Fiji, with its many islands and mountainous terrain, has only about 11% of its total land area (2000 km2) suitable for cultivation. Therefore, it aims to meet both energy and food production simultaneously through agricultural photovoltaic (APV) systems. This study proposed an optimal agricultural management of APV system to increase farm income and solve the problem of low vegetable production. The practice is planned based on the data from farmer surveys, field study, simulation analysis, and agricultural market analysis. Firstly, a farmer survey was conducted to gather data on the agricultural activities and income of local farmers. Based on the survey results, field studies with various vegetables were conducted in an APV system. In simulation, yields of lettuce, taro, long bean, and cucumber were estimated in the APV system with different cropping management techniques (planting schedule and plant density). With the average yields of lettuce, taro, long bean, and cucumber at highest plant densities being (72.4, 71.1, 3.9, and 10.8) Mg/ha, respectively, according to economic analysis, the highest gross margin was achieved in taro in the APV system. This study shows that the APV system can increase farmers' annual household income by 1.19 to 1.38%, which represents a meaningful absolute gain given the low average income levels identified in the farm survey.

Intercepted photosynthetically active radiation (IPAR) to biomass method is widely used in plant growth models to simulate biomass accumulation. This method is closely linked to radiation use efficiency (RUE), which can vary by species, cultivars, and location. Although Chinese cabbage (Brassica rapa L.) is a cool-season plant, it is cultivated year-round in South Korea. Therefore, investigating the RUEs of two different ecotypes of Chinese cabbage is crucial for developing accurate plant growth models. In this study, we examined RUEs and other key agronomic characteristics that influence Chinese cabbage growth across different growing seasons. Field studies were conducted to analyze the growth patterns of fall and summer ecotypes and to explore various agronomic traits for developing a leaf area index (LAI) model. Using a multivariate regression method, we developed an LAI model that simulates the leaf area index for both ecotypes across multiple locations in South Korea (R2 = 0.92). A total of 218 field data points collected from 35 sites between 2020 and 2023 were used to estimate the RUEs and LAIs of the fall and summer ecotypes. Results indicated that Chinese cabbage demonstrated more efficient photosynthesis in the fall, with RUEs of 2.3 g MJ-1 for the fall ecotype compared to 1.3 g MJ-1 for the summer ecotype based on regional estimation. This difference may be attributed to lower radiation availability per unit of heat growth during the summer season (0.3 MJ degrees C-1) compared to the fall (0.78 MJ degrees C-1). The findings of this study will aid plant modelers and enhance the accuracy of simulations for Chinese cabbage growth.

To achieve the goal of the 2015 Paris Agreement to limit global warming to 2 °C compared to pre-industrial levels, South Korea is implementing a policy to use bioethanol as a transportation fuel based on the Renewable Fuel Standard (RFS). This study proposes a mixed-integer linear programming (MILP) model to design an optimal bioethanol supply chain utilizing rice straw, a readily available resource in South Korea. To minimize the total cost of bioethanol production, the proposed model considers optimal facility locations, i.e., those of feedstock collection (farm), refining (refinery), and consumption (market), and transportation volumes. This experiment is conducted to evaluate the blending ratios of bioethanol in gasoline (3%, 6%, and 9%) specified by the Renewable Fuel Standard (RFS) policy, based on actual gasoline consumption data in South Korea. In the RFS 3% scenario, operating a single large-scale refinery was the most economical option, but in the RFS 6% and RFS 9% scenarios, multiple refineries must be utilized to ensure supply chain economics. In conclusion, the proposed MILP model shows the practicality of gradually increasing the number of refineries and selecting the optimal location for each region as future bioethanol demand increases.

Heat stress is a major abiotic factor limiting tomato productivity, increasing the demand for heat-tolerant cultivars. This study aimed to investigate the relationship between pollen and fruit yield traits under heat stress conditions in tomatoes with different fruit sizes. Experiments were conducted in growth chambers and greenhouses over two years, using tomato genotypes categorized by fruit sizes: large (> 100 g), medium (50–100 g), and cherry (< 50 g). Heat stress significantly reduced pollen viability and the number of pollen grains in most accessions. The number of fruits and fruit yield were consistently lower under high temperature compared to control temperature conditions. Significant correlations were observed between pollen viability and the number of fruits and fruit yield in large and medium fruit accessions under high temperature, suggesting that pollen viability can be a reliable indicator for selecting heat-tolerant genotypes in these fruit types. In contrast, no significant correlation was found between pollen traits and fruit yield in cherry fruit accessions, where the number of fruits per truss was a more appropriate target trait for heat tolerance. These findings indicate that different strategies may be needed for breeding heat-tolerant tomato cultivars based on fruit size. Selecting tomato lines with high pollen viability under high temperature conditions could enhance the selection process for heat-tolerant genotypes with large tomatoes, and the use of impedance flow cytometry for testing pollen viability can streamline the breeding process for heat-tolerant tomatoes.

