Estimated Crop Evapotranspiration Research Articles

Assessment of irrigation water management using FAO-CROPWAT 8 in the Paliganj distributary of the Sone irrigation scheme in Bihar: a case study

ABSTRACT Around 15% of India's GDP comes from agriculture, which is the foundation of the country's economy. India's economic development and efforts to combat poverty have already benefited greatly from its agricultural sector. Sustainable management of water resources can increase agricultural productivity. The study performed in the Paliganj distributary is a part of the Sone canal system in south Bihar, India. The Paliganj distributary command area lies wholly in the Gangetic plain. The estimation of crop evapotranspiration is carried out using the modified Penman–Monteith equation for the period 1980–2015. The crop coefficient of the crops has been estimated by using a single crop coefficient and a dual crop coefficient. The crop water requirement, irrigation water requirement, and irrigation scheduling of crops grown in the study area are determined using CROPWAT 8.0 software. The results conclude that the maximum, minimum, and average values of ET0 were found to be 7.65, 1.31, and 3.86 mm/day, respectively. The crop water requirement and irrigation requirement of crops grown in kharif, rabi, hot weather, and annual seasons in the study area were lower by using dual crop coefficient.

Alternative Solution to Estimate Cucumber Crop Evapotranspiration in Shade Nets for the Valle de Culiacán, Mexico

Objective: This work aimed to estimate the evapotranspiration of cucumber crop grown in a shade house in Valle de Culiacán, México. Design/methodology/approach: The FAO56 method was used with a variation of the FAO Penman-Monteith equation, and a non-conventional evaporimeter to measure evaporation inside the shade house. Using the estimated reference evapotranspiration and the measured evaporation, pan coefficients were calculated, and a mathematical model was proposed to calculate this coefficient based on the meteorological parameters: air temperature, relative humidity, and net solar radiation. Results: The estimated crop evapotranspiration was 417.6 mm. The linear regression between the reference evapotranspiration estimated with FAO56 and calculated with evaporation and adjusted evaporimeter pan coefficient yielded excellent statistical estimators (R2>0.9). Findings/conclusions: The analysis of the evaporimeter pan coefficient's dependence on meteorological parameters indicates a strong dependence on air temperature, net solar radiation, and vapor pressure deficit, but not on relative humidity.

Malt Barley Yield and Quality Response to Crop Water Stress Index

Malt barley is a crucial irrigated crop in the semi-arid Western United States, where the states of Idaho, Colorado, Wyoming, and Utah account for 92% of the irrigated production acreage and 30% of total U.S. production. In this region, spring malt barley’s seasonal evapotranspiration ranges from 400 to 650 mm, and competition for limited water supplies, coupled with drought, is straining regional water resources. This study aimed to investigate the use of canopy temperature for deficit irrigation scheduling of malt barley. Specifically, the objectives were to use data-driven models to estimate well-watered (TLL) and non-transpiring (TUL) canopy temperatures, correlate the crop water stress index (CWSI) with malt barley yield and quality measures, and assess the applicability of CWSI for malt barley irrigation scheduling in a semi-arid climate. A 3-year field study was conducted with five irrigation treatments relative to estimated crop evapotranspiration (full, 75%, 50%, 25%, and no irrigation) and four replicates each. Continuous canopy temperature measurements and meteorological data were collected, and a feedforward neural network model was used to predict TLL, while a physical model was used to estimate TUL. The neural network model accurately predicted TLL, with a strong correlation (R2 = 0.99), a root mean square error of 0.89 °C, and a mean absolute error of 0.70 °C. Significant differences in calculated season-average CWSI were observed between the irrigation treatments, and relative evapotranspiration, malt barley relative yield, test weight, and plump kernels were negatively correlated with the season-average CWSI, while seed protein was positively correlated. The relationship between daily CWSI and fraction of available soil water was well described by an exponential decay function (R2 = 0.72). These results demonstrate the applicability of data-driven models for computing CWSI of irrigated spring malt barley in a semi-arid environment and their ability to assess plant water stress and predict crop yield and quality response from CWSI.

