A linear regression machine learning model to estimate the reference evapotranspiration based on temperature data for South Korea is developed in this study. FAO56 Penman–Monteith (FAO56 P–M) reference evapotranspiration calculated with meteorological data (1981–2021) obtained from sixty-two meteorological stations nationwide is used as the label. All study datasets provide daily, monthly, or annual values based on the average temperature, daily temperature difference, and extraterrestrial radiation. Multiple linear regression (MLR) and polynomial regression (PR) are applied as machine learning algorithms, and twelve models are tested using the training data. The results of the performance evaluation of the period from 2017 to 2021 show that the polynomial regression algorithm that learns the amount of extraterrestrial radiation achieves the best performance (the minimum root-mean-square errors of 0.72 mm/day, 11.3 mm/month, and 40.5 mm/year for daily, monthly, and annual scale, respectively). Compared to temperature-based empirical equations, such as Hargreaves, Blaney–Criddle, and Thornthwaite, the model trained using the polynomial regression algorithm achieves the highest coefficient of determination and lowest error with the reference evapotranspiration of the FAO56 Penman–Monteith equation when using all meteorological data. Thus, the proposed method is more effective than the empirical equations under the condition of insufficient meteorological data when estimating reference evapotranspiration.

Effect of Abalone Hydrolysates Encapsulated by Double Emulsion on the Physicochemical and Sensorial Properties of Fresh Cheese double emulsion;fresh cheese;salt enhancer;abalone hydrolysate; The intake of dietary salt through food now exceeds current nutritional recommendations and is thought to have negative effects on human health, such as the increasing prevalence of hypertension. This study was performed to investigate whether $W_1/O/W_2$ double emulsions can be used to enhance the saltiness of cheese without increasing the salt content ($W_1$ is distilled water or 1% abalone hydrolysate, and $W_2$ is 1% NaCl or 1% abalone hydrolysate + 1% NaCl solution). We also investigated the effect of adding abalone hydrolysate to the double emulsion as a saltiness enhancer. The cheeses were physico-chemically evaluated to determine curd yield, pH value, moisture content, color, texture, salt release rate, and sensory properties. No significant differences were observed in curd yield, pH value, moisture content, lightness, or redness between the cheeses made with and without the double emulsion. However, in the evaluation of salt release rate, fresh cheese made with double emulsion ($W_1$ = distilled water, $W_2$ = 1% NaCl + 1% abalone hydrolysate) was detected earlier than the control or the other treatments. In the sensory evaluation, fresh cheese made with the double emulsion showed higher scores for saltiness and overall preference than the control or the other treatments. We concluded that abalone hydrolysate encapsulated in a double emulsion ($W_1$ is water and $W_2$ is abalone hydrolysate and NaCl solution) could enhance the saltiness of fresh cheese while maintaining the same salt concentration, without altering its physical properties.

This study assessed the effects of irrigation with desalinated water on the growth of lettuce (Lactuca sativa L.). Two experiments, the first using saline and the second desalinated irrigation water, respectively, were designed to grow lettuce in plots (22 m × 0.4 m × 0.4 m) under controlled greenhouse conditions. Three levels of saline irrigation water and tap water (control) were used for the first experiment. In the second experiment, the three saline levels underwent a desalination process. Each experiment was carried out twice, in succession, with two replications. The results from the first experiment showed that the use of saline irrigation water caused an increase in the salinity level of soil and, consequently, adversely affected lettuce growth and yields. The results from the second experiment showed that the use of desalinated irrigation water does not negatively affect soil salinity and lettuce growth and yield. However, the need for additional application of the elements such as Ca²⁺ and Mg²⁺ was also identified since continued use of desalinated water irrigation correlated with a decrease in the sodium adsorption ratio (SAR), leading to increased risk of soil sodicity. This is due to the elimination of nutrients during the desalination process.

Storage rate forecasting for the agricultural reservoir is helpful for preemptive responses to disasters such as agricultural drought and planning so as to maintain a stable agricultural water supply. In this study, SVM, RF, and ANN machine learning algorithms were tested to forecast the monthly storage rate of agricultural reservoirs. The storage rate observed over 30 years (1991–2022) was set as a label, and nine datasets for a one- to three-month storage rate forecast were constructed using precipitation and evapotranspiration as features. In all, 70% of the total data was used for training and validation, and the remaining 30% was used as a test. The one-month storage rate forecasting showed that all SVM, RF, and ANN algorithms were highly reliable, with R² values ≥ 0.8. As a result of the storage rate forecast for two and three months, the ANN and SVM algorithms showed relatively reasonable explanatory power with an average R² of 0.64 to 0.69, but the RF algorithm showed a large generalization error. The results of comparing the learning time showed that the learning speed was the fastest in the order of SVM, RF, and ANN algorithms in all of the one to three months. Overall, the learning performance of SVM and ANN algorithms was better than RF. The SVM algorithm is the most credible, with the lowest error rates and the shortest training time. The results of this study are expected to provide the scientific information necessary for the decision-making regarding on-site water managers, which is expected to be possible through the connection with weather forecast data.

Paddy rice constitutes a staple crop in Korea. This study conducted sensitivity analysis to evaluate the vulnerability of paddy rice to future climate change, and compared temporal and regional characteristics to classify regions with unfavorable water balances. Rainfall Effectiveness Index for Paddy fields (REIP), the ratio of effective rainfall and consumptive use, was used as a sensitivity index. Weather data from 1971 to 2010 and future climate change scenarios Representative Concentration Pathways (RCP) 4.5 and 8.5 were used to evaluate the sensitivity. Results showed an overall increase in water requirements and consumptive use. The REIP values were small for every period, except the 2040s, 2060s, and 2080s under scenario RCP 4.5, and the 2040s and 2080s under scenario RCP 8.5. Both climate change scenarios showed high sensitivity in regions Jeollabuk-do, Jeollanam-do, and Gyeongnam-do. However, regions Gyeonggi-do, Gangwon-do, and Chungcheongbuk-do had low sensitivity compared to other regions. The REIPs were used to categorize sensitivity into four types: low consumption–water rich, low consumption–water poor, high consumption–water rich, and high consumption–water poor. The Gangwon-do region had the highest number of regions that changed from the low consumption–water rich category to the high consumption-water poor category, making it a priority for measures to improve its adaptive capacity for climate change.