5% under the 2°C global warming scenario (by ~2050s). Yields less than or equal to the 10^th percentile in the yield distribution for the baseline period are predicted to occur in 19% and 25% of years in 1.5°C (by ~2040s) and 2°C global warming scenarios, with potentially significant implications for food supply, prices and trade. The machine learning and regression methods are computationally much more efficient than process-based models, making it feasible to do probabilistic risk analysis of climate impacts on crop production for a wide range of future scenarios.

Probabilistic evaluation of the impact of compound dry-hot events on global maize yields

Weather and climate extremes, such as droughts and hot extremes, may result in marked damages to crop yields and threaten regional and global food security. Understanding the relationship between climate extremes and crop yields is of critical importance for food security under a changing climate. The objective of this study is to investigate the probabilistic variability of maize yields with respect to compound dry-hot events, which has been shown to be more stressful to crops compared with individual dry or hot events. A multivariate model is first constructed to model the joint behavior of the dry condition, hot condition, and crop yields. The response of crop yields under different dry, hot, and compound dry-hot conditions at national and global scales is then investigated based on the conditional distribution. For the major maize producing countries (top 5), the probability of maize yield reduction could increase by from 0.07 to 0.31 (from 0.04 to 0.31) when the individual extreme drought (extreme hot) conditions changed to compound dry-hot conditions. The probabilistic evaluation of compound dry-hot events' impacts on maize yields is expected to provide useful insights for the mitigation of compound events and their impacts under a changing climate.

Recent changes in county-level maize production in the United States: Spatial-temporal patterns, climatic drivers and the implications for crop modelling

Despite the fact that it is the total crop production that shapes future food supply rather than one of its single component, previous studies have mainly focused on the changes in crop yield. It is possible that recent gains in crop production are mainly due to improvement of yield rather than growth of harvest area. However, it remains unclear about the geographical patterns of their relative contributions at fine scales and the possible mechanisms. Analysis of US maize production shows that maize production has increased significantly at a rate of 2.1%/year during 1980-2010. Although yield is the dominant factor contributing to production growth for the country as a whole, the importance of harvest area has become more evident with time. In 56% of US's maize growing counties, harvest area has also contributed more than yield to production changes. High spatial correlation between the change rates of harvest area and production is observed (R = 0.96), while a weak relation (R = 0.21) is found between the spatial patterns of yield and production. This suggests that harvest area has exerted the dominant role in modulating the spatial distribution pattern of maize production changes. Further analysis suggests that yield and harvest area respond differently to climate variability, which has great implications for adaptation strategies. Comparing 11 state-of-the-art crop model simulations against census data reveals large bias in the simulated spatial patterns of maize production. Nevertheless, such bias can be reduced substantially by incorporating the observed dynamics of harvest area, pointing to a potential pathway for future model improvement. This study highlights the importance of accounting for harvest area dynamics in assessing agricultural production empirically or with crop models.

Crop yield sensitivity of global major agricultural countries to droughts and the projected changes in the future

Understanding the potential drought impacts on agricultural production is critical for ensuring global food security. Instead of providing a deterministic estimate, this study investigates the likelihood of yield loss of wheat, maize, rice and soybeans in response to droughts of various intensities in the 10 largest producing countries. We use crop-country specific standardized precipitation index (SPI) and census yield data for 1961-2016 to build a probabilistic modeling framework for estimating yield loss risk under a moderate (-1.2 < SPI < -0.8), severe (-1.5 < SPI < -1.3), extreme (-1.9 < SPI < -1.6) and exceptional (SPI < -2.0) drought. Results show that there is >80% probability that wheat production will fall below its long-term average when experiencing an exceptional drought, especially in USA and Canada. As for maize, India shows the highest risk of yield reduction under droughts, while rice is the crop that is most vulnerable to droughts in Vietnam and Thailand. Risk of drought-driven soybean yield loss is the highest in USA, Russian and India. Yield loss risk tends to grow faster when experiencing a shift in drought severity from moderate to severe than that from extreme to the exceptional category, demonstrating the non-linear response of yield to the increase in drought severity. Sensitivity analysis shows that temperature plays an important role in determining drought impacts, through reducing or amplifying drought-driven yield loss risk. Compared to present conditions, an ensemble of 11 crop models simulated an increase in yield loss risk by 9%-12%, 5.6%-6.3%, 18.1%-19.4% and 15.1%-16.1 for wheat, maize, rice and soybeans by the end of 21st century, respectively, without considering the benefits of CO₂ fertilization and adaptations. This study highlights the non-linear response of yield loss risk to the increase in drought severity. This implies that adaptations should be more targeted, considering not only the crop type and region but also the specific drought severity of interest.