Assessment of reference evapotranspiration forecasting in the Mediterranean climate of Central Chile using the ASCE standardized Penman-Monteith equation, the Hargreaves-Samani equation, and weather predictions from the Global Forecast System model

Accurate estimation of crop evapotranspiration (ETc) is essential for effective agricultural water resource planning. To determine ETc it is common multiplying the reference evapotranspiration (ETref) by an appropriate crop coefficient (Kc). Forecasting ETref could be particularly beneficial for irrigation scheduling. In this work, daily and cumulative (7 days) ETref predictions were estimated with the Penman-Monteith (PM) and the Hargreaves-Samani (HS) models using environmental variables obtained from the Global Forecast System (GFS). Results were compared with ETref calculated with the ASCE (American Society of Civil Engineers) PM equation from measured daily values of solar radiation, air temperature, relative humidity and wind speed. Four weather stations located in Central Chile during 2020 to 2022 were used. The best results were obtained using the HS equation, for all weather stations, the statistical indicators for daily ETref predictions fall within the following ranges: Accuracy (defined as the percentage of daily ETref predictions with an absolute error within 1mmd−1) varies between 72.8% and 91.8%; the root mean square error (RMSE) ranges from 0.59mmd−1 to 0.98mmd−1; the normalized root mean square error (NRMSE) is between 16.1% and 24.1%; the mean bias error (MBE) is between 0.10mmd−1 and 0.36mmd−1; and the normalized mean bias error (NMBE) varies from 2.5% to 8.3%. On the other hand, the indicators for the 7-day cumulative ETref forecasts were: RMSE from 2.86mm to 5.08mm; NRMSE from 11.0% to 16.3%; MBE from 0.66mm to 2.57mm; and NMBE from 2.4% to 8.3%. These results suggest that HS ETref values predicted from GFS temperature forecasts could be effective for foreseeing near-future water demand and enhancing the accuracy of irrigation scheduling and management in Central Chile.

Prediction of Reference Crop Evapotranspiration in China’s Climatic Regions Using Optimized Machine Learning Models

The accurate estimation of reference crop evapotranspiration (ET0) is essential for crop water consumption modeling and agricultural water resource management. In the present study, three bionic algorithms (aquila optimizer (AO), tuna swarm optimization (TSO), and sparrow search algorithm (SSA)) were combined with an extreme learning machine (ELM) model to form three mixed models (AO-ELM, TSO-ELM, and SSA-ELM). The accuracy of the ET0 estimates for five climate regions in China from 1970 to 2019 was evaluated using the FAO-56 Penman–Monteith (P-M) equation. The results showed that the predicted values of the three mixed models and the ELM model fitted the P-M calculated values well. R2 and RMSE were 0.7654–0.9864 and 0.1271–0.7842 mm·d−1, respectively, for which the prediction accuracy of the AO-ELM model was the highest. The performance of the AO-ELM combination5 (maximum temperature (Tmax), minimum temperature (Tmin), total solar radiation (Rs), sunshine duration (n)) was most significantly improved on the basis of the ELM model. The prediction accuracy for the stations in the plateau mountain climate (PMC) region was the best, while the prediction accuracy for the stations in the tropical monsoon climate region (TPMC) was the worst. In addition to the wind speed (U2) in the temperate continental climate region (TCC)—which was the largest variable affecting ET0—n, Ra, and total solar radiation (Rs) in the other climate regions were more important than relative humidity (RH) and wind speed (U2) in predicting ET0. Therefore, AO-ELM4 was selected for the TCC region (with Tmax, Tmin, Rs, and U2 as inputs) and AO-ELM5 (with Tmax, Tmin, Rs, and n as inputs) was selected for the TMC, PMC, SMC, and TPMC regions when determining the best model for each climate region with limited meteorological data.

