Crop Coefficient Research Articles

Determination of Crop Coefficient of Okra Crop Using Lysimeter for Semi-arid Climatic Condition of Parbhani, Maharashtra

Water being a limited resource, its judicious and efficient uses for irrigation is important, especially in summer seasons to meet challenges of water scarcity. Summer okra is an important vegetable crop of India, which requires timely irrigation to achieve higher production. Information on crop coefficient (KC) of okra crop is vital for proper irrigation scheduling. A field experiment was conducted using weighing-type lysimeter at Vasantrao Naik Marathwada Krishi Vidyapeeth (VNMKV), Parbhani, in Marathwada region (semi-arid climatic condition) of Maharashtra, India, during the summer of 2022. Crop coefficient values for okra crop ranged from 0.61 to 1.41 upto 70 days after dibbling, and then declined to 0.64. The peak value of Kc was 1.41 in the 10th week after dibbling of seeds. The mean value of Kc for initial, development, mid, and late stages were 0.64, 1.07, 1.33, and 0.86, respectively. Estimated Kc values would help in better estimation of irrigation water requirement, proper planning, and utilisation of water resources in the Marathwada region of Maharashtra state.

WINDS Model Simulation of Guayule Irrigation

The WINDS (Water-Use, Irrigation, Nitrogen, Drainage, and Salinity) model uses the FAO56 dual crop coefficient and a daily time-step soil–water balance to simulate evapotranspiration and water content in the soil profile. This research calibrated the WINDS model for simulation of guayule under full irrigation. Using data from a furrow irrigated two-season guayule experiment in Arizona, this research developed segmented curves for guayule basal crop coefficient, canopy cover, crop height and root growth. The two-season guayule basal crop coefficient (Kcb) curve included first and second season development, midseason, late-season and end-season growth stages. For a fully irrigated guayule crop, the year one midseason Kcb was 1.14. The second year Kcb development phase began after the crop was semi-dormant during the first winter. The second year Kcb value was 1.23. The two-season root growth curve included a growth phase during the first season, no growth during winter, and a second growth phase during the second winter. A table allocated fractions of total transpiration to soil layers as a function of root depth. With the calibrated tables and curves, the WINDS model simulated soil moisture content with a root mean squared error (RMSE) of 1- to 3-% volumetric water content in seven soil layers compared with neutron probe water contents during the two-year growth cycle. Thus, this research developed growth curves and accurately simulated evapotranspiration and water content for a two-season guayule crop.

Irrigation Water and Nitrogen Fertilizer Management in Potato (Solanum tuberosum L.): A Review

Intensive irrigation and nutrient management practices in agriculture have given rise to serious issues in aquifer water depletion and groundwater quality. This review discusses the effects of irrigation and nitrogen management practices on potato growth, yield, and quality, and their impacts on water and nitrogen use efficiencies. This review also highlights the economics and consequences of applying deficit irrigation strategies in potato production. Many researchers have demonstrated that excessive irrigation and nitrogen application rates negatively impact potato tuber yield and quality while also increasing nitrate leaching, energy consumption, and the overall costs of production. An application of light-to-moderate deficit irrigation (10–30% of full irrigation) together with reduced nitrogen rates (60–170 kg/ha) has a great potential to improve water and nitrogen use efficiencies while obtaining optimum yield and quality in potato production, depending on the climate, variety, soil type, and water availability. There is an opportunity to reduce N application rates in potato production through deficit irrigation practices by minimizing nitrate leaching beyond the crop root zone. The best irrigation and nitrogen management techniques for potato production, as discussed in this review, include using sprinkle and drip irrigation techniques, irrigation scheduling based on local crop coefficients, soil moisture content, and crop modeling techniques, applying slow-release nitrogenous fertilizers, split nitrogen application, and applying water and nitrogenous fertilizers in accordance with crop growth stage requirements.

