Crop Coefficient Approach Research Articles

Remote Sensing-Assisted Estimation of Water Use in Apple Orchards with Permanent Living Mulch

Orchards are complex agricultural systems with various characteristics that influence crop evapotranspiration (ETc), such as variety, tree height, planting density, irrigation methods, and inter-row management. The preservation of biodiversity and improvement of soil fertility have become important goals in modern orchard management. Consequently, the traditional approach to weed control between rows, which relies on herbicides and soil mobilization, has gradually been replaced by the use of permanent living mulch (LM). This study explored the potential of a remote sensing (RS)-assisted method to monitor water use and water productivity in apple orchards with permanent mulch. The experimental data were obtained in the Lis Valley Irrigation District, on the Central Coast of Portugal, where the “Maçã de Alcobaça” (Alcobaça apple) is produced. The methodology was applied over three growing seasons (2019–2021), combining ground observations with RS tools, including drone flights and satellite images. The estimation of ETa followed a modified version of the Food and Agriculture Organization of the United Nations (FAO) single crop coefficient approach, in which the crop coefficient (Kc) was derived from the normalized difference vegetation index (NDVI) calculated from satellite images and incorporated into a daily soil water balance. The average seasonal ETa (FAO-56) was 824 ± 14 mm, and the water productivity (WP) was 3.99 ± 0.7 kg m−3. Good correlations were found between the Kc’s proposed by FAO and the NDVI evolution in the experimental plot, with an R2 of 0.75 for the entire growing season. The results from the derived RS-assisted method were compared to the ETa values obtained from the Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) surface energy balance model, showing a root mean square (RMSE) of ±0.3 mm day−1 and a low bias of 0.6 mm day−1. This study provided insights into mulch management, including cutting intensity, and its role in maintaining the health of the main crop. RS data can be used in this management to adjust cutting schedules, determine Kc, and monitor canopy management practices such as pruning, health monitoring, and irrigation warnings.

Projecting Irrigation Water and Crop Water Requirements for Paddies Using WEAP-MABIA under Climate Change

Monitoring future irrigation water demand as a part of agricultural interventions is crucial to ensure food security. In this study, the impact of climate change on paddy cultivation in Brunei is investigated, focusing on the Wasan rice scheme. This research aims to project irrigation water requirement (IWR) and crop water requirement (CWR) or the main and off season using the WEAP-MABIA model. Historical data analysis over the past 30 years and future projections up to 2100 are employed to assess potential impacts. An ensemble of statistically downscaled climate models, based on seven CMIP6 GCMs under shared socioeconomic pathways (SSPs) (SSP245, SSP370, and SSP585), was utilised to project the IWR and CWR. Using downscaled CMIP6 data, three future periods were bias-corrected using quantile delta mapping (QDM) for 2020–2046 (near future), 2047–2073 (mid future), and 2074–2100 (far future). The WEAP-MABIA model utilises a dual crop coefficient approach to evaluate crop evapotranspiration (ETc), a critical factor in computing IWR. Results indicate that changes in future temperatures will lead to higher average ETc. Consequently, this results in elevated demands in irrigation water during the off season, and it is especially prominent in high-emission scenarios (SSP370 and SSP585). While the main season experiences a relatively stable or slightly increasing IWR trend, the off season consistently shows a decreasing trend in IWR. Moreover, the off season benefits from substantial rainfall increases, effectively reducing IWR despite the rise in both maximum and minimum temperatures. This study also highlights some recommendations for implementing possible improvements in irrigation management to address the effects of climate change on rice cultivation in the region. Future investigation should focus on enhancing crop yield predictions under climate change by integrating a dynamic crop growth model that adjusts for changing crop coefficient (Kc) values.

Water productivity in Vitis vinifera L. cv. Alvarinho using dual crop coefficient approach

Water productivity (WP) measurement determines the efficiency of water use by assessing the ratio of the crop yield to the amount of water used in production. The objective of this study was to identify the optimal irrigation treatment for Vitis vinifera L. cv. Alvarinho with ground cover in Northern Portugal, with a focus on water productivity. Two irrigation treatments (full irrigation—FI; deficit irrigation—DI) and a control (rainfed—R) were considered. The FI strategy represented the standard irrigation carried out by the vinegrower, based on the water availability and their experience. The cover crop was a variable factor, evaluated in terms of both height and density, both within the crop row and between the rows. In each of the treatments, the available soil water content (ASW) was measured in eight locations in the field throughout the growing season using a capacitive probe (Diviner 2000) previously calibrated. These measurements were used to calibrate the SIMDualKc model, which employed the dual crop coefficient approach. The successful calibration of the model, carried out with treatment R in 2018, was evidenced by the strong correlation between the ASW measured through the capacitive probe and that simulated by SIMDualKc (b=0.988 and r2=0.995). After the model’s calibration, the separation between the transpiration and evaporation components was determined. The maximum transpiration during the growing season was observed in the full irrigation treatment. In this context, the study proceeded to apply the soil water balance components and transpiration generated by the model in the calculation of the WP. The fruit yield productivity was determined by accounting for the total water use in the growing season. The total water used was calculated by combining the volumes of water applied for irrigation and precipitation and the soil water extracted during the growing season by crops and cover crops. The deficit irrigation strategy showed the best performance in both years, with WP values of 3.31 and 1.81 kg m−3 for the years 2018 and 2019, respectively. Therefore, the study concluded that deficit irrigation proved to be the most effective irrigation strategy in terms of water productivity and crop water use efficiency (WUEc).