Satellite remote sensing for monitoring citrus orchards water requirements at the irrigation district scale

In Italy, irrigated agriculture is often managed by Reclamation Consortia, but they generally lack the proper tools for monitoring the irrigation water requirements (IWR), forbidding the required sustainability level by the European Water Framework Directive. In this context, the study aims at monitoring the IWR of citrus orchards of a Sicilian irrigation district by implementing a satellite-based methodological approach, during the irrigation seasons 2019 and 2020. Firstly, neural networks were implemented to map the citrus orchards, by using Normalized Difference Vegetation Index time series as input, and obtaining accuracy values of 94% and 87%, in 2019 and 2020, respectively. Then, the satellite-based ArcDualKc model was used to estimate crop evapotranspiration (ETc) over the identified citrus orchards. The estimated ETc rates were validated at the farm scale in terms of irrigation volumes and compared to those obtained by the traditional FAO-56 approach. Then, the spatially distributed irrigation volumes estimated at district level were compared with those declared by the Reclamation Consortium, obtaining absolute error values of 3.28 Mm3 (294%) and 7.08 Mm3 (647%) for the years 2019 and 2020, respectively. The results of the study confirmed the usefulness of the satellite-based methodological approach for determining spatial distributed IWR estimates.

Short-term effect of different doses of sewage sludge on soil physical and hydraulic properties with different irrigation regimes in a silage maize field

Soil physical and hydraulic properties may be affected significantly from stabilized sewage sludge, and irrigation regimes may change the magnitude of these effects. Therefore, we examined the effects of different sewage sludge doses (D0: 0 t/ha, D1: 30 t/ha, D2: 60 t/ha, D3: 90 t/ha) and irrigation regimes (S1, S2, S3) on the soil physical and hydraulic properties with two-year study in a silage maize cultivated soil. The experiment was carried out in a completely random factorial design with three replications. In the S1, S2 and S3 irrigation regimes, when the sum of estimated crop evapotranspiration by FAO-Penman-Monteith approach and effective precipitation difference were 25 mm, 50 mm and 75 mm, respectively, irrigations were carried out and the moisture deficit in the soil was completed to field capacity. While moisture regimes did not have significant effects on soil physical and hydraulic properties, sewage sludge doses resulted in significant effects. Compared to D0, 3.1% lower bulk density, 1.9% lower particle density, 14.9% higher wet aggregate stability and 2.6% higher gravimetric field capacity values were determined at the D3 treatment. It was determined that these improvements were due to the high organic matter content of the sewage sludge, which increased the organic matter content in the soil with increasing doses, and it was also supported by significant linear relationships between the organic matter and these parameters. As a conclusion, although the importance of the positive effects of increasing dose can be emphasized, it can be stated that longer-term studies are needed to see more permanent and effective results.

A comparative study of crop evapotranspiration estimation in maize using empirical methods, pan evaporation and satellite-based remote sensing technique

A research study was conducted at the Agricultural College and Research Institute, Coimbatore to estimate the evapotranspiration (ET) of maize crop (Zea mays) over 2 consecutive seasons in 2022-2023. Among the different methods used to estimate crop evapotranspiration, the Food and Agricultural Organization Penman-Monteith model (FAO P-M) is widely recognized as the standard approach for ET estimation. This study aimed to compare the effectiveness of three alternative methods - Thornthwaite (TW), NDVI-based and pan methods against the FAO Penman-Monteith (P-M) model in estimating maize evapotranspiration. Meteorological data were collected from the TNAU weather station spanning the period from 2022 to 2023.The performance of the estimation methods was assessed using statistical metrics such as coefficient of determination (R2), root mean squared error (RMSE), percentage error and mean bias error. The findings revealed that the NDVI-based method, relying on satellite data, provided higher accuracy in estimating maize evapotranspiration compared to the FAO PM method. Specifically, the NDVI-based method achieved the highest coefficient of determination (R2) of 0.87 and 0.89, the lowest RMSE of 12.44 mm/month and 15.5 mm/month, the lowest percentage error of 4.8 % and 9.00 % and the lowest mean bias error of 5.5 and 7.85 for the first and second seasons respectively. This study highlights the effectiveness of the NDVI-based ET estimation method for accurately assessing maize evapotranspiration. While the FAO-56 Penman-Monteith method is highly regarded for its accuracy in both theoretical and practical contexts, the comparative evaluation presented in this paper offers valuable insights for selecting alternative methods that require less data, particularly in regions with limited data availability.

