Crop Coefficient Research Articles (Page 1)

A machine learning framework to estimate crop coefficient dynamics of citrus orchards

In irrigation and agricultural planning, the crop coefficient value plays an important role in calculating water planning on land. This study aims to calculate the crop coefficient (Kc) of rice in a Nonpowered Automatic Fertigation (FONi) irrigation system combined with a minapadi system as a reference for irrigation planning. This study was conducted experimentally for 99 days in Dramaga, Bogor, using the FONi Minapadi system consisting of a fiberglass tank, a water supply tank, and an automatic float to maintain the water level. Actual evapotranspiration (ETa) data were calculated based on water balance, while potential evapotranspiration (ETo) was modeled using five methods: Penman-Monteith, Turc, Hargreaves, Makkink, and Blaney-Criddle. Model validation was performed using linear regression against the Penman-Monteith method as the standard reference. The results show that the total ETa during the observation period was 421.93 mm. Among the ETo calculation methods, the Turc model provided results closest to the Penman-Monteith method, with a coefficient of determination (R²) of 0.741 and the lowest sum of squares error (SSE) of 56.026. The calculated Kc values varied throughout the rice growth phase, with the highest value of 1.84 observed during the reproductive phase. The relatively high Kc value reflects significant water demand in the FONi Minapadi system, influenced by system characteristics and environmental conditions. This study concludes that the FONi Minapadi system has the potential to improve irrigation management efficiency in integrated agriculture. However, further research is needed to understand the influence of technical and environmental factors on the Kc value and to compare it with other irrigation systems.

Introduction. Computational Fluid Dynamics (CFD) is a technique for simulating the behavior of thermodynamic parameters. Objective. Evaluate the thermodynamics of a greenhouse using CFD, in order to propose improvements in lettuce production. Materials and Methods. The research was conducted between October 2022 and February 2023 at the Los Diamantes Agricultural Innovation Center, Limón, Costa Rica. The yield of three lettuce cycles was collected. A 3D mechanical model of the greenhouse was developed. A mesh of 482,664 elements was generated with refinement in the interior. The analysis was performed under steady-state flow, using the Navier-Stokes equation with the k-ε turbulence model and species transport with thermal interactions using the energy equation. Fluid materials (air and air-vapor mixture), solids (soil and polyethylene), and the crop as a porous medium were modeled. Evapotranspiration was estimated using meteorological data and crop coefficients. Boundary conditions included variable velocity input, constant temperature walls, and porous surfaces calibrated with bibliographic data. The model was validated using MAE and RMSE with errors less than 10%, and passive and structural improvements were proposed to optimize the internal microclimate of the greenhouse. Results. During the day, the average temperature and relative humidity in the greenhouse exceeded 30 °C and 65%, respectively, while during the night they decreased to 18 °C and close to 90 %. The temperature showed significant variations in the vertical axis, but remained more homogeneous longitudinally, while the relative humidity showed greater variability in both directions. Conclusion. The modeling allowed visualization of the greenhouse behavior, and it was proposed to increase the dimensions of the zenith window from 11 m² to 20 m²; the installation of two air recirculators and a mobile shade with 50% light transmissibility.