Estimation of Crop Coefficient and Irrigation Water Requirements of Cucumber Using the Accumulated Heat Units (Hus) in Open Field and Greenhouse Conditions

Estimation of Crop Coefficient and Irrigation Water Requirements of Cucumber Using the Accumulated Heat Units (Hus) in Open Field and Greenhouse Conditions

REMOTE SENSING AND GEOSTATISTICS APPLIED TO NDVI, KC AND PRODUCTIVE PARAMETERS IN IRRIGATED CORN CROP

Remote sensing (SR) has contributed to digital agriculture, associated with the use of geostatistical techniques that allow data acquisition, analysis and decision making. In view of this, the objective of this work was to evaluate the spatial variability of soil physical attributes, NDVI and second crop corn productivity in the territory of Santa Helena de Goiás, and to estimate values of crop coefficient (KC) through the NDVI, and to evaluate the spatiality. Soil samples were collected in a georeferenced way using a regular grid. The images were from the Sentinel 2A satellite. The Normalized Difference Vegetation Index (NDVI) was calculated, the Kc values of the crop were estimated, using two methodologies Toureiro et al., (2017) and Kamble et al. (2013), and later the values were extracted through a regular GRID, with which the geostatistical analyzes and correlations with productivity were submitted. The NDVI showed an increase up to 69 days after planting, in addition to proving the spatial variability and spatial dependence. With the geostatistics it was possible to generate a map of the spatial distribution of productivity. The NDVI allowed to obtain the Kc corresponding to the different phases of cultivation, sensitive to the methodologies used. The Kc obtained presents a high response and potential to the management of irrigated corn..

Predicting Crop Evapotranspiration under Non-Standard Conditions Using Machine Learning Algorithms, a Case Study for Vitis vinifera L. cv Tempranillo

This study focuses on assessing the accuracy of supervised machine learning regression algorithms (MLAs) in predicting actual crop evapotranspiration (ETc act) for a deficit irrigated vineyard of Vitis vinifera cv. Tempranillo, influenced by a typical Mediterranean climate. The standard approach of using the Food and Agriculture Organization (FAO) crop evapotranspiration under standard conditions (FAO-56 Kc-ET0) to estimate ETc act for irrigation purposes faces limitations in row-based, sparse, and drip irrigated crops with large, exposed soil areas, due to data requirements and potential shortcomings. One significant challenge is the accurate estimation of the basal crop coefficient (Kcb), which can be influenced by incorrect estimations of the effective transpiring leaf area and surface resistance. The research results demonstrate that the tested MLAs can accurately estimate ETc act for the vineyard with minimal errors. The Root-Mean-Square Error (RMSE) values were found to be in the range of 0.019 to 0.030 mm·h⁻¹. Additionally, the obtained MLAs reduced data requirements, which suggests their feasibility to be used to optimize sustainable irrigation management in vineyards and other row crops. The positive outcomes of the study highlight the potential advantages of employing MLAs for precise and efficient estimation of crop evapotranspiration, leading to improved water management practices in vineyards. This could promote the adoption of more sustainable and resource-efficient irrigation strategies, particularly in regions with Mediterranean climates.

Estimating cucumber crop coefficients under different greenhouse microclimatic conditions

This study aimed to determine cucumber crop coefficients under different greenhouse microclimatic conditions, parameterizing the Priestley-Taylor reference evapotranspiration model. Crop evapotranspiration was directly measured with the use of lysimeters, and crop coefficients were computed following the two-step climate FAO 56 methodology. Greenhouse compartments (i.e., cooled or uncooled) showed reference evapotranspiration differences of up to 12% in an autumn-winter crop. The results presented cucumber crop coefficient values from the initial to the late-season growth stages from 0.45 to 0.94 depending on the greenhouse climate. Based on the greenhouse hourly microclimatic variation of KC, it is recommended not to apply a KC as a constant for transpiration estimation even at greenhouses located within the same region Regression analysis relating crop coefficients with leaf area revealed very high correlation coefficients for the equations tested. The results indicated that evapotranspiration can be modeled satisfactory based on a significant relationship between crop coefficient and simple measurements of the leaf area index (i.e., KC = 0.447 × LAI).

