Simulation of Crop Yields Grown under Agro-Photovoltaic Panels: A Case Study in Chonnam Province, South Korea

Abstract

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.

Agrivoltaic System and Modelling Simulation: A Case Study of Soybean (Glycine max L.) in Italy

“…These results confirm that, considering the conceptual model proposed by Laub et al [70], soybean is shade tolerant. The yield reduction measured in this work is lower that what previously reported in the literature [45,64,70,[78][79][80], which could be a consequence of pedoclimatic conditions or variety choice, considering that the effect of shading in soybean varies largely with the genotype [64]. Considering the large effect that genotype, environmental factors, and AV system design have on crop yield, it would be necessary to run multiple long-term studies to provide the information needed to support the design and management of sustainable AV systems.…”

“…The average grain yield reduction for the whole AV system was 8%, which is largely under the limits of the yield reductions indicated by the DIN standards in Germany ( [77], for which at least 66% of the reference yield needs to be achieved under the AV system). In a Korean study [45], which investigated soybean cultivation under an AV system, grain yield was reduced by 20% with a shade depth of 25%, while, in the present work, a similar shade depth (27% in AV1) determined a yield reduction of only 8%. These results confirm that, considering the conceptual model proposed by Laub et al [70], soybean is shade tolerant.…”

“…Information on the performance of industrial crops cultivated under AV systems is scarce, and limited data are available on soybean (Glycine max L.) cultivation under AV systems. Namely, two modelling approaches have been investigated for soybean [45,46]. The adoption of the crop modelling approach allows an insight into the microclimatic conditions that are generated under AV systems by considering multiple micrometeorological conditions (i.e., solar radiation, temperature, CO 2 concentration, soil nutrients, and water); the management of the crops (i.e., plant number per m 2 or fertilisation); and the crop yield response to the shaded environment [46,47].…”

“…In a recent work, the response of soybean to an AV environment was modelled by coupling a crop model with a solar power generation model to obtain data on the effect of an AV system on both the crop yield and the net revenue for the landowners [46]. In another study carried out in Korea [45], field trials were carried out on rice, barley, and soybean to collect the data needed to calibrate and validate three crop models (CERES-rice, CERES-barley, and CROPGRO-soybean) and to predict the impact of shade on crop yield.…”

Influence Mechanisms of Dynamic Changes in Temperature, Precipitation, Sunshine Duration and Active Accumulated Temperature on Soybean Resources: A Case Study of Hulunbuir, China, from 1951 to 2019

“…The existing studies have mainly adopted the Meteorological Yield Separation Method, the Wavelet Analysis Method, the First-Order Difference Methods of yield and climate factors, the Mann-Kendall Test and other mathematical and statistical methods, and integrated the knowledge of phenology to explore the influence of meteorological factors such as precipitation and temperature on soybean yields [11][12][13]. Some researchers have also paid attention to the role of the combination of different climatic factors, thus using crop yield models, such as the Difference Model, the CERES-Rice Model and the APSIM Model, in synchronization with climate patterns, to comprehensively assess the trend of soybean growth under the influence of different meteorological factors [14,15]. However, most studies have focused on the effects of temperature and precipitation on soybeans, while neglecting the effects of other meteorological elements such as cumulus temperature and light on soybean growth.…”