Estimating Crop Evapotranspiration in Data-Scare Regions: A Comparative Analysis of Eddy Covariance, Empirical and Remote-Sensing Approaches

Estimating Crop Evapotranspiration in Data-Scare Regions: A Comparative Analysis of Eddy Covariance, Empirical and Remote-Sensing Approaches

The effects of drought and salinity on KS and RAW managerial coefficients in the efficient water management in maize farms

This study aimed to investigate the simultaneous effects of drought and salinity on irrigation management coefficients in maize farms. A three-year field research was conducted in the form of a 3 × 3 factorial experiment with a randomized complete block design and three replications from 2020 to 2022 in a maize farm, in Aliabad Fashafoye, Qom province, Iran. The applied treatments included three levels of salinity (S0 = 1.8, S1 = 5.2, and S2 = 8.6 dS/m) and three levels of irrigation (W0 = 100%, W1 = 75%, and W2 = 50% of field capacity). Evapotranspiration stress coefficient (KS) due to W0S1 and W0S2 treatments was (0.975 and 0.934), (0.974 and 0.932), and (0.962 and 0.935) in 2020, 2021, and 2022, respectively. According to the results, KS decreased by increasing the salinity level of irrigation water, so a 1-unit increase in salinity level above the tolerance threshold of the crop to salinity decreased KS by 0.78 and 1.76% for S1 and S2, respectively. Moreover, each percent of volumetric moisture decrease from field capacity decreased KS by 5.9 and 13.3% in W1 and W2, respectively. Also, with the increase in the intensity of the stresses, the readily available water (RAW) of treatments decreased. The sole application of salinity stress decreased the decreasing slope of RAW by 3.2%, while the application of both stresses resulted in the decreasing slopes of 4.9, 5.7, and 7.8% at the salinity levels of S0, S1, and S2, respectively, compared to the control. The findings of this study show that the accurate estimation of crop evapotranspiration and RAW can help to improve the irrigation schedule, and the amount of irrigation water used is less than in non-stress conditions due to the reduction of total evapotranspiration and less water uptake in environmental stresses in maize farms.

Precision modelling of leaf area index for enhanced surface temperature partitioning and improved evapotranspiration estimation

Remote Sensing-based two-source model is widely used to estimate crop evapotranspiration (ET), involving one key step of partitioning land surface temperature (LST) into canopy and soil temperatures (Tc and Ts). Leaf area index (LAI) plays a significant part in available energy allocation during this process. However, the asymptotic saturation problem makes the mismatch between vegetation index and LAI. In this study, two-stage LAI models were developed through the red-edge chlorophyll index (CIred-edge) considering the hysteresis between them. Considering the distinct characteristics, modeling LAI by one-degree linear equations for sunflower (C3), linear and exponential functions for maize (C4) were presented in the distinguished grow-up and senescence periods. The two-source energy balance (TSEB) and hybrid dual-source scheme and trapezoid framework-based evapotranspiration (HTEM) models were selected to estimate Tc, Ts, ET, and its components contrastively. The established LAI models and other modified parameters were then integrated into the two models to improve the estimation of Tc, Ts, and ET (named the R-TSEB and R-HTEM models, respectively). Results demonstrated that the partitioned Tc & Ts became closer to the measurements after utilizing the presented LAI models. For daily ET, the R-TSEB and R-HTEM models alleviated the overestimation and underestimation existing in the original two models, respectively. At monthly and seasonal scales compared to the water balance results (ETwb), the ET of R-TSEB model had significant promotion, including the determination coefficient (R2), mean relative error (RE), root mean square error (RMSE), and model agreement index (d) with values of 0.87, 6.54%, 11.65 mm, and 0.95, from the according values of 0.80, 12.85%, 17.60 mm, and 0.90 for the TSEB model, respectively. The estimated ET by the R-HTEM model was more consistent with ETwb than the HTEM model. These results indicate that the established LAI models can enhance ET estimation and further advance water cycle understanding.

Research on methods for estimating reference crop evapotranspiration under incomplete meteorological indicators.

