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The need for accurate estimates of reference crop evapotranspiration (ETo) is important in irrigation planning and design, irrigation scheduling, reservoir management among other applications. ETo can be accurately determined using the internationally accepted FAO Penman–Monteith (FAO-56 PM) equation. However, this requires numerous observed data, including solar radiation, air temperature, relative humidity, and wind speed, which in most cases are unavailable, particularly in developing countries such as the Philippines. This study developed models based on Support Vector Machines (SVMs) and Extreme Learning Machines (ELMs) for the estimation of daily ETo using different input combinations of meteorological data in Region IV-A, Philippines. The performance of machine learning models was compared with the different established alternative empirical models for ETo. The results show that the SVM and ELM models, with at least Tmax, Tmin, and Rs as inputs, provide the best daily ETo estimates. The accuracy of machine learning models was also found to be superior compared to the empirical models given with same input requirements. In general, SVM and ELM models showed similar modeling performance, although the former showed lower run time than the latter.

Reference crop evapotranspiration (ET O ) is a basic component of the hydrological cycle and its estimation is critical for agricultural water resource management and scheduling. In this study, three tree-based machine learning algorithms (random forest [RF], gradient boosting decision tree [GBDT], and extreme gradient boosting [XGBoost]) were adopted to determine the essential factors for ET O prediction. The tree-based models were optimized using the Bayesian optimization (BO) algorithm and were compared with three standalone models in terms of daily ET O and monthly mean ET O estimations in North China, with different input combinations of essential variables. The results indicated that solar radiation ( R s ) and air temperature ( T s ), including the maximum, minimum, and average temperatures, in daily ET O were the key variables affecting model prediction accuracy. R s was the most influential factor in the monthly average ET O model followed by T s . Both relative humidity (RH) and wind speed at 2 m ( U 2 ) had little impact on ET O prediction at different scales, although their importance differed. Compared with the GBDT and RF models, the XGBoost model exhibited the best performance for daily ET O and monthly mean ET O estimations. The hybrid tree-based models with the BO algorithm outperformed standalone tree-based models. Overall, compared with the other inputs, the model with three inputs ( R s , T s , and RH/ U 2 ) had the highest accuracy. The BO-XGBoost model exhibited superior performance in terms of the global performance index (GPI) for daily ET O and monthly mean ET O predictions and is recommended as a more accurate model for predicting daily ET O and monthly mean ET O in North China or areas with a similar climate.

Climate change and human activities have massively impacted the hydrological cycle. Thus, it is of the greatest concern to examine the effect of climate change on water management, especially at the regional level, to understand the possible future shifts in water supply and water-related crises and support regional water management. Fortunately, there is a high degree of ambiguity in determining the effect of climate change on water requirements. In this paper, the statistical downscaling (SDSM) model is applied to simulate the potential impact of climate on crop water requirements (CWR) by downscaling ET 0 in the region of Western Maharashtra, India, for the future periods, viz., the 2030s, 2050s, and 2080s, across three meteorological stations (Pune, Rahuri, and Solapur). Four crops, i.e., cotton, soybean, onion, and sugarcane, were selected during the analysis. The Penman-Monteith equation calculates reference crop evapotranspiration (ET 0 ). Furthermore, in conjunction with the crop coefficient ( K c ) equation, it calculates crop evapotranspiration (ET c )/CWR. The predictor variables were extracted from the National Centre for Environmental Prediction (NCEP) reanalysis dataset for 1961–2000 and the HadCM3 for 1961–2099 under the H3A2 and H3B2 scenarios. The results indicated by SDSM profound good applicability in downscaling due to satisfactory performance during calibration and validation for all three stations. The projected ET 0 indicated an increase in mean annual ET 0 compared to the present condition during the 2030s, 2050s, and 2080s. The ET 0 would increase for all months (in summer, winter, and pre-monsoon seasons) and decrease from June to September (monsoon season). The estimated future CWR shows variation in the range for cotton (− 0.97 to 2.48%), soybean (− 2.09 to 1.63%), onion (0.49 to 4.62%), and sugarcane (0.05 to 2.86%). The significance of this research lies in its contribution to understanding the potential impacts of climate change at a regional level. This study provides valuable insights into the expected changes in water demand for key crops. The research also manifests implementing an identical methodology for downscaling other environmental parameters using a similar approach.