From 2020 to 2021, crop production increased by 54% globally, and the popularity of commercial agriculture to increase profitability is gradually increasing. However, global warming and climate issues make it difficult to maintain stable crop production. To improve crop production efficiency, techniques for efficiently managing large-scale commercial farmland are needed. This study proposes a satellite image-based soil moisture and onion yield prediction technique as a methodology for managing large-scale farmland. This preemptive soil moisture management technique effectively manages increased soil pressure, resulting in soil drying due to rising temperatures. To remotely identify agricultural land, vegetation indices were extracted from satellite image data, and K-means clustering was applied. Ensemble machine learning is performed on soil images collected from satellite images. This model combines soil physical properties with soil environmental factor information to develop a model. The results show that soil color information obtained from satellite images is highly correlated with soil organic matter content. The proposed model is validated using crop yield data and environmental factor data obtained from actual crop production experiments. Consequently, the proposed methodology can be effectively applied to manage large-scale farmland and enables decision-making to improve profitability.

Improving the accuracy of yield predictions for cash crops such as pepper (Capsicum annum L.) has increasingly captured the interest of many scientists in South Korea. This study marks the first initiative to develop yield prediction tools for peppers cultivated under heat stress conditions. To refine the yield prediction model, field studies were conducted to establish the plant growth curve and parameter sets for two different pepper accessions, PHR18 and PHR23, under heat stress conditions. According to field studies, the two pepper accessions exhibited distinct growth patterns under prolonged heat stress conditions. PHR18 experienced significant heat stress effects in the first month of exposure, whereas it demonstrated stress priming to regain growth by the 75th day of heat stress exposure. PHR23, having a larger leaf area, accumulated more biomass than fruit yields in the initial month of exposure, thus increasing its yields at higher temperature conditions due to enhanced photosynthesis rates. The crop growth curve and parameters were formulated based on these studies, and the open field simulations were calibrated with measured yields from multiple locations in South Korea from 2020-2024. Consequently, a robust pepper growth model was developed and employed to assess the effects of heat stress on the yields of two pepper accessions across various South Korean locations. The development of this crop growth model under stressful conditions will aid farmers and policymakers in making informed decisions during extreme events.

Chinese cabbage (Brassica rapa) is one of the most important fall vegetables in South Korea. Recently, cabbage yields fluctuated due to climate change, leading to an unstable supply and increased prices. Additionally, raised temperatures led to increased beet armyworm (Spodoptera exigua) populations, resulting in greater plant damage. In this study, the Agricultural Policy/Environmental Extender (APEX) model was employed to develop the cabbage growth model. To enhance model accuracy, 4 years of field data collected from multiple locations in South Korea were utilized for model validation and calibration. The model goodness of fit tests revealed R2 values between 0.9485 and 0.9873. Two different cabbage models, representing the physiological characteristics of common varieties cultivated in Korea, were applied to assess growth patterns under two distinct climate change scenarios, SSP245 and SSP585. A larval duration prediction model was formulated using previous field data. Under future climate conditions, simulation results indicate that as temperatures rise, Chinese cabbage yields will likely decrease continually, with increasing plant damage from insects. The modeling results can help farmers to control and manage crop insect pests under varying environmental conditions.