Accurate estimation of reference crop evapotranspiration (ET0) is crucial for farmland hydrology, crop water requirements, and precision irrigation decisions. The Penman-Monteith (PM) model has high accuracy in estimating ET0, but it requires many uncommon meteorological data inputs. Therefore, an ideal method is needed that minimizes the number of input data variables without compromising estimation accuracy. This study aims to analyze the performance of various methods for estimating ET0 in the absence of some meteorological indicators. The Penman-Monteith (PM) model, known for its high accuracy in ET0 estimation, served as the standard value under conditions of adequate meteorological indicators. Comparative analyses were conducted for the Priestley-Taylor (PT), Hargreaves (H-A), McCloud (M-C), and FAO-24 Radiation (F-R) models. The Bayesian estimation method was used to improve the ET estimation model. Results indicate that, compared to the PM model, the F-R model performed best with inadequate meteorological indicators. It demonstrates higher average correlation coefficients (R2) at daily, monthly, and 10-day scales: 0.841, 0.937, and 0.914, respectively. The corresponding root mean square errors (RMSE) are 1.745, 1.329, and 1.423, and mean absolute errors (MAE) are 1.340, 1.159, and 1.196, with Willmott's Index (WI) values of 0.843, 0.862, and 0.859. Following Bayesian correction, R2 values remained unchanged, but significant reductions in RMSE were observed, with average reductions of 15.81%, 29.51%, and 24.66% at daily, monthly, and 10-day scales, respectively. Likewise, MAE decreased significantly, with average reductions of 19.04%, 34.47%, and 28.52%, respectively, and WI showed improvement, with average increases of 5.49%, 8.48%, and 10.78%, respectively. Therefore, the F-R model, enhanced by the Bayesian estimation method, significantly enhances the estimation accuracy of ET0 in the absence of some meteorological indicators.

Estimating crop coefficients and water use of a full-bearing mango orchard in north-eastern South Africa using the fraction of vegetation cover and a dual source evapotranspiration model

In semi-arid countries like South Africa, commercially produced mango (Mangifera Indica L.) are grown entirely under irrigation. However, there is little accurate quantitative information on the water use of mango orchards, and few accurate water use models currently exist. In this study, we evaluated the performance of the FAO 56 method of estimating crop evapotranspiration (ETc) and a dual source water use model against measurements of actual consumptive water use of a commercial mango orchard. The FAO approach calculates ETc as the product of a crop coefficient (Kc) and the reference evapotranspiration (ETo). We derived Kc for well-watered orchards from readily available data including the fraction of vegetation cover, average tree height, and a stomatal control coefficient. The dual-source model calculated orchard evapotranspiration as the sum of tree transpiration and orchard floor evaporation derived from the modified Shuttleworth and Wallace model. Actual evapotranspiration was measured using an open path eddy covariance system, while tree transpiration was measured using the heat ratio method of monitoring sap flow. Data were collected in a mature ‘Tommy Atkins’ mango orchard grown with micro-sprinkler irrigation in north-eastern South Africa. Results show that the reference evapotranspiration explained most of the variation in orchard transpiration (R2 ∼ 0.78) compared to the solar radiation (R2 ∼ 0.66) and the vapour pressure deficit of the air (R2 ∼ 0.66). Compared with field measured water use data, the FAO method gave relatively accurate estimates of transpiration (R2 = 0.74, NRMSE = ±18.00 %, NMAE = ±15.00 %, NSE = 0.70) and evapotranspiration (R2 = 0.58, NRMSE = ±19.00 %, NMAE = ±16.00 %, NSE = 0.55). NSE is the Nash-Sutcliffe Efficiency. The dual-source model yielded slightly less accurate estimates of transpiration (R2 = 0.61, NRMSE = ±22.00 %, NMAE = ±20.00 %, NSE = 0.39) and evapotranspiration (R2 = 0.52, NRMSE = ±23.00 %, NMAE = ±25.00 %, NSE = 0.51). The basal crop coefficient (Kcb) showed small seasonal fluctuations, varying between 0.40 and 0.60 throughout the year. In contrast, Kc showed clear seasonality varying between 0.59–0.64 at flowering and fruit set and rising to 0.81–0.90 during the fruit growing phase. Estimates of the annual total transpiration (686 mm) from the FAO method were within 5 % of the actual measured values (677 mm) while the dual source model estimated 612 mm of transpiration, which was almost 10 % lower than the measured values. The results highlight that even though the FAO method requires fewer input parameters, it is potentially more accurate in estimating water use in mango orchards than the dual-source model.

