Agro-photovoltaic systems are of interest to the agricultural industry because they can produce both electricity and crops in the same farm field. In this study, we aimed to simulate staple crop yields under agro-photovoltaic panels (AVP) based on the calibration of crop models in the decision support system for agricultural technology (DSSAT) 4.6 package. We reproduced yield data of paddy rice, barley, and soybean grown in AVP experimental fields in Bosung and Naju, Chonnam Province, South Korea, using CERES-Rice, CERES-Barley, and CROPGRO-Soybean models. A geospatial crop simulation modeling (GCSM) system, developed using the crop models, was then applied to simulate the regional variations in crop yield according to solar radiation reduction scenarios. Simulated crop yields agreed with the corresponding measured crop yields with root mean squared errors of 0.29-ton ha⁻¹ for paddy rice, 0.46-ton ha⁻¹ for barley, and 0.31-ton ha⁻¹ for soybean, showing no significant differences according to paired sample t-tests. We also demonstrated that the GCSM system could effectively simulate spatiotemporal variations in crop yields due to the solar radiation reduction regimes. An additional advancement in the GCSM design could help prepare a sustainable adaption strategy and understand future food supply insecurity.

A remote sensing-integrated crop model (RSCM) able to simulate crop growth processes using proximal or remote sensing data was formulated for simulation of soybean through estimating parameters required for modelling. The RSCM-soybean was then evaluated for its capability of simulating leaf area index (LAI), above-ground dry mass (AGDM), and yield, utilising the proximally sensed data integration into the modelling procedure. Field experiments were performed at two sites, one in 2017 and 2018 at Chonnam National University, Gwangju, and the other in 2017 at Jonnam Agricultural Research and Extension Services in Naju, Chonnam province, South Korea. The estimated parameters of radiation use efficiency, light extinction coefficient, and specific leaf area were 1.65 g MJ⁻¹, 0.71, and 0.017 m² g⁻¹, respectively. Simulated LAI and AGDM values agreed with the measured values with significant model efficiencies in both calibration and validation, meaning that the proximal sensing data were effectively integrated into the crop model. The RSCM reproduced soybean yields in significant agreement with the measured yields in the model assessment. The study results demonstrate that the well-calibrated RSCM-soybean scheme can reproduce soybean growth and yield using simple input requirement and proximal sensing data. RSCM-soybean is easy to use and applicable to various soybean monitoring projects.

The cowpea is one of the most important legume species globally, with both the grains and fresh pods widely consumed for the rich nutritional content. In the Republic of Korea, the cultivation and breeding progress of cowpeas is relatively low but gradually receiving interest due to its potential contribution to nutrition and sustainable agriculture. Given the changing pattern of global climatic conditions, any effort in cowpea breeding in Korea may focus on important traits such as improving yield, stress resistance, and adaptability to local climate. This review provides a discussion on the current status of the cultivation and breeding of cowpeas in the Republic of Korea, with the aim of improving crop performance, agricultural sustainability, and food security.

Dong-Kwan Kim, Jin-Gyung Choi, Seon-Gon Kim, Kyung-Dong Lee, Min-Jung Seo, Beom-Kyu Kang, and Tae-Jeong Ha. Korean J. Breed. Sci. 2019;51:209-13. https://doi.org/10.9787/KJBS.2019.51.3.209

Dong-Kwan Kim, Jin-Gyung Choi, Oh-Do Kwon, Kyung-Dong Lee, and Kyoung-In Ryu. Korean J. Breed. Sci. 2018;50:319-23. https://doi.org/10.9787/KJBS.2018.50.3.319