Water requirements of banana and papaya under different water table depths and soil types in the Lower Indus Basin of Pakistan

AbstractThe water requirements of banana and papaya, the two most important fruit crops of Pakistan, are not known because farmers continue to apply water even if the crop does not need it. In this study, the evapotranspiration (ET), groundwater contribution (GW) and crop coefficient (Kc) values of banana (1995–1999) and papaya (2013–2016) were determined under different water table depths (WTDs) and soil types using drainage‐type lysimeters. The WTDs were maintained at 1.50, 2.00 and 2.50 m from the ground surface by means of Mariotte bottles. The banana ET during the first year was between 1653 and 1992 mm with a GW contribution of 11–20%, whereas during the second to fifth years, it was between 2430 and 3023 mm with a GW contribution of 10–16%. The banana yield was in the range of 58,638–79,780 kg ha−1, and the water use efficiency (WUE) was between 2.65 and 3.31 kg m−3. The papaya ET was between 1478 and 1766 mm with a GW contribution of 7–18% at 1.50–2.50 m WTDs. The papaya yield was in the range of 61,184–83,004 kg ha−1 with a WUE of 4.13–4.70 kg m−3. Therefore, GW contribution is an important component of the water balance under shallow water table conditions and should be considered when devising irrigation scheduling for banana and papaya crops.

Water and Irrigation Requirements of Glycine max (L.) Merr. in 1981–2020 in Central Poland, Central Europe

Soya, one of the most economically important crops, is sensitive to periodic water deficits, which, because of climate change, are becoming more and more common in central Europe. The goal of this study was to estimate the water requirements of soybean from 1981 to 2020 in four provinces of central Poland and the rainfall deficits affecting soybean cultivation; the study also evaluates rainfall water use efficiency for soybean cultivated in production fields to investigate the future necessity of supplemental irrigation. Calculations were based on the values of monthly air temperature and the sum of precipitation. Soybean water requirements were calculated using the method of crop coefficients and reference evapotranspiration was estimated using the Blaney–Criddle approach. Crop water requirements were defined as potential evapotranspiration. Precipitation deficits for soybean were estimated using the Ostromęcki method. Water use efficiency was calculated based on rainfall totals and soybean yield in the rainfed fields. It was found that, on average in the central Poland provinces, from 1981 to 2020, the water requirements of soybean in the growing season amounted to 384 mm, and the highest water requirements occurred in June and July. In the studied forty-year period, a significant upward trend in soybean water requirements in central Poland was observed, both from 1 May to 31 August and from 1 June to 31 August. Rainfall deficits in soybean cultivation in central Poland were found from May to August and amounted to 123 mm in normal years (N50%). The rainfall water use efficiency from April to August for soybean cultivated in central Poland on average amounted to 6.6 kg ha−1 mm−1 and varied in individual years and regions. The results of the study indicate the need to develop supplemental irrigation systems for soybean crops cultivated in central Poland and other areas of the world with similar climate conditions to optimize yield and the sustainable use of water resources.

Determination of Crop Coefficients and Evapotranspiration of Potato in a Semi-Arid Climate Using Canopy State Variables and Satellite-Based NDVI

Estimating crop coefficients and evapotranspiration (ET) accurately is crucial for optimizing irrigation. Remote sensing techniques using green canopy cover, leaf area index (LAI), and normalized difference vegetation index (NDVI) have been applied to estimate basal crop coefficients (Kcb) and ET for different crops. However, analysis of the potential of these techniques to improve water management in irrigated potato (Solanum tuberosum L.) is still lacking. This study aimed to assess the modified nonlinear relationship between LAI, Kcb and NDVI in estimating crop coefficients (Kc) and ET of potato. Moreover, Kc and ET were derived from the measured fraction of green canopy cover (FGCC) and the FAO-56 approach. ET estimated from the FAO-56, FGCC and NDVI approaches were compared with the ET simulated using the LINTUL-Potato model. The results showed that the Kc values based on FGCC and NDVI were on average 0.16 lower than values based on FAO-56 Kc during the mid-season growing stage. ET estimated from FAO-56, FGCC and NDVI compared well with ET calculated by the LINTUL-Potato model, with RMSE values of 0.83, 0.79, and 0.78 mm day−1, respectively. These results indicate that dynamic crop coefficients and potato ET can be estimated from canopy cover and NDVI. The outcomes of this study will assist potato growers in determining crop water requirements using real-time ETo, canopy state variables and NDVI data from satellite images.