Estimating crop evapotranspiration of wheat-maize rotation system using hybrid convolutional bidirectional Long Short-Term Memory network with grey wolf algorithm in Chinese Loess Plateau region

Accurate estimation of crop evapotranspiration (ET) is essential for the efficient utilization of agricultural water resources, crop production enhancement, and sustainable agricultural development. However, direct measurement of ET is highly expensive, intricate, and time-consuming, highlighting the imperative of establishing a novel model to accurately estimate ET in agricultural ecosystems. To address the above problems, this study proposed a novel model (GWA-CNN-BiLSTM), which incorporates Grey Wolf Algorithm (GWA), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory network (BiLSTM) as a hyperparameter adjuster, feature extractor, and regression component, respectively, to estimate ET built upon various input combinations comprising net solar radiation (Rn), vapor pressure deficit (VPD), average air temperature (Ta), soil water content (SWC), and leaf area index (LAI) about winter wheat-spring maize rotation system during 2012–2020 in the Loess Plateau. Besides, following a comparative assessment within GWA-CNN-BiLSTM, Convolutional Bidirectional Long Short-Term Memory network (CNN-BiLSTM), BiLSTM, Long Short-Term Memory network (LSTM), and Shuttleworth-Wallace (SW) models, the results revealed that GWA-CNN-BiLSTM under varied inputs obtained the superior performance, ranging from 0.562 to 0.957 in determination coefficient (R2), 8.4–41.5 % in relative root mean square error (RRMSE), 0.349 mm d−1 to 1.521 mm d−1 in mean absolute error (MAE), −3.26 % to 14.11 % in percent bias (PBIAS), and 0.820–1.091 in regression coefficient (b0), respectively. Moreover, while the accuracy of BiLSTM over LSTM was evident, its performance was notably improved by the incorporation of the CNN module. Additionally, LSTM-type models under complete input combination present better precision than SW by 29.7−51.4 % in R2, 44.2−76.1 % in RRMSE, and 33.6−63.4 % in MAE, respectively. Furthermore, the accuracy of all models under varied inputs exhibited excellence in winter wheat compared to spring maize, and corresponding improvements ranged 1.4−4.3 % in R2, 5.1−20.1 % in RRMSE, and 3.1−17.9 % in MAE, respectively. Besides, the meteorological factors (Rn, VPD, Ta) proved to be the most important inputs for ET estimation in winter wheat and spring maize. Wherein the importance of SWC exceeded that of LAI in winter wheat, while the opposite trend was observed in spring maize. In brief, GWA-CNN-BiLSTM is the highly recommended model to estimate ET of winter wheat-spring maize rotation system under diverse input data scenarios in the Loess Plateau, which can facilitate to offer valuable assistance in regional agriculture water management decisions.

Parameterization of Four Models to Estimate Crop Evapotranspiration in a Solar Greenhouse.

Working to simplify mechanistic models on the basis of reliability for estimating crop evapotranspiration (ET) in a greenhouse is still worthwhile for horticulturists. In this study, four ET models (Penman-Monteith, Priestley-Taylor, and Shuttleworth-Wallace models, and the Crop coefficient method) were parameterized after taking the restriction effect of resistance parameters in these models on ET into account, named as PA-PM, PA-PT, PA-CC, and PA-SW, respectively. The performance of these four parameterized models was compared at different growth stages, as well as the entire growing season. Tomatoes that were ET-grown in a solar greenhouse without a heating device were measured using weighting lysimeters during 2016-2017 and 2019-2021, in which data from 2016 were used to adjust the model parameters, and data from the other four study years were used to examine the model performance. The results indicated that the PA-PT and PA-CC models have a better performance in estimating tomato ET at four growth stages, while the PA-PM and PA-SW performed well only at the development and middle stages. Compared to the ET that was measured with the weighting lysimeters, the ET that was predicted using the PA-PM model was 27.0% lower at the initial stage, and 8.7% higher at the late stage; the ET that was computed using the PA-SW model was 19.5% and 13.6% higher at the initial and late stages, respectively. The PA-PT model yielded the lowest root mean square error and the highest index of agreement against the other models over the entire growing season, indicating that the PA-PT model is the best recommended model for estimating tomato ET in a solar greenhouse.

