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1. Impact of Agrivoltaic Shade on Beet Curly Top Virus and Yield in Chile Pepper (Capsicum annuum)

   https://doi.org/10.21273/HORTSCI18537-25  

   Joukhadar, Israel; Estrada, Mariela; Velasco-Cruz, Ciro; Coon, Danise; Lavrova, Olga; Thompson, Marisa; Guzman, Ivette; Walker, Stephanie (June 2025, HortScience)

   Curtovirusspp., a group of widely distributed geminiviruses, are among the most significant plant pathogens in the United States. Beet curly top virus (BCTV), vectored by the beet leafhopper [Circulifer(Neoaliturus)tenellusBaker], causes severe economic losses in crops such as tomatoes, peppers, dry beans, sugar beets, melons, and leafy greens, particularly in the western United States. In New Mexico, chile (Capsicum annuum) is vulnerable, with infected plants exhibiting symptoms such as stunting, chlorotic and curled leaves, misshapen fruits, reduced yields, and plant death. Yield losses can reach up to 50% in some years. Current management strategies, including pesticide applications, provide limited protection, leaving growers with substantial losses. Research indicates beet leafhoppers prefer full sun and avoid shaded areas, suggesting potential for innovative pest management strategies. Agrivoltaic systems (AVSs), which combine photovoltaic (PV) installations with agriculture, increase field shade and could potentially deter crop pests. However, the effect of AVS on pest management and chile crop yields remains underexplored. This study evaluated the impact of AVS shade on chile yield and plant growth, beet leafhopper abundance, and BCTV incidence. In 2023 and 2024, ‘NuMex Odyssey’, a New Mexico type chile cultivar, was grown in PV module–shaded and full sun replications at New Mexico State University’s Leyendecker Plant Science Research Center. PV modules provided shade to plants during the morning hours (0630 to 1330 HR), resulting in an 11% reduction in mean light intensity compared with the full sun replications. Full sun replications had more marketable green yield, while PV shaded replications exhibited significantly lower BCTV-affected fruit in 2024 and beet leafhopper abundance in both 2023 and 2024. These findings suggest that shading can reduce beet leafhopper abundance and BCTV incidence, offering potential benefits for chile cultivation. 

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2. What would it look like? Visualizing a future US Corn Belt landscape with more table food production

   https://doi.org/10.1017/S1742170524000024  

   Stone, Tiffanie F; Thompson, Janette R; Zimmerman, Emily; Brighenti, Tassia M; Liebman, Matt (January 2024, Renewable Agriculture and Food Systems)

   Abstract Most farmland in the US Corn Belt is used to grow row crops at large scales (e.g., corn, soybean) that are highly processed before entering the human food stream rather than specialty crops grown in smaller areas and meant for direct human consumption (table food). Bolstering local table food production close to urban populations in this region through peri-urban agriculture (PUA) could enhance sustainability and resilience. Understanding factors influencing PUA producers' preferences and willingness to produce table food would enable supportive planning and policy efforts. This study combined land use visualization and survey data to examine the potential for increased local table food production for the US Corn Belt. We developed a spatial visualization of current agricultural land use and a future scenario with increased table food production designed to meet 50% of dietary requirements for a metropolitan population in 2050. A survey was administered to row crop (1360) and specialty crop (55) producers near Des Moines, Iowa, US to understand current and intended agricultural land use and factors influencing production. Responses from 316 row crop and 25 specialty crop producers were eligible for this analysis. A future scenario with increased table food production would require less than 3% of available agricultural land and some additional producers (approximately 130, primarily for grain production). Survey responses indicated PUA producers planned small increases in table food production in the next three to five years. Producer plans, including land rental for table food production, could provide approximately 25% of residents' fruit, vegetables, and grains, an increase from the baseline of 2%. Row crop producers ranked food safety regulations, and specialty producers ranked labor concerns as strong influences on their decision-making. Both groups indicated that crop insurance and processing facilities were also important. Increasing table food production by clustering mid-scale operations to increase economies of scale and strengthening supply chains and production infrastructure could provide new profitable opportunities for farmers and more resilient food systems for growing urban regions in the US Corn Belt. Continuing to address producer factors and landscape-scale environmental impacts will be critical in considering food system sustainability challenges holistically. 