Keeping global warming within 1.5 °C reduces future risk of yield loss in the United States: A probabilistic modeling approach

This study assess the possible outcomes of yield changes in the United States which is responsible for 40% of global maize supply under 1.5 °C and 2 °C global warming scenarios. Instead of providing deterministic estimates, this study introduces a probability-based approach that allow for examination of the associated probability of each outcome, which has great implications for decision-makings. Results show distinct spatial patterns in future yield loss risk associated with temperature rise at the county scale, with highest probability in central and southeastern US, and lowest risk in western US and high production regions such as Iowa. Comparing the estimates under 1.5 °C global warming against that in 2.0 °C warming indicates that keeping global warming within 1.5 °C has great benefits for reducing future yield loss risk. Based on the ensemble mean of 97 climate model simulations, the risk of yield dropping below historical long-term mean is projected to decrease from 81% to 75% for the country as a whole. Such benefit is more evident when considering the risk of yield reduction by 10% and 20%, which is expected to decrease by 25% and 28%, respectively. This suggests that constraining global temperature rise to 1.5 °C has more benefits for reducing extreme yield reductions. Spatially, keeping global warming within 1.5 °C would benefit more in Missouri, South Dakota, Eastern Kansas, Southern Texas and southeastern part of the country than other regions, highlighting the spatially variable benefits of climate mitigation efforts. The analysis framework introduced in this study can also be easily extended to other regions and crops. The results of this study highlight the areas where maize yield is most vulnerable to temperature rise, and the spatially variable benefits for reducing yield loss risk by keeping global warming within 1.5 °C.

Climate-Driven Crop Yield and Yield Variability and Climate Change Impacts on the U.S. Great Plains Agricultural Production

Climate variability and trends affect global crop yields and are characterized as highly dependent on location, crop type, and irrigation. U.S. Great Plains, due to its significance in national food production, evident climate variability, and extensive irrigation is an ideal region of investigation for climate impacts on food production. This paper evaluates climate impacts on maize, sorghum, and soybean yields and effect of irrigation for individual counties in this region by employing extensive crop yield and climate datasets from 1968-2013. Variability in crop yields was a quarter of the regional average yields, with a quarter of this variability explained by climate variability, and temperature and precipitation explained these in singularity or combination at different locations. Observed temperature trend was beneficial for maize yields, but detrimental for sorghum and soybean yields, whereas observed precipitation trend was beneficial for all three crops. Irrigated yields demonstrated increased robustness and an effective mitigation strategy against climate impacts than their non-irrigated counterparts by a considerable fraction. The information, data, and maps provided can serve as an assessment guide for planners, managers, and policy- and decision makers to prioritize agricultural resilience efforts and resource allocation or re-allocation in the regions that exhibit risk from climate variability.

Recent changes in county-level corn yield variability in the United States from observations and crop models

The United States is responsible for 35% and 60% of global corn supply and exports. Enhanced supply stability through a reduction in the year-to-year variability of US corn yield would greatly benefit global food security. Important in this regard is to understand how corn yield variability has evolved geographically in the history and how it relates to climatic and non-climatic factors. Results showed that year-to-year variation of US corn yield has decreased significantly during 1980-2010, mainly in Midwest Corn Belt, Nebraska and western arid regions. Despite the country-scale decreasing variability, corn yield variability exhibited an increasing trend in South Dakota, Texas and Southeast growing regions, indicating the importance of considering spatial scales in estimating yield variability. The observed pattern is partly reproduced by process-based crop models, simulating larger areas experiencing increasing variability and underestimating the magnitude of decreasing variability. And 3 out of 11 models even produced a differing sign of change from observations. Hence, statistical model which produces closer agreement with observations is used to explore the contribution of climatic and non-climatic factors to the changes in yield variability. It is found that climate variability dominate the change trends of corn yield variability in the Midwest Corn Belt, while the ability of climate variability in controlling yield variability is low in southeastern and western arid regions. Irrigation has largely reduced the corn yield variability in regions (e.g. Nebraska) where separate estimates of irrigated and rain-fed corn yield exist, demonstrating the importance of non-climatic factors in governing the changes in corn yield variability. The results highlight the distinct spatial patterns of corn yield variability change as well as its influencing factors at the county scale. I also caution the use of process-based crop models, which have substantially underestimated the change trend of corn yield variability, in projecting its future changes.