Prediction of the seasonal occurrence and potential distribution of agricultural pests has accomplished by software toolsimplementing species distribution models (SDMs). In this aspect, we used CLIMEX software to evaluate the seasonaloccurrence and potential distribution of Indian meal moth, Plodia interpunctella (Hübner), which is one of household mothsdamaging dried fruits in pantries. Based on the simulation, the beginning of period for suitable climate was predictedto be from mid-March to end-March, while it might be end in late October to early November. The peak time for P.interpunctella was ranged from early or mid-July to mid-August, but depended on local geography. When applying RCP8.5 climate change scenario, it was predicted that P. interpunctella would not occur due to intensive rainfall in July andAugust in 2060.

Invasive pests have posed an ecological threat as climate change has been accelerated, suggesting early prediction ofinvasive pests is required to minimize damages by them. As one of predictive tools, CLIMEX has been effectively usedin a few regions, including US, Australia, and Europe. It allows us to predict a species distribution on a local area inresponse to climatic conditions: and thus, potential distribution of invasive species, risk assessment of agricultural pests,and suitability of biological control agents have been tested by CLIMEX. In this study, we introduced how to use CLIMEXfor predicting a species distribution differed by climate change in terms of its functions, required data, and examplesof its application.

Response surface methodology (RSM) is one of the statistical methods for optimizing dependent variable in response to independent variables, and has been used in various field of food engineering. The model coded by RSM has a canonical formulation of 2nd order polynomial with the normalized ranges of independent variables. To accurately accomplish the optimization using RSM an adequate experimental design, i.e., response surface design, is necessary. Response surface design is determined by type of design and number of independent variables. In this study, we are to develop a response surface design applicable for optimizing hulled barley (Hordeum vulgare) production under various conditions of temperature and humidity in forage growing system. As a result, 3 experimental designs were conceived for future RSM; central composite design (CCD), inscribed central composite design (ICCD) and equiradial design. Each design requires experimental trials of 13, 13, and 8, respectively. We will further select one of the designs for actual experiments for finding the optimal temperature and humidity necessary for maximizing fresh forage production in the system.

Beneficial effects of glucosinolate in Chinese cabbage on human health have propelled researches on biochemical and genetic characteristics of glucosinolate and Chinese cabbage. However, growth conditions which are practically important in producing functional Chinese cabbage and in optimizing glucosinolate have been little focused. The objective of this study, hence, was to identify growth conditions affecting glucosinolate contents so that the result could be used for further optimizing glucosinolate in Chinese cabbage. We used principal component analysis (PCA) for analyzing glucosinolate contents in Chinese cabbage cultivated under various growth conditions in the plant factory. As a result, PCA showed that two principal components were able to explain more than 76% of variations in glucosinolate contents caused by different growth conditions. The first principal component (PC1) was mainly represented by humidity and temperature, while growth duration was the main component of PC2. From these results, it was conclusive that glucosinolate contents in Chinese cabbage were largely affected by humidity, temperature and growth duration.

Chinese cabbage, a traditional food source in Asia, has been highlighted due to its functional aspects. In particular, breakdown products of glucosinolates (GSLs) that contribute to the unique flavor and fragrance of Brassicaceae vegetables and act in plant defense mechanisms has been known as an anti-cancer agent. The objective of this study was to investigate optimal condition to grow Chinese cabbage with focusing on enhancing glucosinolates contents. The main tools for our analysis were correlation analysis and principal component analysis (PCA), and they were applied into a data obtained from Chinese cabbage grown in the plant factory. The data included glucosinolate contents and growth conditions (day, temperature, humidity, and EC). As a result of correlation analysis, day showed the largest significance with glucosinolates, followed by EC and temperature. The results of PCA showed that three principal components were able to explain more than 75% of variations in glucosinolate contents caused by different growth conditions. The first principal component (PC1) was mainly represented by day, EC, and temperature, while the main factors in the second (PC2) and the third (PC3) components were represent humidity, and , respectively. In conclusion, glucosinolate contents in Chinese cabbage were largely affected by day, EC, and temperature during the growth.

RSM (response surface method) is a statistical method that optimizes a response variable (dependent variable) according to multiple explanatory variables (independent variable) [1]. RSM visualizes responses of the target depending on experimental conditions, using a regression equation containing an intercept, and coefficients of first-order, second-order, and interactive terms (equation 1). Response surface experimental design is a method for designing RSM experiments [2] which aims to identify the optimal number of trials (number of data points) and number of conditions (range of experimental variables) according to the order of the regression model. Generally, the number of trials in an experiment is composed of central points, factorial points, and axial (or star) points, which varies depending on the number of variables. In this study, we used three widely used response surface experimental designs, i.e., simplex, central composite, and equiradial designs to propose experimental set-up applicable for a future study regarding the effects of storage conditions (e.g., temperature and humidity) on glucosinolate content.

Calcium is an essential nutrient generally obtained from dietary foods. However, its metabolism is a complex biological network requiring an effective analytical tool. In this review, a few notable calcium mechanistic models are assessed, emphasizing the power of mechanistic modeling in analyzing biological systems because no publication has reviewed them in a single place. Reviewed models are categorized by target systems: calcium absorption, calcium excretion, bone turnover, and whole calcium homeostasis. In the calcium absorption model, two transport systems carrying calcium from intestine to blood have been mechanistically described. Since urine calcium excretion is poorly understood, its model has not been explicitly developed although it is modeled as a part of the whole calcium metabolic system. Cell-based bone turnover model has been conceived to describe the mechanism under bone cell regulation. Finally, whole calcium metabolism has been a target to explain the metabolic control of calcium homeostasis that is linked to the endocrine system. Reviewed models focus on explaining how the calcium metabolic system behaves in response to conditional perturbations by involving effective factors such as endocrine system. We expect that not only will this study provide comprehensive information for future studies in calcium metabolism, but also that it will suggest what the concepts of biosystem modeling are and how they can be used for assessing the target system.