Increasing water scarcity and climate variability have intensified the need for precise agricultural irrigation management. Accurate estimation of crop coefficients (Kc) is critical for determining crop water requirements, especially in arid and semi-arid regions. However, conventional methods for estimating Kc often rely on generalized plant characteristics, which may not account for local climatic variations. In this study, we address this challenge by predicting the daily crop coefficient for soybean using four machine learning models: Extreme Gradient Boosting (XGBoost), Extra Tree (ET), Random Forest (RF), and CatBoost. These models were trained on meteorological data from Suhaj Governorate, Egypt, spanning 1979–2014. Additionally, SHapley Additive exPlanations (SHAP), Sobol sensitivity analysis, and Local Interpretable Model-agnostic Explanations (LIME) were applied to evaluate model interpretability and consistency with physical processes. Among the models evaluated, the ET model achieved the highest accuracy, with r = 0.96, NSE = 0.93, RMSE = 0.05, and MAE = 0.02. XGBoost and RF also performed well, each obtaining r = 0.96, NSE = 0.92, RMSE = 0.06, and MAE = 0.02. In comparison, CatBoost demonstrated slightly lower accuracy, with r = 0.95, NSE = 0.91, RMSE = 0.06, and MAE = 0.02. SHAP and Sobol analyses consistently identified the antecedent crop coefficient [:Kc(d-1)] and solar radiation (Sin) as the most influential variables. LIME results revealed localized variations in predictions, reflecting dynamic crop-climate interactions. This study underscores the importance of integrating interpretable machine learning models to enhance both predictive accuracy and reliability while maintaining alignment with critical physical processes. The proposed framework offers a robust tool for improving daily Kc estimation, thereby supporting more sustainable irrigation practices and climate-resilient agriculture.

Basil (Ocimum basilicum L.) is a high-value aromatic crop with growing global demand, and optimizing its yield under tropical conditions is critical for sustainable agriculture. This study aimed to (1) quantify basil crop coefficient (Kc) and evapotranspiration (ETc) via lysimeters and (2) characterize soil physical–chemical variability across three Tolima (Colombia) region sites: Mariquita, Honda, and El Espinal. Crop evapotranspiration, measured via lysimeters, peaked at 7.41 mm day-1 during maturity, with a total crop water requirement of 228.82 mm. Crop coefficients varied dynamically by stage, with values of 0.75, 0.98, and 0.76 during establishment, peak growth, and senescence, respectively. Historical climate analysis revealed a bimodal rainfall distribution modulated by ENSO phenomenon, with El Niño-La Niña phases, with significant impacts on crop water availability. Soil analyses showed that Mariquita soils are higher in total porosity Tp (47.80%), organic matter (2.42 g 100g-1), field capacity FC (31.62%), and available water (3.59%), whereas El Espinal showed higher bulk density (1.65 gr cm-3) and permanent wilting point PWP (21.99%), constraining water availability. Honda soils presented intermediate conditions but were notable for higher cation exchange capacity CEC (9.55 cmol kg-1) and moderate organic matter content (1.56 g 100g-1), supporting balanced nutrient retention. Cultivated plots across sites showed increased phosphorus and copper relative to adjacent natural areas, reflecting fertilization practices. These results highlight the need for precision irrigation scheduling and site-specific soil management to maximize water productivity and yield stability. Our findings provide a baseline for adapting basil production systems to climatic variability in tropical dry regions.

ABSTRACT Ecological drought (ED) poses significant challenges to terrestrial ecosystems under environmental change. However, most existing remote sensing indices are merely descriptive, lacking a comprehensive ED assessment that integrates vegetation conditions, evapotranspiration, and water deficit. Therefore, the study developed a ‘Vegetation-Evapotranspiration-Water Balance’ framework that captures the complete process in water supply and demand balance and reflects the combined effects of meteorological conditions, soil moisture, and vegetation physiological status, revealing ED's physio-ecological mechanisms. The study applied this framework to assess ED spatiotemporal patterns across the Yellow River Basin (YRB) from 1982 to 2020 using an eight-subregion division. Key findings include: (1) normalized difference vegetation index showed an abrupt circa around 2003, with 95.3% area of yrb exhibiting increases. (2) Crop coefficients increased in northern subregions but decreased in southern, reflecting divergent ecological responses; (3) ecological water metrics exhibited strong spatial gradients, with ecological water requirement (EWR) decreasing southward and ecological water consumption (EWC) and ecological water deficit (EWD) increasing from north to south; (4) despite rising EWR and EWC, EWD decreased in 54.6% area of YRB. Spatially, terrestrial ED was slightly alleviated in the upper reaches (loop irrigation area), remained stable in the middle Reaches (Loess Plateau core), and was significantly alleviated across the downstream.