In South Korea, cherry tomato (Solanum lycioersucum) is a major greenhouse vegetable crop. However, climate change has steadily raised Earth’s average temperature, posing a serious challenge for greenhouse agriculture. Elevated temperatures can trigger heat stress in greenhouse crops, leading to considerable yield losses. This study developed a greenhouse tomato growth model for two cherry tomato accessions, HR17 and HR24, cultivated under heat stress conditions during growing periods. Climate projections based on polynomial regression were incorporated into the plant growth model to assess climate change impacts on tomato yields. The two tomato accessions demonstrate distinct growth characteristics: HR24 allocates more biomass relative to fruit yield (Harvest index:0.48), whereas HR17 shows greater fruit production than biomass accumulation (Harvest index:0.65). Their yield responses also vary under future climate scenarios highlighted by temperature increases of 1-8°C and extended hot seasons compared to historical records. HR24 appears more resilient to heat stress than HR17. Under Climate Change scenarios (SSP245 and SSP585 pathways), HR17 will decrease its fruit yield by around 1.2 Dry Ma/ha, while HR24 yields will be increased by round 1.3 Dry Mg/ha. This increased tolerance in HR24 may be attributable to its ability to sustain photosynthetic activity through higher production of biomass organs such as leaves and stems. These findings form a foundation for developing greenhouse crop models in future research and supporting farmers by providing more reliable yield forecasts.

As crop harvesting becomes more difficult in environments affected by climate change, the application of artificial intelligence technology to crop management through accurate yield prediction is receiving worldwide attention. This study proposes a convolutional neural network (CNN)-based transfer learning framework to increase the productivity and improve the economic feasibility of cherry tomatoes (solanum lycopersicum) in South Korea. You-Only-Look-Once 10 Nano (YOLOv10n) is adopted as a CNN-based algorithm. The source model for transfer learning is trained using cherry tomato imagery from the Tomato Plantfactory Dataset, while the target model is trained based on field survey data collected by the National Institute of Horticultural & Herbal Science, Rural Development Administration, Korea. In that process, an image segmentation technique is developed to improve the prediction accuracy, which reduces the root-mean-square deviation of the existing YOLOv10n from 32.3 to 19.8, a 38.7% reduction. Also, the devised economic feasibility analysis method finds the cost of producing cherry tomatoes in South Korea to be 11.12 USD/m(2), while the maximum revenue can reach 22.44 USD/m(2). As a result, the proposed transfer learning framework helps general farms where it is difficult to collect big data to use machine learning techniques to predict crop or vegetable production.

Sustainable production of Chinese cabbage (Brassica rapa L.) is essential for the economy and food security in South Korea. However, climate change poses significant threats to its growth and quality. Changes in precipitation patterns, such as drought and heavy rain, can affect the development of crop, leading to stunted growth or disease. This can lead to significant losses in crop yield. In this study, a hybrid modeling system was developed to predict the incidence of viral disease and evaluate impacts of combination of virus infection and environmental variabilities (including climate and location) on marketable cabbage yields. The crop growth model was successfully developed using a limited number (n = 7) of previous studies (root mean square error 0.25-0.33 Mg ha-1, R2 = 0.87-0.99). The developed hybrid modeling system was composed of a virus incident model and a crop growth model. According to simulated results, all study locations had around 20% incidence rates. However, based on the simulation results, the rates of viral disease incidence varied depending on the climate of each year. Among several climatic factors, precipitation had the greatest effect on virus outbreaks. Jeju Island, which had relatively high rainfall, had a higher disease incidence rate than other provinces. According to shared socioeconomic pathways (SSPs), in SSP245 (an intermediate development pathway), the yield of 1.58 Mg ha-1 was reduced to 1.26 Mg ha-1 due to a 25% viral disease outbreak. In SSP585 (a high development pathway), with an incidence of 23%, the yield was reduced by 0.2 Mg ha-1. These results will be useful for efficiently cultivating crops under various climatic conditions and seeking management methods to minimize damage from viral disease. A hybrid modeling system for predicting Chinese cabbage yield loss by viruses was developed.Incident rates of viruses were highly related to rainfall amount.Simulated virus-infected cabbage yields varied with location and climate.In future climate conditions, chances of virus incidence are increased by around 20%.