Substrate moisture variations of extensive green roofs with different structural configurations in a semi-arid region: Observational data and dynamic simulation

Substrate moisture condition is crucial for hydrological performance, water management and plant survival of green roofs. To date, the behaviour of long-term substrate moisture of green roofs in various climate regions has not been well understood. In this study, 24 months continuous field monitoring of substrate moisture content within nine extensive green roof modules with varies structural configurations was conducted to investigate the impacts of structural configurations and meteorological factors on substrate moisture content, and identify the key parameters for simulating the long-term substrate moisture dynamics. Results showed that green roofs planted with SedumSpectabilehave the highest mean volumetricwater content (VWC) (0.12–0.15 m3/m3) compared to other plant species considered. Green roofs with 5-cm substrate depth had lower mean VWC (0.07–0.08 m3/m3) than other substrate depths. The average substrate moisture loss rate of green roofs in summer and autumn seasons was 1.35 and 1.22 mm/day, respectively, which was higher than the spring (0.73 mm/day) and winter (0.64 mm/day) seasons for 2021. There were significant differences in mean VWC of green roofs among substrate material types. The mean VWC of green roofs was gradually enhanced from 0.12 to 0.14 m3/m3 as the substrate depth increased from 5 cm to 15 cm. The differences of mean VWC among vegetation types were all extremely significant (p < 0.001). The relative humidity was the most significant influencing factor on the average VWC level (correlation coefficient r = 0.42), with antecedent dry weather period (ADWP) had less significant effects on substrate VWC (r = 0.33), and followed by precipitation depth (r = 0.27). However, there was no significant relationship between air temperature and average VWC level of green roofs as well as total solar radiation. Nash-Sutcliffe efficient (NSE) values of the model simulations ranged from 0.70 to 0.81, and the mean absolute relative error (MAE) values ranged from 1.25 to 2.36, which indicated the model can adequately simulate the long-term substrate moisture content dynamics of green roofs. The crop coefficient and maximum water holding capacity of the substrate were the key parameters affecting long-term moisture content simulation of green roofs. These results could provide scientific suggestions for selecting suitable structural configuration to enhance water holding capacity of extensive green roofs in arid climates.

Evapotranspiration adjustment for irrigated maize-soybean rotation systems in Nebraska, USA.

Irrigation water requirements are commonly estimated based on the estimated crop evapotranspiration (ETc) as determined by the reference evapotranspiration (ETr) and crop coefficient (Kc). Recent studies show that, at high evaporative demand (high ETr), Kc tends to decrease, creating an inverse ETr-Kc relationship. The focus of this long-term study is to, if at high atmosphere demand, there is the same inverse ETr-Kc relationship in Nebraska, USA, one of the most intensely irrigated regions in the world, and as a result, propose an adjustment to the Kc-ETr approach. The study was carried out in eastern Nebraska for maize-soybean rotation fields for the period 2002-2012. The Kc was estimated based on energy balance data from eddy covariance flux towers installed in the field and a nearby automated weather station throughout the growing seasons. We found that average Kc values varied depending on the year under high ETr; measured ETc agreed reasonably well with the FAO-56 manual predicted values, but in years with high ETr such as 2012 and 2002 affecting ETc values over the growing season. It was observed that Kc decreased as ETr increased, mainly when ETr reaches values greater than 6mm d-1 (P values < 0.001). This most likely was due to internal plant stomatal resistance to vapor release from the leaves diffusing to the atmosphere at high atmospheric demands. So, the time-based Kc curves described by FAO 56 manual should be adjusted for the analyzed crops considering different ranges of ETr to improve the required irrigation depth and irrigation management.