Estimation of actual evapotranspiration and stage-wise crop coefficients for transplanted rice using a modified non-weighing paddy lysimeter and their prediction on machine ensemble approach in the middle Indo-Gangetic plains of South Asia

The escalating pressure on water resources in agricultural regions has become a catalyst for water conflicts. The adoption of innovative approaches to estimate actual evapotranspiration (ETa) offers potential solutions to mitigate conflicts related to water usage. This research presents the application of a remote sensing-based methodology for estimating actual evapotranspiration (ETa) based on a two-source energy balance model (TSEB) for riparian vegetation in Nebraska, US using the Spatial EvapoTranspiration Modeling Interface (SETMI). Estimated results through SETMI and field data using the eddy covariance system (EC) considering the period 2008–2013 were used to validate the energy balance components and ETa. Modeled energy balance components showed a strong correlation to the ground data from EC, with ET presenting R2 equal to 0.96 and RMSE of 0.73 mm.d−1. In 2012, the lowest adjusted crop coefficient (Kcadj) values were observed across all land covers, with a mean value of 0.49. The years 2013 and 2012, due to the dry conditions, recorded the highest accumulated ETa values (706 mm and 664 mm, respectively). Soybeans and corn exhibited the highest ETa values, recording 699 mm and 773 mm, respectively. Corn and soybeans, together accounting for a substantial portion of the land cover at 15% and 3%, respectively, play a significant role. Given that most fields cultivating these crops are irrigated, both pumped groundwater and surface water directly impact the water source of the Republican River. The SETMI model has generated appropriate estimated daily ETa values, thereby affirming the model’s utility as a tool for assisting water management and decision-makers in riparian zones.

Accurate assessment of a crop’s water requirement is essential for optimising irrigation scheduling and increasing the sustainability of water use. The crop coefficient (Kc) is a dimensionless factor that converts reference evapotranspiration (ET0) into actual crop evapotranspiration (ETc) and is widely used for irrigation scheduling. The Kc reflects canopy cover, phenology, and crop type/variety, but is difficult to measure directly in heterogeneous perennial systems, such as vineyards. Remote sensing (RS) products, especially open-source satellite imagery, offer a cost-effective solution at moderate spatial and temporal scales, although their application in vineyards has been relatively limited due to the large pixel size (~100 m2) relative to vine canopy size (~2 m2). This study aimed to improve grapevine Kc predictions using vegetation indices derived from harmonised Sentinel-2 imagery in combination with spectral unmixing, with ground data obtained from canopy light interception measurements in three winegrape cultivars (Shiraz, Cabernet Sauvignon, and Chardonnay) in the Barossa and Eden Valleys, South Australia. A linear spectral mixture analysis approach was taken, which required estimation of vine canopy cover through beta regression models to improve the accuracy of vegetation indices that were used to build the Kc prediction models. Unmixing improved the prediction of seasonal Kc values in Shiraz (R2 of 0.625, RMSE = 0.078, MAE = 0.063), Cabernet Sauvignon (R2 = 0.686, RMSE = 0.072, MAE = 0.055) and Chardonnay (R2 = 0.814, RMSE = 0.075, MAE = 0.059) compared to unmixed pixels. Furthermore, unmixing improved predictions during the early and late canopy growth stages when pixel variability was greater. Our findings demonstrate that integrating open-source satellite data with machine learning models and spectral unmixing can accurately reproduce the temporal dynamics of Kc values in vineyards. This approach was also shown to be transferable across cultivars and regions, providing a practical tool for crop monitoring and irrigation management in support of sustainable viticulture.