Agrophotovoltaic (APV) systems, which were first proposed in 1982 by Goetzberger and Zastrow, can simultaneously produce both photovoltaic solar energy and crops. However, their structure, whereby photovoltaic (PV) panels are located above the crops, could negatively affect crop growth. Therefore, engineers need to identify an efficient structure for an APV system before beginning any construction project involving an APV. The goal of this study is to propose a multi-objective optimization approach with a fuzzy inference system to identify the optimal design of the APV system in terms of crop production and solar power generation. To this end, a Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) is adopted with three objectives: (1) Maximization of solar energy production, (2) Maximization of crop production, and (3) Minimization of the construction cost of an APV system. In the proposed approach, linear regression and polynomial regression are used to estimate the electricity generation quantity, the yields of five crops (sesame, mung bean, red bean, corn, and soybean), and the overall construction cost. Field study data collected from the Jeollanam-do Agricultural Services and Extension Services in South Korea are used to develop the estimation models. After the Pareto Front is constructed via NSGA-II, the devised fuzzy inference system selects the best solution regarding the impact of the APV system on the farmers’ economy. The proposed approach suggests that the final optimum design of the APV system according to the fuzzy inference system is seen in the cultivation of red beans with a shading ratio of 27.83% underneath the bifacial PV panel type. As a result, the proposed approach allows for the simultaneous consideration of multiple aspects, so it is expected to provide practical and sustainable solutions to farmers and engineers by supporting APV system design.

Eastern gamagrass (Tripsacum dactyloides) is a highly productive, highly palatable native grass tolerant to both drought and flooding. It has frequently shown great response to nitrogen (N) applications, but the responses of southern native ecotypes in upland and bottomland sites have yet to be reported. The objectives were to measure the responses of long-established eastern gamagrass with different N application rates in two bottomland hay pastures and two upland grazed sites, and to measure the N responses for six diverse ecotypes in a common garden. A randomized block design was used with ecotype as the main block and fertilizer rate as the subplot. In the long-established sites, 75 N peak yields were not statistically different to those of 0 N, while upland yields across the season were consistently higher for 150 N but varied for the bottomland. The common garden ecotypes had no significant difference in yield between treatments when averaged across years. Roaring Springs showed the most consistent and greatest benefit to additional N, more than doubling the dry weight of the control. All remaining ecotypes, however, had more modest responses. Eastern gamagrass responds inconsistently to applications of 75 N, whereas most applications of 150 N generally result in higher yield, though significant increases are not guaranteed.

Onion (Allium cepa) is a major field vegetable in South Korea and has been produced for a long time along with cabbage, radish, garlic, and dried peppers. However, as field vegetables, including onions, have recently been imported at low prices, the profitability of onion production in South Korea is beginning to be at risk. In order to maximize farmers’ profits through onion production, this study develops onion yield prediction models via an ensemble learning-based framework involving linear regression, polynomial regression, support vector regression, decision tree, ridge regression, and lasso regression. The use of nitrogen fertilizers is considered an independent variable in the development of the yield prediction model. This is because the use of nitrogen fertilizers accounts for the highest production cost (13.47%) after labor cost (41.21%) and seed cost (17.42%), and it also directly affects onions yields. For the model development, five research datasets on changes in onion yield according to changes in the use of existing nitrogen fertilizers were used. In addition, a non-linear optimization model was devised using onion yield prediction models for the profit maximization of onion production. As a result, the developed non-linear optimization model using polynomial regression enables an increase in profits from onion production by 67.28%.

Building-integrated photovoltaics (BIPVs) entail the use of photovoltaics as building materials, such as windows, roofs, and walls. Owing to their electricity-generation ability, BIPVs have become popular building materials for green buildings. This study involves an economic feasibility analysis of BIPVs for an agricultural research and development center in Fiji. The computerized relative allocation of facilities technique (CRAFT) is extended for cost-efficient facility design, and the levelized cost of electricity (LCOE) of BIPVs is evaluated to identify feasible alternatives from among three options, namely roof-integrated, window, and rooftop BIPVs. From the experiments, the LCOE values of the roof-integrated, window, and rooftop BIPVs were USD 0.13/kWh, 0.17/kWh, and 0.09/kWh, respectively. Moreover, the profit for the window BIPV was negative when the discount rate was greater than 0.08 due to its inefficient productivity. Contrarily, the roof-integrated BIPV showed a reasonable LCOE even though it required the highest investment cost. Nevertheless, further efforts are needed to reduce the cost of BIPVs for practical implementation.

