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The article estimating crop water requirements for irrigation scheduling in different crops in humid subtropical agro-climate of Western Himalayas . Topics discussed include estimating crop water requirements for irrigation scheduling in different crops in humid subtropical agro-climate of Western Himalayas ; and the crop water requirement is generally crop evapotranspiration considering atmospheric water loss through plant transpiration and soil evaporation simultaneously.

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]

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]

Precise estimation of evapotranspiration obtained by multiplying crop coefficient to reference evapotranspiration using readily available climatic data. Penman-Monteith equation is adopted world wide as the most reliable and accurate method for computing reference evapotranspiration. The Food and Agriculture Organization (FAO) presented crop coefficient for estimation of evapotranspiration of different crops based on Penman-Monteith equation. The crop coefficient curves can be developed by plotting the ratios of crop evapotranspiration and reference evapotranspiration with respect to time. The crop coefficient curves were developed for mustard (Brassica juncea) by FAO-56 curve method and modified FAO-56 curve method. The FAO-56 curve method underestimates mustard evapotranspiration by 16.80 per cent. Therefore, FAO-56 curve method does not appear to predict mustard evapotranspiration accurately. The modified FAO-56 curve method underestimates mustard evapotranspiration only by 8.33 per cent. Therefore, performance of modified FAO-56 curve method was found better than FAO-56 curve method for estimation of mustard evapotranspiration. [ABSTRACT FROM AUTHOR]

Efficient irrigation water management requires a good quantification of evapotranspiration. The precise estimation of water requirement of crop is very important factor in the application of irrigation design and scheduling. Water relation model are essential component of all crop model because of critical role of water status has in determining growth, productivity and produce quality. Irrigation futures aim to identify an appropriate model for the calculation of reference crop evapotranspiration. Different climatological methods are using for estimating reference crop evapotranspiration on a daily basis. Some of these methods are based on combination theory and others are empirical methods based primarily on solar radiation, temperature and relative humidity. This paper evaluate and review the use of different evapotranspiration models and data in studies of geographical ecology it is also used in the estimation of daily water requirements for agricultural crops grown in different climatic regions of India and worldwide. [ABSTRACT FROM AUTHOR]

Efficient irrigation water management requires a good quantification of evapotranspiration. Lysimeter was used to measure actual crop water use and local weather data were used to determine the reference evapotranspiration (ETo). The K c values determined over the growing seasons varied from 0.5 to 1.15 for pea. The development of regionally based and growth-stage-specific Kc helps in irrigation management and provides precise water applications for this region. Six climatological models were selected for estimating reference crop evapotranspiration on a daily basis. Some of these methods are based on combination theory and others are empirical methods based primarily on solar radiation, temperature and relative humidity. According to results the crop coefficient vary among locations and even among years, depending on soil evaporation (rainfall, irrigation), vapour pressure deficit, solar radiation and reference evapotranspiration (ET0). [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]

• SOM based model is developed for predicting the CWSI using environmental variables. • Predicted CWSI correlated well with the observed CWSI for common field moisture conditions. • SOM model performance is impacted by significant proportion of zero CWSI values in the dataset. Crop water stress index (CWSI) is a reliable, economic and non-destructive method of monitoring the onset of water stress for irrigation scheduling purposes. Its application, however, is limited due to the need of obtaining the baseline canopy temperatures. This study developed a self-organizing map (SOM) based model to predict the CWSI using microclimatic variables, namely air temperature, canopy temperature and relative humidity. The canopy temperature measurements were made from Indian mustard crop grown in a humid sub-tropical agro-climate during the 2017 and 2018 cropping seasons. Eight levels of irrigation treatments (I 1 – I 8) based on maximum allowable depletion of available soil water were considered in the study. The CWSI for treatments I 2 – I 7 was computed using the empirical approach based on the experimentally measured baseline canopy temperatures from treatments I 1 and I 8. The number of data points used was 1260 and 1350 for model training and testing, respectively. The developed SOM model was evaluated using the error indices Nash-Sutcliffe efficiency (NSE), bias error (BE), absolute error (AE), and coefficient of determination (R2). The SOM predicted CWSI presented a good agreement with the baseline computed CWSI values during model training (R2 = 0.98, NSE = 0.97, AE = 0.018, BE = 0.0004) and testing (R2 = 0.98, NSE = 0.98, AE = 0.018, BE = 0.002). Treatment specific analysis was conducted to evaluate the performance of SOM predicted CWSI for different irrigation levels. Results indicated that the presence of zero CWSI values in a significant proportion in the dataset impacted the model prediction performance at low CWSI (<0.1) values, with an R2 of 0.71 during testing. Nonetheless, the model performed exceptionally well in predicting CWSI values between 0.1 and 0.6 (R2 = 0.93–0.98, NSE = 0.92–0.98, AE = 0.013–0.015, BE = −0.002–0.004), which is the commonly observed CWSI range for irrigation scheduling in field crops. For better understanding, the developed SOM model was also analysed through the component planes, U-matrix, clusters and high-low bar planes in the cluster features. [ABSTRACT FROM AUTHOR]