The crop coefficient (Kc) values given in FAO-56 report need to corrected for local conditions due to differences in climate, soil types, and water management practices. Therefore, present study was undertaken to develop growth-stage-specific Kc values of wheat grown under drip irrigated conditions for two years (2022-23 and 2023-24) at Jalandhar, Punjab. The developed Kc values are 0.36, 0.77, 1.05, 0.69, and 0.25 during initial, development, mid, late, and end growth stages, respectively. These average Kc values can be effectively utilized to schedule irrigation for drip-irrigated wheat crops in the Jalandhar region of Punjab. Irrigating wheat crop under drip irrigation with developed Kc values not only enhances grain yield by 36% but also improves IWUE by three times and saves 61% use of irrigation water as compared to conventional irrigation practices (flood irrigation).

A field experiment was conducted at Lovely Professional University, Phagwara, Punjab for two years (2022 and 2023) to study the response of tomato crop to drip irrigation scheduling based on climatological approach viz. Penman-Monteith, Blaney-Criddle and pan evaporation. Result revealed that, the all treatments of irrigation scheduling were found feasible for optimizing tomato yield (30.8 to 44.6 t ha-1), water saving (27 to 50.7%) and water use efficiency (1.31 to 1.61 t ha-1-cm) under drip irrigation over soil moisture depletion approach. In selected region the average daily ET0 (4.4 to 9 mm day-1) and ETC (2.5 to 10.8 mm day-1) varies with different growth stages and results varying crop water demand of tomato. This water demand can successfully meet out by applying water at 100 % ET0 based on Penman-Monteith method with significant crop yield (44.6 t ha-1) and water use efficiency (1.33 t ha-1-cm). Correlation analysis indicated that, in case of other regions where the availability of weather parameters will be limited for irrigation scheduling, the farmer could irrigate their tomato crop at 90% ET0 based on daily pan evaporation method under drip irrigation. In selected region, the Blaney-Criddle method was found ineffective and shows under and overestimated values of daily ETo during mid stage and late stage which gives more water saving (up to 51%) but reduces significant tomato yield over Pan Evaporation method.

Predicting the impact of future climate change on food security has important implications for sustainable food production. The 26 meteorological stations’ future climate data in the study area are assembled from four global climate models under two representative concentration pathways (RCP4.5 and RCP8.5). The future maize yield, actual crop evapotranspiration (ETa), and water use efficiency (WUE) were predicted by calibrated AquaCrop model under two deficit irrigation (the regulated deficit irrigation (RDI) at jointing stage(W1), filling stage(W2)), and full irrigation (W3) during the three periods (2021–2040, 2041–2060, and 2061–2080). The result showed that the maize yields under W1, W2, and W3 of RCP4.5 were 2.8%, 2.9%, and 2.5% lower than those in RCP8.5, respectively. In RCP8.5, the yield of W3 was 1.9% and 1.4% higher than W1 and W2, respectively. Under the RCP4.5, the ETa of W1, W2, and W3 was 481.32 mm, 484.94 mm, and 489.12 mm, respectively. Moreover, the ETa of W1 was significantly lower than W2 under the RCP4.5 and RCP8.5 (p > 0.05). In conclusion, regulated deficit irrigation at the maize jointing stage is recommended in the study area when considering WUE.

Effective management is crucial to achieve the high yield potential of quinoa (Chenopodium quinoa Willd.), renowned for its resilience in harsh environments, to meet the rising global demand. The present study examines how varying levels of water and nitrogen affect the agronomic and yield traits of quinoa (cv. Q-36) during the two growing seasons of 2020/2021 and 2021/2022. The experiment was a 3 × 4 factorial laid out in a randomized complete block design with three replications per treatment during the two seasons of the study, as water regimes were considered the main factor, including 100%, 80%, and 60% ETc, whereas nitrogen levels were considered the sub-plot factor, comprising four levels (75, 150, 225, and 300 kgN ha−1). The analysis of variance indicated that nitrogen level, irrigation regime, and irrigation regime × nitrogen level had highly significant effects (p < 0.001) on all studied traits, including plant height, panicle length, dry weight, seed weight, seed yield, and total yield in the two growing seasons under study. For all traits of study, the combined application of 100% ETc with 300 kgN, followed by 80% ETc with 225 kgN, resulted in the highest value of plant height, panicle length, dry weight, seed weight, seed yield, and total yield, whereas the combination of 60% ETc and 75 kgN applications resulted in the lowest value for all of the aforementioned traits. Furthermore, the water regime impacted water productivity at all nitrogen levels as the highest productivity level was recorded under the 80% ETc (0.58 kg/m3), followed by the 100% ETc (0.54 kg/m3), and the 60% ETc (0.52 kg/m3). The highest water productivity rate was observed at 300 kg/ha of the nitrogen levels for 60% and 80% ETc regimes, where water productivity levels were 0.73 and 0.71 (kg/m3), respectively. The results also indicate that the water productivity of quinoa plants is noticeably affected by both water regime and nitrogen level; as the water regimes decrease from 100% to 60% ETc, water productivity increases for all nitrogen levels. The information obtained from these results can be applied to optimize the methods for cultivating quinoa under conditions of water scarcity and minimal nitrogen availability, thus gaining an insight into the impact of these conditions on quinoa growth and yield.