Accurately estimating reference crop evapotranspiration (ET₀) is essential for assessing crop water requirements and optimizing regional water resource management. Traditional ET₀ estimation models are limited by incomplete meteorological data, while sun-induced chlorophyll fluorescence (SIF) provides new opportunities for ET₀ estimation. However, existing models neglect the influence of environmental variables on the relationship between ET0 and SIF, resulting in an inability to accurately capture the dynamic variations of ET0. To overcome this limitation, we incorporated the basal crop coefficient (Kcb) into the original ET0_SIF model to enhance its constraints, developing a hybrid SIF-based model (RET0_SIF). By integrating this model with satellite observations and reanalysis data, we produced high-resolution spatiotemporal ET0 estimates (RET0_SIFd). The research findings demonstrate that: (1) the improved RET0_SIF model significantly enhances ET0 estimation accuracy, effectively capturing seasonal ET0 variations across 22 monitoring stations; (2) RET0_SIF outperforms conventional empirical models, exhibiting a minimal multi-year mean bias (0.59 mm/8-day) compared to the Penman-Monteith (ET0PM) at all 22 stations; (3) RET0_SIFd reveals a spatial pattern of gradual decrease from west to east in the study area, along with an increasing trend over time (2.56 mm/year). This study provides a novel methodology for precise ET0 estimation in arid and semi-arid regions.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-17716-5.

Severe water scarcity has become a critical constraint to global agricultural development. Enhancing both the timeliness and accuracy of crop evapotranspiration (ETc) retrieval is essential for optimizing irrigation scheduling. Addressing the limitations of conventional ground-based point-source measurements in rapidly acquiring two-dimensional ETc information at the field scale, this study employed unmanned aerial vehicle (UAV) remote sensing equipped with multispectral and thermal infrared sensors to obtain high spatiotemporal resolution imagery of a representative citrus orchard (Citrus reticulata Blanco cv. ‘Yichangmiju’) in western Hubei at different phenological stages. In conjunction with meteorological data (air temperature, daily net radiation, etc.), ETc was retrieved using two established approaches: the Seguin-Itier (S-I) model, which relates canopy–air temperature differences to ETc, and the multispectral-driven single crop coefficient method, which estimates ETc by combining vegetation indices with reference evapotranspiration. The thermal-infrared-driven S-I model, which relates canopy–air temperature differences to ETc, and the multispectral-driven single crop coefficient method, which estimates ETc by combining vegetation indices with reference evapotranspiration. The findings indicate that: (1) both the S-I model and the single crop coefficient method achieved satisfactory ETc estimation accuracy, with the latter performing slightly better (accuracy of 80% and 85%, respectively); (2) the proposed multi-source fusion model consistently demonstrated high accuracy and stability across all phenological stages (R2 = 0.9104, 0.9851, and 0.9313 for the fruit-setting, fruit-enlargement, and coloration–sugar-accumulation stages, respectively; all significant at p < 0.01), significantly enhancing the precision and timeliness of ETc retrieval; and (3) the model was successfully applied to ETc retrieval during the main growth stages in the Cangwubang citrus-producing area of Yichang, providing practical support for irrigation scheduling and water resource management at the regional scale. This multi-source fusion approach offers effective technical support for precision irrigation control in agriculture and holds broad application prospects.

Water scarcity, soil salinization, and desertification threaten sustainable agricultural ecosystems of Hetao irrigation district, Yellow River Basin (YRB). Precise quantification of soil water dynamics and plant water consumption processes is essential for the agricultural sustainability of the irrigation district. Therefore, this study mainly focused on the crop coefficients and water consumption processes of three representative plant types in the Hetao irrigation district, each corresponding to a specific land system: Helianthus annuus (cultivated land), Tamarix chinensis (wasteland), and Phragmites australis (lake). The SIMDualKc model was calibrated and validated based on situ observation data (soil water content and yield) during 2018 (conventional conditions), 2023 and 2024 (deep water-saving conditions). Results show strong agreement between simulated and observed soil moisture and crop yields. The results indicate that the process curves of Kcb (basal crop coefficient) and Kcbadj (adjusted crop coefficient) nearly overlapped for the three plant types in 2018 and 2023. However, under the deep water-saving project implemented in 2024, the Kcbadj process curves for all three plant types exhibited a significant reduction (approximately 15%). Soil evaporation fractions (E/ETcadj) were stable at 19–30% during the 2018, 2023, and 2024. The contribution of capillary rise to ET reached 38.61–43.18% in cultivated land (Helianthus annuus), 41.52–48.93% in wasteland (Tamarix chinensis), and 38.08–46.57% in lake boundary areas (Phragmites australis), which underscores the significant role of groundwater recharge in sustaining plant water consumption. Actual-to-potential transpiration ratios (Ta/Tp) during 2023–2024 decreased by 3–11% for Helianthus annuus, 5–12% for Tamarix chinensis, and 23% for Phragmites australis compared to Ta/Tp values in 2018. Capillary rise decreased approximately 10% during the whole system. Deep water-saving practices increased the groundwater depth and restricted groundwater recharge to plants via capillary rise, thereby impairing plant transpiration and growth. These findings provide scientific support for sustainable agriculture and ecological security in the Yellow River Basin.