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3. Addressing Challenges for Mapping Irrigated Fields in Subhumid Temperate Regions by Integrating Remote Sensing and Hydroclimatic Data

   https://doi.org/10.3390/rs11030370  

   Xu, Tianfang; Deines, Jillian; Kendall, Anthony; Basso, Bruno; Hyndman, David (February 2019, Remote Sensing)

   High-resolution mapping of irrigated fields is needed to better estimate water and nutrient fluxes in the landscape, food production, and local to regional climate. However, this remains a challenge in humid to subhumid regions, where irrigation has been expanding into what was largely rainfed agriculture due to trends in climate, crop prices, technologies and practices. One such region is southwestern Michigan, USA, where groundwater is the main source of irrigation water for row crops (primarily corn and soybeans). Remote sensing of irrigated areas can be difficult in these regions as rainfed areas have similar characteristics. We present methods to address this challenge and enhance the contrast between neighboring rainfed and irrigated areas, including weather-sensitive scene selection, applying recently developed composite indices and calculating spatial anomalies. We create annual, 30m-resolution maps of irrigated corn and soybeans for southwestern Michigan from 2001 to 2016 using a machine learning method (random forest). The irrigation maps reasonably capture the spatial and temporal pattern of irrigation, with accuracies that exceed available products. Analysis of the irrigation maps showed that the irrigated area in southwestern Michigan tripled in the last 16 years. We also discuss the remaining challenges for irrigation mapping in humid to subhumid areas. 

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4. A Data-Driven Framework for Optimal Placement of Grid-Connected Solar Generation

   A. M. Ospina, M. Kezunovic (January 2019, IEEE PES General Meeting, Atlanta, Georgia, August, 2019)

   This work presents a decision making approach for selecting an optimal placement of the grid-connected solar generation using Geographical Information System (GIS) as the decision making tool. A terrain analysis for solar radiation assessment, as well as buildings and vegetation spatial data are analyzed in order to determine the shadow impact that can be anticipated for medium or large-scale PV installation. In addition, different historical weather conditions are considered and integrated into the model to show the impact of this variable on the solar generation output. Some details of the methodology, testbed development and results related to the selection of potential sites for PV installation are presented. To illustrate the process and proposed methodology, an example using large scale synthetic networks is implemented. 

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5. Improving the ability of solar-induced chlorophyll fluorescence to track gross primary production through differentiating sunlit and shaded leaves

   https://doi.org/10.1016/j.agrformet.2023.109658  

   Zhang, Zhaoying; Chen, Jing M; Zhang, Yongguang; Li, Manchun (October 2023, Agricultural and Forest Meteorology)

   Recently, solar-induced chlorophyll fluorescence (SIF) is a promising tool to estimate gross primary production (GPP). Photosynthesis gradually saturates with the increasing light, but fluorescence tends to keep increasing, leading to a nonlinear SIF-GPP relationship. This nonlinearity occurs for sunlit leaves but not for shaded leaves for which photosynthesis is light-limited. However, the separation of sunlit and shaded SIF has not been systematically investigated when estimating GPP from SIF. Therefore, it is promising to develop a model for GPP estimation considering such differences. This study proposed an approach to separate the total canopy SIF emission (SIFtotal) from TROPOspheric Monitoring Instrument (TROPOMI) SIF into their sunlit and shaded components (SIFsun and SIFshade). The nonlinearity and linearity in SIF-GPP relationships for sunlit and shaded leaves were incorporated into a two-leaf hybrid model, which was fitted using flux tower data and then evaluated using leave-one-site-out crossing validation. We also elucidated the distinct SIF-GPP relationships between sunlit and shaded leaves using the Soil-Canopy-Observation of Photosynthesis and the Energy balance (SCOPE) model simulation. Compared to previously used linear (R2 = 0.68, RMSE = 2.13 gC⋅m^-2*d^-1) or hyperbolic (R2 = 0.72, RMSE = 2.01 gC⋅m^-2⋅d^-1) model based on the big-leaf assumption, our proposed two-leaf hybrid model has the best performance on GPP estimation (R2 = 0.77, RMSE = 1.79 gC⋅m^-2⋅d^-1). We also applied this two-leaf hybrid model to estimate the global GPP during the main growing season in Northern Hemisphere, which were highly correlated with several existing GPP products, with R2 ranging from 0.79 to 0.88. These results will improve our understanding of the relationship between SIF and GPP for sunlit and shaded leaves and will advance application of satellite SIF data to GPP estimation. 

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