Weather-based Scheduling and Pulse Drip Irrigation Increase Growth and Production of Northern Highbush Blueberry

Northern highbush blueberry (Vaccinium corymbosum L.) often requires frequent irrigation for commercial production, but irrigation is becoming increasingly challenging for many growers because of warmer and drier weather conditions, increased water regulations, and other water-use limitations. The purpose of this study was to develop improved methods of irrigation to prepare the industry more effectively against future water uncertainties. Treatments were applied for 2 years (2021 and 2022) and included a combination of weather-based or fixed irrigation schedules using continuous or pulse irrigation in a commercial field of ‘Draper’ blueberry in eastern Washington, USA. The soil at the site was a silt loam, and irrigation was applied using two laterals of drip tubing per row. Plants on a fixed schedule were irrigated for 12 to 13 hours per application (set by the grower), whereas those on a weather-based schedule were irrigated according to daily estimates of crop evapotranspiration (downloaded from an automated weather station). In both cases, irrigation was applied every 2 to 4 days as a single, continuous application or in 30- to 50-minute pulses every 2 hours (up to nine times per day) with the same amount of water as the continuous treatment. During the first year of the study, weather-based scheduling maintained greater stem water potentials in the plants and, on average, increased yield by 3.4 t⋅ha–1, berry weight by 0.14 g/berry, berry diameter by 0.4 mm, and fruit bud set by 4.3% when compared with fixed scheduling. Likewise, pulse irrigation maintained greater stem water potentials and, on average, increased berry weight and diameter by 0.10 g and 0.4 mm, respectively, fruit bud set by 3.3%, and canopy cover by 2.4% relative to continuous irrigation. Yield and canopy cover were unaffected by any treatment in the second year, which was likely a result of uncharacteristically cool, wet weather in the spring. However, weather-based scheduling continued to maintain greater stem water potentials and, when combined with pulse irrigation, increased berry weight and diameter by 3.7 g and 1.0 mm, respectively, relative to continuous irrigation on a fixed schedule. Pulse drip irrigation also increased fruit bud set by 5.1% during the second year. These results demonstrate the potential benefits of using weather-based scheduling and pulse drip in northern highbush blueberry, especially when the plants are grown on light-textured soils in hot, dry climates.

Precision Estimation of Crop Coefficient for Maize Cultivation Using High-Resolution Satellite Imagery to Enhance Evapotranspiration Assessment in Agriculture.

The estimation of crop evapotranspiration (ETc) is crucial for irrigation water management, especially in arid regions. This can be particularly relevant in the Po Valley (Italy), where arable lands suffer from drought damages on an annual basis, causing drastic crop yield losses. This study presents a novel approach for vegetation-based estimation of crop evapotranspiration (ETc) for maize. Three years of high-resolution multispectral satellite (Sentinel-2)-based Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Red Edge Index (NDRE), and Leaf Area Index (LAI) time series data were used to derive crop coefficients of maize in nine plots at the Acqua Campus experimental farm of Irrigation Consortium for the Emilia Romagna Canal (CER), Italy. Since certain vegetation indices (VIs) (such as NDVI) have an exponential nature compared to the other indices, both linear and power regression models were evaluated to estimate the crop coefficient (Kc). In the context of linear regression, the correlations between Food and Agriculture Organization (FAO)-based Kc and NDWI, NDRE, NDVI, and LAI-based Kc were 0.833, 0.870, 0.886, and 0.771, respectively. Strong correlation values in the case of power regression (NDWI: 0.876, NDRE: 0.872, NDVI: 0.888, LAI: 0.746) indicated an alternative approach to provide crop coefficients for the vegetation period. The VI-based ETc values were calculated using reference evapotranspiration (ET0) and VI-based Kc. The weather station data of CER were used to calculate ET0 based on Penman-Monteith estimation. Out of the Vis, NDWI and NDVI-based ETc performed the best both in the cases of linear (NDWI RMSE: 0.43 ± 0.12; NDVI RMSE: 0.43 ± 0.095) and power (NDWI RMSE: 0.44 ± 0.116; NDVI RMSE: 0.44 ± 0.103) approaches. The findings affirm the efficacy of the developed methodology in accurately assessing the evapotranspiration rate. Consequently, it offers a more refined temporal estimation of water requirements for maize cultivation in the region.