Strategies for water productivity enhancement in maize—A comprehensive review

AbstractMaize (Zea mays L.) is the third most important cereal crop produced globally after wheat and rice and provides the basic raw materials for starch, oil, protein, alcoholic beverages, food sweeteners and fuel. It is grown on an area of 201.98 Mha worldwide, with an average productivity of 5.75 t ha−1. Maize is mostly grown during the rainy season, but it performs better during the winter season with irrigation and resource conservation techniques. The average productivity of maize during the rainy and winter seasons is 4.3 and 6.2 t ha−1, respectively. The total production is as low as 2.7 Mt in Nepal to 362.78 Mt in the USA. Maize performs better under the furrow and drip irrigation methods, and the range of water requirements is between 425–789 and 351–685 mm, respectively. Plastic and straw mulching has enhanced irrigation water use efficiency up to 34.0–47.2 and 15.3–24.1 kg ha−1 mm−1, respectively. The crop coefficients, Kc values, for maize crops ranges between 0.2 and 1.2. Of the different methods of irrigation, the drip irrigation method enhances the maize yield by 28% and plays a great role in corn yield. This paper discusses various strategies for enhancing global maize yield.

Analyzing the role of soil and vegetation spatial variability in modelling hydrological processes for irrigation optimization at large scale

The main purpose of this paper was to study the effect of spatial variability of soil hydraulic properties (HP) and vegetation parameters (VP) (e.g., leaf-area index, LAI, and crop coefficient, Kc) on modelling agro-hydrological processes and optimising irrigation volumes at large scale. Based on this analysis, the effect of partly overlooking the spatial variability of soil HP and/or VP inputs was verified on a 140 ha irrigation sector in “Sinistra Ofanto” irrigation system in Apulia Region, Southern Italy. Five soil profiles were excavated and the HP were measured in all the soil horizons. Additionally, measurements of soil HP were taken in the surface soil layer in ninety sites distributed over the whole irrigation sector. All the HP measurements were carried out using tension infiltrometer. Remote sensing applications were used to obtain LAI and Kc using European Space Agency’s (ESA) Sentinel-2 images with 10 m resolutions. First, distributed (on ninety polygons with an average area of about 1.5 ha) optimal irrigation volumes and related deep percolation volumes at a depth of 80 cm, were computed using an agro-hydrological model and accounting for the actual observed variability of soil HP and VP inputs. The sector scale irrigation and deep percolation volumes were obtained by aggregating the distributed irrigation volumes. This was considered as the reference scenario (hereafter DVS—Detailed Variability Scenario). Then, reduced variability scenarios (hereafter RVS—Reduced Variability Scenario) were considered, where the information on the actual spatial variability of the soil HP and VP was gradually overlooked to find the minimum data set needed to still have sector scale irrigation volumes and related deep percolation volumes comparable to those obtained under the DVS. Results showed that overlooking VP (RVS-VP) variability did not significantly change the optimal irrigation volumes and the deep percolation fluxes. By contrast, neglecting the HP variability (RVS-HP) showed significant effects on both the irrigation and percolation volumes compared to the DVS. The main practical finding was that, at least for the area investigated in this study, hydraulic characterization of one soil profile in an area of approximately 30 ha provides sector scale irrigation volumes and percolation fluxes comparable to those obtained under the DVS, thus by accounting for all the observed local variability.

Determination of the Water use Efficiency of a Pecan Nut Orchard based on the Irrigation Scheduling and the Evapotranspiration Rate in Northern Mexico