Biodiesel has received worldwide attention as a renewable energy resource that reduces greenhouse gas (GHG) emissions. Unlike traditional fossil fuels, such as coal, oil, and natural gas, biodiesel made of vegetable oils, animal fats, or recycled restaurant grease incurs higher production costs, so its supply chain should be managed efficiently for operational cost reduction. To this end, multiple machine learning technologies have recently been applied to estimate feedstock yield, biodiesel productivity, and biodiesel quality. This study aims to identify the machine learning technologies useful in particular areas of supply chain management by review of the scientific literature. As a result, nine machine learning algorithms, the Gaussian process model (GPM), random forest (RF), artificial neural network (ANN), support vector machine (SVM), k-nearest neighbor (KNN), AdaBoost regression, multiple linear regression (MLR), linear regression (LR). and multilayer perceptron (MLP), are used for feedstock yield estimation, biodiesel productivity prediction, and biodiesel quality prediction. Among these, RF and ANN were identified as the most appropriate algorithms, providing high prediction accuracy. This finding will help engineers and managers understand concepts of machine learning technologies so they can use appropriate technology to solve operational problems in supply chain management.

A hybrid framework is proposed to identify the optimal design of agrophotovoltaic (APV) system that can be a promising alternative to resolve the food security issue by producing both solar energy and crops. It consists of four components: (1) Environmental database involving historical climate and soil data, (2) Solar energy module estimating energy quantity via polynomial regression (PR), (3) ALMANAC simulation that estimates crop parameters and yields; and (4) Analysis module identifying the optimum operational plan under climate change scenarios. The framework is calibrated with historical data collected from the APV system at the Jeollanamdo Agricultural Research and Extension Services (35.0161° N, 126.7108° E) in South Korea. Five crops of sesame, mungbean, red bean, corn, and soybean are considered under four climate change scenarios (i.e., SSP126, SSP245, SSP370, and SSP585) with two different time horizons (i.e., 2021–2050 and 2051–2080). According to the experiment, the APV system with mungbean is the most profitable with the unit profit of $ 77.44/m2 under 25.6% shading ratio from 2021 to 2050. The novel framework for the optimal design of the APV system enables to increase the income of a famer and resolve the food security under climate change environment in future.

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.

The Agrophtovoltaic (APV) system is an alternative for sustainable crop production, where solar power is generated via Photovoltaic (PV) modules. Since both crop production and solar power generation activities are heavily dependent on dynamic environmental conditions, it is challenging to design an APV system based on accurate estimation of its performance. To this end, this study aims to introduce an agent-based simulation (ABS) framework integrated with polynomial regression and ridge regression. In particular, two agent types are devised, as follow: (1) The photovoltaic agent calculates electricity produced via PV modules, and estimates its profits; and (2) the crop production agent calculates crop harvests underneath the PV modules, and estimates their profits. To validate the proposed framework, field experiment data with five types of crops (i.e., corn, sesame, soybean, mung bean, and red bean) at the APV system at Jeollanam-do Agricultural Research and Extension Services in South Korea with three different shading ratios of 21.3%, 25.6%, and 32.0% have been used. In addition, for the sustainable operation of an APV system in terms of electricity generation as well as crop production, three climate change scenarios based on the shared socioeconomic pathway (SSP) are considered. The proposed framework identifies that the agrophotovoltaic system with 32% sharing ratio increases up to 20% of the total profit of normal farmland. As a result, the proposed framework enables the performance of an APV system under dynamic climate conditions to be accurately estimated, so that APV system designers can utilize it to identify a profitable long-term APV system.

Agrophotovoltaic (APV) systems produce both solar energy and crops, so they are considered a sustainable alternative to traditional solar power plants, which can potentially destroy farmlands. However, it is challenging to diffuse APV systems because of their high installation and operating costs. Thus, to resolve the issue by maximizing the productivity and profits of an APV system, this study aims to propose a mobile-phone-based decision support system (DSS) for a supply chain network design for APV systems in South Korea using satellite imagery incorporating geographic information system (GIS) data. Particularly, polynomial regression models estimating annual corn (Zea mays) yields and the predicted generation of electricity were developed and integrated with the proposed DSS. Field experiment data provided by the APV system at Jeollanamdo Agricultural Research and Extension Services in South Korea were utilized. Two photovoltaic (PV) module types (mono-facial and bi-facial) and three different shading ratios for APV systems (21.3%, 25.6%, and 32.0%) were considered design factors for APV systems. An optimal network structure of 6 candidate APV systems and 15 agricultural markets was devised using the generalized reduced gradient (GRG) method. The profits of the six candidate APV systems are mainly affected by the transportation costs to the markets and the policy of the electricity selling prices. As a result, the proposed supply chain design framework successfully identifies an APV system network with maximum profits from crop production as well as electricity generation.