Assessing evapotranspiration and single crop coefficients of common crops by eddy covariance measurement at multi-sites

AimsThe South African National Water Act (No. 36 of 1998) mandates the regulation of land-based activities that reduce streamflow by declaring them streamflow reduction activities (SFRAs). Hemp (Cannabis sativa L.) is commonly known as a water-intensive crop, yet no published journal articles providing measurements of its evapotranspiration (ET) or crop factor (Kc) exist in South Africa, and there is limited information on hemp ET and Kc internationally. Therefore, its impact on streamflow reduction cannot be assessed. In the context of this research, the term water use was used synonymously with ET, and refers to the combined soil evaporation and transpiration from the Cannabis sativa L. crop (and when present, weeds or grasses in the interrow), which is the overall water use associated with growing the crop.MethodsThis study provides ET data to determine if irrigated hemp should be investigated further as a potential SFRA by determining its ET and water productivity. An eddy covariance (EC) system was utilised in a hemp field trial. Standard microclimatic variables, volumetric soil water content, plant height, and Leaf Area Index (LAI) were measured.ResultsTotal ET from the hemp crop over the measurement period (7 December 2022 to 15 April 2023) was 377 mm. The average daily ET was 28.4 L/tree, or 2.94 mm/plant irrigation depth. The crop coefficient varied between 0.73 and 0.77, and the water productivity was 0.96 kg of fresh bud per m− 3 of water. Hemp had a high water use and low water productivity compared to international hemp studies due to a low planting density (2000 plants/ha).ConclusionsThese results provide the first field-based measurements of water use and crop coefficient estimates of hemp in South Africa and contribute to the very limited data available internationally. In South Africa they will be critical to assess the streamflow reduction activity of hemp.

The aim of this study was to investigate the method of estimating actual crop evapotranspiration (ETcact) in a greenhouse using other measured meteorological parameters when solar radiation (Rs) data are missing. The study estimated ETcact of greenhouse green peppers by combining solar radiation estimation models with the Penman–Monteith (PM) model and evaluated model performance. The results showed that the prediction accuracy of the temperature-based solar radiation model was higher than the model based on sunshine hours in the Hexi Corridor region. The effect of the insulation cover on the incident solar radiation in the greenhouse is modeled by introducing a ramp function. In terms of crop coefficients (Kcb), the initial Kcb value of green peppers in the 2023 growing season was generally consistent with the updated FAO-56 standard values, whereas the initial Kcb values (0.17) were higher than the standard values in the 2023–2024 growing season. During the two growing seasons, the mid-stage Kcb values were 1.01 in the 2023 growing season and 0.82 in the 2023–2024 growing season. The study also found that PM–RT4, PM–RT5, and PM–RT6 models were all able to accurately predict the ETcact of greenhouse green peppers during the 2023 growing season. The PM–RT4 model performed well in both growing seasons, with R2 = 0.8101 in the 2023 growing season and R2 = 0.7561 in the 2023–2024 growing season. Our research supports the PM–RT4 model as appropriate to estimate green pepper actual evapotranspiration in Gobi solar greenhouses (GSGs) and may be further used to improve irrigation scheduling for green peppers grown in GSGs.

