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Further, the original study mainly focuses on the objective of predicting the HT ht of Indian mustard using these techniques without focusing on their mathematical details. • The HT ht value during the validation stage of the MLR model is different in Table 3 and Fig. Further, the discussers pointed out a mistake in the HT ht calculation for the MLR models during the validation stage. The original study employed Kohonen self-organizing map (KSOM), feed-forward neural network (FFNN), and multiple linear regression (MLR) for estimating the well-watered canopy temperature ( HT ht ) of Indian mustard grown in a humid subtropical agro-climate. [Extracted from the article]

The crop water stress index (CWSI) is a reliable indicator of water status in plants and has been utilized for stress monitoring, yield prediction, and irrigation scheduling. Despite this, however, its use is limited because its estimation requires baseline temperatures under similar environmental conditions, which can be problematic. In this study, field crop experiments were performed to monitor the canopy temperature of Indian mustard (Brassica juncea) from crop development through harvest under different irrigation treatment levels during the 2017 and 2018 growing seasons. Kohonen self-organizing map (KSOM), feed-forward neural network (FFNN), and multiple linear regression (MLR) models were developed for estimating the well-watered canopy temperature (Tc-ww) using air temperature and relative humidity as input predictor variables. Comparisons were performed between model-estimated and measured Tc-ww values. The findings indicate that the KSOM-modeled values presented a better agreement with the measured values in comparison to MLR- and FFNN-based estimates, with R2 values of 0.978, 0.924, and 0.923 for KSOM, MLR, and FFNN, respectively, during model validation. The dry canopy temperature was estimated to be air temperature plus 2°C. The CWSI computed using KSOM-based estimates of Tc-ww was compared with the CWSI obtained from measured values of Tc-ww. The results suggest a significant potential of KSOM for reliable estimation of the Tc-ww for calculating the CWSI that can be automated for developing precision irrigation systems. [ABSTRACT FROM AUTHOR]

The core of salinity problems starts from the fact that irrigation waters contain some amount of dissolved salts. Soil moisture salinity is dependent on soil type, climate, water use, and irrigation. The root water-uptake pattern for paddies is studied for saline as well as nonsaline conditions in the present study using a nonlinear root water uptake model. Field crop experiments are carried out using irrigation water with two different levels of salinity (4 and 6.25 dS=m) and fresh water. The effect of salinity on soil moisture uptake is studied by two approaches, i.e., effect on crop coefficient and effect on hydraulic conductivity. Based upon the experimental observations for low-saline (4 dS=m), high-saline (6.25 dS=m), and freshwater conditions, an exponential form of an equation is established for the hydraulic conductivity. The results obtained for soil moisture depletion in the crop root zone show significant improvement in prediction of soil moisture uptake for saline cases with the use of the obtained nonlinearity parameter. [ABSTRACT FROM AUTHOR]

An empirical relationship is developed for the nonlinear root water uptake parameter in the O-R moisture uptake model from easily measurable plant physiological parameters, such as maximum daily transpiration, maximum root depth, and time to attain the maximum transpiration. A nondimensional parameter, termed specific transpiration, that involves the plant physiological parameters is used in this empirical relationship. Data for determining this relationship are obtained by minimizing the deviations between the field observed moisture depletions of 28 crops reported in the literature, and the Richards equation-based numerically simulated soil moisture depletions combined with the moisture uptake model accounting for root water uptake. In addition to cross-validation, field experiments on three Indian crops (maize, Indian mustard, and wheat) are conducted to further validate the proposed empirical relationship. Comparisons of model predictions with field observations of soil moisture profiles and moisture depletions in different layers of the root zone show good agreement during different stages of crop growth. The results highlight the utility of the developed equation for modeling root water uptake over a wide range of crops. [ABSTRACT FROM AUTHOR]

Soil-water movement due to root-water uptake, is a key process for plant growth and transport of water in the soil plant system. There are different root-water uptake models to determine the extraction rate of soil moisture by roots. The present study examines the performance of different root-water extraction models using available data as well as data generated under controlled conditions. Data pertaining to moisture uptake in respect to two crops: wheat (Triticum aestivum L.) and maize (Zea mays L.) along with soil-water characteristics have been monitored at the Indian Institute of Technology Roorkee, agricultural farm. For this purpose, a numerical model is also formulated by incorporating different moisture extraction terms as sink terms in the Richards equation. A nonlinear root-water uptake model selected as the base model was evaluated for its moisture uptake efficiency. The work establishes the merits of the base model over other extraction terms considered, particularly constant and linear extraction terms in predicting the soil moisture depletion in the root zone. The work stresses the nonlinearity parameter of the base model, which is capable of defining crop specific nonlinearity in the plant moisture uptake. [ABSTRACT FROM AUTHOR]