Estimation of crop evapotranspiration using statistical and machine learning techniques with limited meteorological data: a case study in Udham Singh Nagar, India

Estimation of crop evapotranspiration using statistical and machine learning techniques with limited meteorological data: a case study in Udham Singh Nagar, India

Enhancing water balance assessment in urban areas through high-resolution land cover mapping: Case study of Debrecen, Hungary

Land cover (LC) mapping in urban areas is one of the core applications in remote sensing (RS), and it plays an important role in modern urban planning and management. In order to support up-to-date simplified water balance calculation, LC modelling needs to be developed for precise and high-resolution operations to monitor rapidly expanding urban areas. This study provides a spatial decision support tool for urban water balance calculations considering hydrological parameters at pixel scale. The study is aimed to create a high-resolution LC map for more precise water balance calculations to identify and link the most important parameters, such as crop coefficient, for estimating crop evapotranspiration, runoff coefficients, and infiltration rate. Meteorological data from 2016 to 2019 were involved in evapotranspiration estimation. The study site is Debrecen, a city in northeast Hungary with a population of about 200,000. By integrating Landsat 8 imagery, Google Earth (GE), and Corine Land Cover (CLC), a LC map of 30 m resolution was created with 81.2 % overall accuracy (OA) and a coefficient of kappa greater than 0.78. For the classification of Landsat 8, seven classes were assigned (forests, sealed surfaces, areas with crop cover, grassland, semi-sealed surfaces, bare ground and surface water bodies). Classification results were validated by 101 ground truth samples using other satellite images and aerial photographs. Water balance parameters (i.e. surface runoff, infiltration and evapotranspiration) were then defined and calculated at a pixel scale and for larger areas. The technique developed in this study can also be utilized in other urban areas.

Assessment of crop water requirement and irrigation scheduling of potato using weather models under sub temperate climatic condition of North-Western Himalaya

To optimize irrigation water and enhance yield, appropriate amount of irrigation water and its scheduling according to real time weather data can help in reducing crop water foot prints. The evapotranspiration (ET) based irrigation scheduling using present weather conditions reflecting changed climatic scenarios in the study regions in the past few decades has proved to be sagacious use of water. To study the ET based irrigation scheduling, field experiment conducted with the treatments comprising four ET based weather models irrigation scheduling methods viz. no irrigation or rainfed, Thornthwaite, Hargreaves method, Temperature and modified Penman Monteith under sowing windows of 20th December and 20th January were conducted at research farm, of Department of Agronomy, CSK HPKV, Palampur, Himachal Pradesh during Rabi season of 2015-16. The estimated crop evapo-transpiration (ETc) with different ET estimation methods under North Western Himalayas showed variation during crop season. The highest estimated ET was observed in December as compared to January sown potato crop with Hargreaves method (376.8 mm) followed by Temperature (354 mm), Thornthwaite (329.5 mm) and the lowest in modified Penman Monteith methods (241.9 mm). The estimated ETc from pan evaporation observed to be the highest to the tune of (382.3 mm) compared to other methods of ETc estimation. The crop coefficients modeled at different pheno-phases were used for estimating water requirement of potato crop. It was found that irrigation scheduling based on modified Penman Monteith of ET estimation saved one irrigation to the tune of 60 mm during crop period without affecting significantly growth, and tuber yield of the potato as compared to other methods, viz., However, the irrigation schedule based on Thornthwaite method has recorded highest total potato tuber yield (171.76 q ha-1) being at par with irrigation schedules worked out from other ET estimation methods. The total water footprints were the lowest in modified Penman Monteith to the tune of 300 liters per kg of potato yield compared to other methods.