Small reservoirs are structures that function to accommodate excess water during the rainy season so that it can be used during the dry season. Dungdo Reservoir is expected to help meet the needs of irrigation water and raw water for livestock in the surrounding community. Water distribution simulation aims to optimize water availability efficiently and evenly. The methodology used includes water balance analysis based on rainfall data, evapotranspiration, inflow, and changes in reservoir capacity. Irrigation water requirements are calculated based on the crop coefficient (Kc), while raw water requirements are calculated based on the number of livestock. Based on the simulation results with the existing planting pattern with an irrigation area of 171.60 Ha, it shows that the average water requirement is 279605.66 m3/15 days, while the reservoir's capacity to provide water is 53135.20 m3/15 days. The simulation results show that Dungdo Reservoir has not been able to optimally meet irrigation water and raw water needs.

This study, conducted in Southern Italy in 2023, investigated the effects of a dynamic agrivoltaics (AV) system on microclimate, water consumption, plant growth, and essential oil yield in six medicinal species: lavender (Lavandula angustifolia L. 'Royal purple'), lemmon thyme (Thymus citriodorus (Pers.) Schreb. ar. 'Aureus'), common thyme (Thymus vulgaris L.), rosemary (Salvia rosmarinus Spenn. 'Severn seas'), mint (Mentha spicata L. 'Moroccan'), and sage (Salvia officinalis L. subsp. Officinalis). Due to the rotating solar panels, two distinct ground zones were identified: a consistently shaded area under the panels (UP), and a partially shaded area between the panels (BP). These were compared to an adjacent full-sun control area (T). Microclimate parameters, including solar radiation, air and leaf infrared temperature, and soil temperature, were recorded throughout the cultivation season. Reference evapotranspiration (ETO) was calculated using Turc's method, and crop evapotranspiration (ETC) was estimated with species-specific crop coefficients (KC). Results showed significantly lower microclimatic values in the UP plot compared to both BP and especially T, resulting in ETC reductions of 81.1% in UP and 13.1% in BP relative to T, an advantage in water-scarce environments. Growth and yield responses varied among species and treatment plots. Except for mint, all species showed a significant reduction in fresh biomass (40.1% to 48.8%) under the high shading of UP compared to T. However, no biomass reductions were observed in BP. Notably, essential oil yields were higher in both UP and BP plots (0.60-2.63%) compared to the T plot (0.51-1.90%). These findings demonstrate that dynamic AV systems can enhance water use efficiency and essential oil yield, offering promising opportunities for sustainable, high-quality medicinal crop production in arid and semi-arid regions.

A state-of-the-art novel approach to predict potato crop coefficient (Kc) by integrating advanced machine learning tools

Toward Estimating the Crop Coefficient of Vineyards Using a Smartphone Camera

Climate warming poses significant challenges for the sustainable management of natural water resources, making efficient planning and usage essential. This study evaluates the water requirements, irrigation demand, and rainfall deficits for two key vegetable crops, carrot and white cabbage, under projected climate scenarios RCP 4.5 and RCP 8.5 for the period 2031–2100. The analysis was conducted for Kraków and Rzeszów Counties in southern Poland using projected monthly temperature and precipitation data from the Klimada 2.0 portal. Potential evapotranspiration (ETp) during the growing season (May–October) was estimated using Treder’s empirical model and the crop coefficient method adapted for Polish conditions. The reference period for comparison was 1951–2020. The results reveal a significant upward trend in water demand for both crops, with the highest increases under the RCP 8.5 scenario–seasonal ETp values reaching up to 517 mm for cabbage and 497 mm for carrot. Rainfall deficits are projected to intensify, especially during July and August, with greater shortages in Rzeszów County compared to Kraków County. Irrigation demand varies depending on soil type and drought severity, becoming critical in medium and very dry years. These findings underscore the necessity of adapting irrigation strategies and water resource management to ensure sustainable vegetable production under changing climate conditions. The data provide valuable guidance for farmers, advisors, and policymakers in planning effective irrigation infrastructure and optimizing water-use efficiency in southern Poland.