Obtaining accurate estimates of reference crop evapotranspiration (ET0) using limited climatic inputs is essential in data-short situations where the preferred FAO-56 Penman–Monteith (PM) equation cannot be implemented. Among several available for ET0 estimation, the empirical temperature-based Hargreaves–Samani (HG) equation remains a popular alternative. However, accurate HG estimates can be obtained by local calibration and replacing the mean daily temperature with the effective daily temperature. Therefore, the present study was taken up to evaluate the effects of site-specific calibration of model parameters and the use of effective air temperature on the accuracy of ET0 estimates by the HG model. For this purpose, climate records for the historical period 2006–2016 of 67 stations located across 10 agro-climatic zones of Karnataka State, India, were used and the analysis was carried out using a monthly time step. Calibration and statistical performance evaluation was performed using FAO-56 PM ET0 estimates as a reference. Overall results showed significant improvement in HG estimates across all zones with the use of locally calibrated parameters, whereas the use of effective air temperature did not lead to any significant gain in prediction accuracies. The derived information on the spatial distribution of calibrated parameters will help obtain accurate ET0 estimates with only air temperature inputs.

The two-step and one-step models for calculating evapotranspiration of maize were evaluated in a semi-humid and drought-prone region of northern China. Data were collected in the summers of 2013 and 2014 to determine relative model accuracy in calculating maize evaopotranspiration. The two-step model predicted daily evaoptranspiration with crop coefficients proposed by FAO and crop coefficient calibrated by local field data; the one-step model predicted daily evapotranspiration with coefficients derived by other researcher and coefficients calibrated by local field data. The predicted daily evapotranspiration in 2013 and 2014 growing seasons with the above two different models was both compared with the observed evapotranspiration with eddy covariance method. Furthermore, evapotranspiration in different growth stages of 2013 and 2014 maize growing seasons was predicted using the models with the local calibrated coefficients. The results indicated that calibration of models was necessary before using them to predict daily evapotranspiration. The model with the calibrated coefficients performed better with higher coefficient of determination and index of agreement and lower mean absolute error and root mean square error than before. And the two-step model better predicted daily evapotranspiration than the one-step model in our experimental field. Nevertheless, as to prediction ET of different growth stages, there still had some uncertainty when predicting evapotranspiration in different year. So the comparisons suggested that model prediction of crop evapotranspiration was practical, but requires calibration and validation with more data. Thus, considerable improvement is needed for these two models to be practical in predicting evapotranspiration for maize and other crops, more field data need to be measured, and an in-depth study still needs to be continued.

FAO Blaney-Criddle has been generally an accepted method for estimating reference crop evapotranspiration. In this regard, it is inevitable to estimate the b-factor provided by the Food and Agriculture Organization (FAO) of the United Nations Irrigation and Drainage Paper number 24. In this study, five soft computing methods, namely random forest (RF), M5 model tree (M5), support vector regression with the polynomial function (SVR-poly), support vector regression with radial basis function kernel (SVR-rbf), and random tree (RT), were adapted to estimate the b-factor. And Their performances were also compared. The suitable hyper-parameters for each soft computing method were investigated. Five statistical indices were deployed to evaluate their performance, i.e., the coefficient of determination (r2), the mean absolute relative error (MARE), the maximum absolute relative error (MXARE), the standard deviation of the absolute relative error (DEV), and the number of samples with an error greater than 2% (NE > 2%). Findings reveal that SVR-rbf gave the highest performance among five soft computing models, followed by the M5, RF, SVR-poly, and RT. The M5 also derived a new explicit equation for b estimation. SVR-rbf provided a bit lower efficacy than the radial basis function network but outperformed the regression equations. Models’ Applicability for estimating monthly reference evapotranspiration (ETo) was demonstrated.