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1. Modeling the Impacts of Climate Change on Crop Yield and Irrigation in the Monocacy River Watershed, USA

   https://doi.org/10.3390/cli8120139  

   Paul, Manashi; Dangol, Sijal; Kholodovsky, Vitaly; Sapkota, Amy R.; Negahban-Azar, Masoud; Lansing, Stephanie (December 2020, Climate)

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   Crop yield depends on multiple factors, including climate conditions, soil characteristics, and available water. The objective of this study was to evaluate the impact of projected temperature and precipitation changes on crop yields in the Monocacy River Watershed in the Mid-Atlantic United States based on climate change scenarios. The Soil and Water Assessment Tool (SWAT) was applied to simulate watershed hydrology and crop yield. To evaluate the effect of future climate projections, four global climate models (GCMs) and three representative concentration pathways (RCP 4.5, 6, and 8.5) were used in the SWAT model. According to all GCMs and RCPs, a warmer climate with a wetter Autumn and Spring and a drier late Summer season is anticipated by mid and late century in this region. To evaluate future management strategies, water budget and crop yields were assessed for two scenarios: current rainfed and adaptive irrigated conditions. Irrigation would improve corn yields during mid-century across all scenarios. However, prolonged irrigation would have a negative impact due to nutrients runoff on both corn and soybean yields compared to rainfed condition. Decision tree analysis indicated that corn and soybean yields are most influenced by soil moisture, temperature, and precipitation as well as the water management practice used (i.e., rainfed or irrigated). The computed values from the SWAT modeling can be used as guidelines for water resource managers in this watershed to plan for projected water shortages and manage crop yields based on projected climate change conditions. 

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2. Detecting Recent Crop Phenology Dynamics in Corn and Soybean Cropping Systems of Kentucky

   https://doi.org/10.3390/rs13091615  

   Yang, Yanjun; Tao, Bo; Liang, Liang; Huang, Yawen; Matocha, Chris; Lee, Chad D.; Sama, Michael; Masri, Bassil El; Ren, Wei (May 2021, Remote Sensing)

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   Accurate phenological information is essential for monitoring crop development, predicting crop yield, and enhancing resilience to cope with climate change. This study employed a curve-change-based dynamic threshold approach on NDVI (Normalized Differential Vegetation Index) time series to detect the planting and harvesting dates for corn and soybean in Kentucky, a typical climatic transition zone, from 2000 to 2018. We compared satellite-based estimates with ground observations and performed trend analyses of crop phenological stages over the study period to analyze their relationships with climate change and crop yields. Our results showed that corn and soybean planting dates were delayed by 0.01 and 0.07 days/year, respectively. Corn harvesting dates were also delayed at a rate of 0.67 days/year, while advanced soybean harvesting occurred at a rate of 0.05 days/year. The growing season length has increased considerably at a rate of 0.66 days/year for corn and was shortened by 0.12 days/year for soybean. Sensitivity analysis showed that planting dates were more sensitive to the early season temperature, while harvesting dates were significantly correlated with temperature over the entire growing season. In terms of the changing climatic factors, only the increased summer precipitation was statistically related to the delayed corn harvesting dates in Kentucky. Further analysis showed that the increased corn yield was significantly correlated with the delayed harvesting dates (1.37 Bu/acre per day) and extended growing season length (1.67 Bu/acre per day). Our results suggested that seasonal climate change (e.g., summer precipitation) was the main factor influencing crop phenological trends, particularly corn harvesting in Kentucky over the study period. We also highlighted the critical role of changing crop phenology in constraining crop production, which needs further efforts for optimizing crop management practices. 

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3. Field‐scale analysis of miscanthus production indicates climate change may increase the opportunity for water quality improvement in a key Iowa watershed

   https://doi.org/10.1111/gcbb.13078  

   Ferin, Kelsie M.; Balson, Tyler; Audia, Ellen; Ward, Adam S.; Liess, Stefan; Twine, Tracy E.; VanLoocke, Andy (June 2023, GCB Bioenergy)