Background: Adult pecan trees require a significant water supply for high yields and nut quality. Pecan nut orchards are established in semi-arid regions where water is the primary limiting resource for agriculture. Therefore, in these regions, improving water use efficiency is essential. Objective: Evaluate the water use efficiency of a pecan tree orchard based on the comparison of irrigation scheduling with the evapotranspiration rate data. Methods: The study was conducted in a pecan nut (Carya illinoinensis) orchard of 7.2 ha with 7-year-old Western and Wichita cv tress. The volume of water applied to each tree (drip irrigation) was converted to a daily irrigation depth and compared against the daily rate of actual (measured with an Eddy covariance system) and the FAO Penman-Monteith evapotranspiration. Results: Comparing the monthly water depth applied to each tree against the monthly FAO-Penman Monteith ET, surplus or deficit irrigation episodes were detected. Because the canopy trees only covered 18.7% of the orchard area, the daily rate of actual evapotranspiration during the months of the trees growing was very small (1 to 2 mm) compared with the orchards of mature pecan trees. The corresponding monthly crop coefficients (Kc) were also small (0.315 on average). Conclusion: This study showed that by comparing the rate of ETref against the irrigation depth applied to the trees in the irrigation scheduling, it is possible to reveal cases of surplus or deficit of water supplied to a pecan nut orchard.

Exploring interpretable and non-interpretable machine learning models for estimating winter wheat evapotranspiration using particle swarm optimization with limited climatic data

Accurate estimation of crop evapotranspiration (ETc) is crucial for improving the water use efficiency and designing and operating irrigation systems. To accurately calculate winter wheat ETc with limited meteorological data, the present study proposed two interpretable machine learning (ML) models (random forest (RF) and extreme gradient boosting (XGBoost)) as well as non-interpretable ML models (support vector machine (SVM) and deep neural network (DNN)) based on the particle swarm optimization (PSO) algorithm using observed winter wheat ETc data during the period from 2007 to 2013 at Luan Cheng Agro-ecosystem Experimental Station. Mean absolute error (MAE), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), coefficient of determination (R2), and global performance indicator (GPI) were used to assess the performance of models. This demonstrated that the ML models based on the crop coefficient (Kc) and solar radiation (Rn) were accurate and offered a workaround for calculating winter wheat ETc in the absence of meteorological data. In four ML models, the ninth input combination, consisting of Kc, Rn, daily air maximum temperature (Tmax), daily air minimum temperature (Tmin), sunshine hours (n), and wind speed with a height of 2 m (U2), produced the best estimate of ETc. Among them, the PSO-based SVM (PSO-SVM) model obtained the best results for estimating ETc with MAE, RMSE, NSE, R2, and GPI values of 0.389 mm·d−1, 0.562 mm·d−1 0.910, 0.911, and 0.975, respectively, showing the advantages of the non-interpretable ML model in ETc forecasting. Accurate descriptions of actual hydrological and climatic processes were given by local interpretable model-agnostic explanations (LIME). The inflection points of daily climatic parameters (Tmin, Tmax, Rn, n) related to ETc were determined to be 3.80 °C, 5.50 °C, 1.62 MJ·m−2·d−1, 1.37 h, respectively. This work has potential to overcome the difficulty of measuring winter wheat ETc properly due to the lack of meteorological data and accomplish appropriate water management to conserve water and increase water productivity.

Water requirement and crop coefficient of sugarcane cultivated in Nicaragua

Water requirement and crop coefficient of sugarcane cultivated in Nicaragua

A new approach to estimate daily evapotranspiration, based on Landsat data and FAO56 principles

ABSTRACT The accurate estimation of evapotranspiration is crucial for enhancing crop water productivity and effectively managing water resources. This research offers a novel method that integrates satellite data and crop coefficients to calculate ET c and ET a on a daily basis and overcome the limitation of low temporal frequency of non-commercial satellite data. The study was carried out in the southern part of Khuzestan Province, Iran, on sugarcane crops in the Amirkabir Agro-industries area. The method involves obtaining Landsat-8 data with an 8-day temporal resolution, which was used to estimate Land Surface Temperature (LST) using a Single-Channel Algorithm. The estimated LST was then validated with in-situ canopy temperature measurements and used to predict the crop stress coefficient (K s) based on its relationship with the crop water stress index (CWSI). The crop coefficient (K c) was obtained using the Surface Energy Balance Algorithm for Land (SEBAL) algorithm, and both Ks and Kc were utilized to calculate daily ETa by multiplying by the daily reference evapotranspiration (ET 0) obtained from local meteorological data. The results indicated that the crop coefficients of sugarcane in the initial and mid-stages were 12% and 18% higher, respectively, compared to the FAO56 guideline. The aggregated decadal and monthly ET a showed good agreement with the WaPOR datasets, with an RMSE of 8.7 and 1.93 mm, respectively. This approach offers a potential solution to the challenge of obtaining remote sensing data with a higher temporal frequency.

