The aim of this study was to identify an efficient agrophotovoltaic (APV) system structure for generating electricity from solar radiation without causing an adverse impact on crop growth. In a temperate climate region, it is critical to design an APV system with appropriate structure with the maximum amount of electricity generation because, unlike in desert areas, strong solar radiation is only available for a few hours a day. In this study, APV systems with three different shading ratios (i.e., 32%, 25.6%, and 21.3%) were considered, and the optimum structure in terms of electricity efficiency and profitability was investigated via nonlinear programming. Moreover, an estimation model of electricity generation was developed via a polynomial regression model based on remote sensing data given by the APV system located at Jeollanamdo Agricultural Research and Extension Services in South Korea. To evaluate the impact of the APV on crop production, five different grain crops—sesame (Sesamum indicum), mung bean (Vigna radiata), red bean (Vigna angularis), corn (Zea mays), and soybean (Glycine max)—were cultivated in the system. As a result, the proposed optimization model successfully identified the best APV system structure without reducing existing crop production.

As global temperatures have steadily increased over past decades, studying of the impacts of heat stress on morpho-physiological traits and economic yields of horticultural crops have been increasingly gained attentions by many scientists and farmers. Hot pepper (Capsicum annuum L.) is an important vegetable crop mostly grown in open-fields in South Korea. In this study, the impacts of prolonged heat stress on three hot pepper genotypes differing by levels of stress susceptibility were evaluated. The study was conducted in two different temperature-controlled greenhouses for 75 days. 48 days old plants were grown in control and heat-treated greenhouses where the temperatures had been set at 30 °C and 35 °C during the day for 75 days, respectively. Morphological, physiological, and nutrient characteristics of three accessions were measured. All hot pepper accessions were enabled to recover from prolonged heat stress exposures within approximately a month. The phenomenon of recovery was observed in some significant morphological and physiological characteristics. For example, the plant growth rate and photosynthesis rate significantly increased after 40th days of heat treatment. The heat stress sensitivity varied between genotypes. The plants that produced more fruits over biomass at early stage of heat treatment had relatively slow recovery, resulting in the largest yield loss. This key morphological characteristic can be used for future breeding program to adapt the prolonged heat stress.

Sorghum is the world’s fifth major cereal in terms of production and acreage. It is expected that its growth will be affected by the increase in air temperature, an important component of global climate change. Our objective was to use the Agricultural Land Management Alternatives with Numerical Assessment Criteria (ALMANAC) model to (a) evaluate the impact of climate warming on forage and grain sorghum production in Argentina and (b) to analyze to what extent yield changes were associated with changes in water or nitrogen stress days. For model calibration, we used previous information related to the morpho-physiological characteristics of both sorghum types and several soil parameters. We then used multiyear field data of sorghum yields for model validation. Yield simulations were conducted under three possible climate change scenarios: 1, 2, and 4 °C increase in mean annual temperature. ALMANAC successfully simulated mean yields of forage and grain sorghum: root mean square error (RMSE): 2.6 and 1.0 Mg ha⁻¹, respectively. Forage yield increased 0.53 Mg ha⁻¹, and grain yield decreased 0.27 Mg ha⁻¹ for each degree of increase in mean annual temperature. Yields of forage sorghum tended to be negatively associated with nitrogen stress (r = −0.94), while grain sorghum yield was negatively associated with water stress (r = −0.99). The information generated allows anticipating future changes in crop management and genetic improvement programs in order to reduce the yield vulnerability.

The Agrophotovoltaic (APV) system is a novel concept in the field of Renewable Energy Systems. This system enables the generation of solar energy via photo-voltaic (PV) modules above crops, to mitigate harmful impact on food production. This study aims to develop a performance evaluation model for an APV system in a temperate climate region, such as South Korea. To this end, both traditional electricity generation models (solar radiation-based model and climate-based model) of PV modules and two major machine learning (ML) techniques (i.e., polynomial regression and deep learning) have been considered. Electricity generation data was collected via remote sensors installed in the APV system at Jeollanam-do Agricultural Research and Extension Services in South Korea. Moreover, economic analysis in terms of cost and benefit of the subject APV system was conducted to provide information about the return on investment to farmers and government agencies. As a result, farmers, agronomists, and agricultural engineers can easily estimate performance and profit of their APV systems via the proposed performance model.