   Abstract The Raccoon River Basin is the primary source for drinking water in Iowa's largest city and plays a major role in the Mississippi River Basin's high nutrient exports. Future climate change may have major impacts on the biological, physiological, and agronomic processes imposing a threat to ecosystem services. Efforts to reduce nitrogen (N) loads within this basin have included local litigation and the implementation of the Iowa Nutrient Reduction Strategy, which suggest incorporating bioenergy crops (i.e., miscanthus) within the current corn–soybean landscape to reach a 41% reduction in nitrate loads. This study focuses on simulating N export for historical and future land use scenarios by using an agroecosystem model (Agro‐IBIS) and a hydrology model (THMB) at the 500‐m resolution, similar to the scale of agricultural fields. Model simulations are driven by CMIP5 climate data for historical, mid‐century, and late‐century under the RCP 4.5 and 8.5 warming projections. Using recent crop profit analyses for the state of Iowa, profitability maps were generated and nitrogen leaching thresholds were used to determine where miscanthus should replace corn–soybean area to maximize reductions in N pollution. Our results show that miscanthus inclusion on low profit and high N leaching areas can result in a 4% reduction of N loss under current climate conditions and may reduce N loss by 21%–26% under future climate conditions, implying that water quality has the potential continue to improve under future climate conditions when strategically implemented conservation practices are included in future farm management plans. 

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4. A generic risk assessment framework to evaluate historical and future climate-induced risk for rainfed corn and soybean yield in the U.S. Midwest

   Zhou, W. (August 2021, Weather and climate extremes)

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   Fluctuations in temperature and precipitation are expected to increase with global climate change, with more frequent, more intense and longer-lasting extreme events, posing greater challenges for the security of global food production. Here we proposed a generic framework to assess the impact of climate-induced crop yield risk under both current and future scenarios by combining a stochastic model for synthetic climate generation with a well-validated statistical crop yield model. The synthetic climate patterns were generated using the extended Empirical Orthogonal Function method based on historically observed and projected climate conditions. We applied our framework to assess the corn and soybean yield risk in the U.S. Midwest for historical and future climate conditions. We found that: (1) in the U.S. Midwest, about 45% and 40% of the interannual variability in corn and soybean yield, respectively, can be explained by the climate; (2) the risk level is higher in the southwest and northwest regions of the U.S. Midwest corresponding to 25% yield reduction for both corn and soybean compared to other regions; (3) the severity for the 1988 and 2012 major droughts quantified by our method represent 21-year and 30-year events for corn, and 7-year and 12-year events for soybean, respectively; (4) the crop yield risk will increase under a future climate scenario (i.e., Representative Concentration Pathway 8.5 or RCP 8.5 at 2050) compared with the current climate condition, with averaged yield decreases and yield variability increases for both corn and soybean. The framework and the results of this study enable applications for risk management policies and practices for the agriculture sectors. 

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5. Modeling Present and Future Permafrost Distribution at the Seward Peninsula, Alaska

   https://doi.org/10.1029/2019JF005355  

   Debolskiy, M. V.; Nicolsky, D. J.; Hock, R.; Romanovsky, V. E. (August 2020, Journal of Geophysical Research: Earth Surface)

   Abstract Permafrost, a key component of Arctic ecosystems, is currently affected by climate warming and anticipated to undergo further significant changes in this century. The most pronounced changes are expected to occur in the transition zone between the discontinuous and continuous types of permafrost. We apply a transient temperature dynamic model to investigate the spatiotemporal evolution of permafrost conditions on the Seward Peninsula, Alaska—a region currently characterized by continuous permafrost in its northern part and discontinuous permafrost in the south. We calibrate model parameters using a variational data assimilation technique exploiting historical ground temperature measurements collected across the study area. The model is then evaluated with a separate control set of the ground temperature data. Calibrated model parameters are distributed across the domain according to ecosystem types. The forcing applied to our model consists of historic monthly temperature and precipitation data and climate projections based on the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. Simulated near‐surface permafrost extent for the 2000–2010 decade agrees well with existing permafrost maps and previous Alaska‐wide modeling studies. Future projections suggest a significant increase (3.0°C under RCP 4.5 and 4.4°C under RCP 8.5 at the 2 m depth) in mean decadal ground temperature on average for the peninsula for the 2090–2100 decade when compared to the period of 2000–2010. Widespread degradation of the near‐surface permafrost is projected to reduce its extent at the end of the 21st century to only 43% of the peninsula's area under RCP 4.5 and 8% under RCP 8.5. 

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