A remote sensing approach to estimate variable crop coefficient and evapotranspiration for improved water productivity in the Ethiopian highlands

Proper and reasonable estimation of actual evapotranspiration is critical for the design, operation, and management of irrigation systems. However, the density of climatic stations is low in many parts of Ethiopia to estimate spatially reasonable reference evapotranspiration (ETo), and the lack of spatial variability of crop coefficient (Kc) is clear barrier to the proper management of irrigation water. Therefore, the main objective of this study was to estimate reasonable crop evapotranspiration (ETc) by deriving spatially and temporally varying crop coefficients using remote sensing products in 10 wheat plots at the Koga irrigation scheme. The moderate-resolution imaging spectroradiometer (MODIS) potential evapotranspiration was calibrated at two class-I climate stations (Bahir Dar and Dangila) based on Penman–Monteith estimates and Normalized Difference Vegetation Index (NDVI) was derived from Sentinel 2B, which was later used to derive Kc. The correlation between reference evapotranspiration from MODIS Penman Monteith was acceptable for both Dangila (R2 = 0.64) and Bahir Dar (R2 = 0.74) stations. The MODIS calibration constant for Koga irrigation schemes was 0.27 and 1.99 (regression slope and constant) on an 8-day basis. Similarly, a strong correlation (R2 = 0.95) was found between Sentinel-based NDVI and FAO crop coefficient, which indicated an alternative pathway for estimating crop coefficient. The value of Kc varies in space (across the 10 plots) from 0.16 to 0.42 at the initial stage and from 1.20 to 1.32 at the mid-stage. Similarly, the mean value of Kc varies in time from 0.29 at the initial stage to 1.26 at the mid-stage. On the other hand, evaluation of MODIS and WaPOR ETc found a significant difference (p < 0.05) with the calibrated MODIS-Sentinel 2B derived ETc. This indicated the need to calibrate both MODIS and WaPOR for the proper estimation of crop water needs. Underestimation of ET was observed from MODIS, and the reverse is true for WaPOR. Our findings showed that calibrating and integrating MODIS with Sentinel 2B would be a feasible approach to estimate Kc and hence ETc that varies in time and space. This would assist water managers in estimating crop water needs for better productivity in the region.

Evaluating the Impact of Future Seasonal Climate Extremes on Crop Evapotranspiration of Maize in Western Kansas Using a Machine Learning Approach

Data-driven technologies are employed in agriculture to optimize the use of limited resources. Crop evapotranspiration (ET) estimates the actual amount of water that crops require at different growth stages, thereby proving to be the essential information needed for precision irrigation. Crop ET is essential in areas like the US High Plains, where farmers rely on groundwater for irrigation. The sustainability of irrigated agriculture in the region is threatened by diminishing groundwater levels, and the increasing frequency of extreme events caused by climate change further exacerbates the situation. These conditions can significantly affect crop ET rates, leading to water stress, which adversely affects crop yields. In this study, we analyze historical climate data using a machine learning model to determine which of the climate extreme indices most influences crop ET. Crop ET is estimated using reference ET derived from the FAO Penman–Monteith equation, which is multiplied with the crop coefficient data estimated from the remotely sensed normalized difference vegetation index (NDVI). We found that the climate extreme indices of consecutive dry days and the mean weekly maximum temperatures most influenced crop ET. It was found that temperature-derived indices influenced crop ET more than precipitation-derived indices. Under the future climate scenarios, we predict that crop ET will increase by 0.4% and 1.7% in the near term, by 3.1% and 5.9% in the middle term, and by 3.8% and 9.6% at the end of the century under low greenhouse gas emission and high greenhouse gas emission scenarios, respectively. These predicted changes in seasonal crop ET can help agricultural producers to make well-informed decisions to optimize groundwater resources.