Upgrading Maize Cultivation in Bosnia and Herzegovina from Rainfed to Irrigated Systems: Use of Remote Sensing Data and the Dual Crop Coefficient Approach to Estimate Evapotranspiration

A two-year experiment was conducted with a local maize hybrid under full (F) and deficit (D) drip irrigation and rainfed conditions (R) to estimate maize evapotranspiration in Bosnia and Herzegovina (BiH). Three approaches, namely, A&P, SIMDualKc (SD), and vegetation index (VI), to estimate the actual crop coefficient (Kc act), the actual basal crop coefficient (Kcb act), and the actual crop evapotranspiration (ETc act), were applied with the dual crop coefficient method and remote sensing (RS) data for the first time. While Kcb act from all approaches matched FAO56 tabulated values, SD showed differences in comparison to A&P of up to 0.24 in D and R conditions, especially in the initial and mid-season stages. VI demonstrated very good performance in all treatments. In F, the obtained Kc act for all approaches during the initial and end stages were higher than the tabulated values, ranging from 0.71 to 0.87 for the Kc ini act and from 0.80 to 1.06 for the Kc end act, while the mid-season period showed very good agreement with the literature. The maize crop evapotranspiration range is 769–813 mm, 480–752 mm, and 332–618 mm for F, D, and R, respectively. The results confirmed the suitability of both approaches (SD and VI) to estimate maize crop evapotranspiration under F, with the VI approach demonstrating an advantage in calculating Kcb act, Kc act, and ETc act values under water stress conditions. The higher observed yields (67.6%) under irrigation conditions emphasize the need to transition from rainfed to irrigation-dependent agriculture in BiH, even for drought-resistant crops like maize.

Customizing pyfao56 for evapotranspiration estimation and irrigation scheduling at the Limited Irrigation Research Farm (LIRF), Greeley, Colorado

Estimation of evapotranspiration (ET, the water used by soil evaporation and plant transpiration) and soil water depletion (Dr, the amount of water to bring the soil water in the root zone to field capacity) are critical in irrigation water management. The open-source Python-based crop ET and water balance modeling package named “pyfao56” was originally developed based on the dual crop coefficient approach as described in the Food and Agricultural Organization of the United Nations, Irrigation and Drainage Paper No. 56 (FAO-56). The package later expanded on the seminal FAO-56 document to consider various options relevant to ET and water balance modeling, including both short (grass) and tall (alfalfa) reference crops, optional discretization of variable soil layers based on field capacity, and interpretation of readily available water (RAW) and Dr based on both a dynamic (growing) root zone and maximum root zone. The package requires two input data objects; the “Parameters” class defines variables affecting soil water balance and ET and the “Weather” class specifies relevant meteorological data. Other optional input data objects include the “Irrigation” class for irrigation events, the “Soil_Profile: class for defining stratified soil layer data, and the “Update” class for assimilation of measured data. Additional tools are available for estimating and forecasting standardized reference ET (ETref), providing seasonal water balance summaries, computing goodness-of-fit statistics between measured and modeled values, and visualizing time series plots of daily Dr, ET, and crop coefficients. The current pyfao56 release (v1.2.1) was incorporated into a customized workflow for specific use at the USDA-ARS Limited Irrigation Research Farm (LIRF) in Greeley, Colorado, and named the LIRF Implementation of Pyfao56 (LIRFIP). Field data from 2023 full and limited irrigation field trials were used to demonstrate the functionality of LIRFIP and its customizations and integration of pyfao56 into the LIRF workflow. Specific customizations included use of pyfao56 “customload” functions to input data from various sources, including weather from an on-site micrometerological station (using the application programming interface (API) developed by the maintainers of the Colorado Agricultural Meteorological (CoAgMet) weather station network), irrigation events (via a shared Google docs sheet), field capacity values by plot and soil layer (via an Excel spreadsheet), and measured soil water content as well as basal crop coefficient (Kcb) calculated from fractional canopy cover (fc) (via an SQLite database). To evaluate multiple research plots, LIRFIP was designed to iterate simulations from multiple instances of pyfao56 and to produce customized output summary files and interactive hypertext markup language (html) graphs as an aid for irrigation management decisions for LIRF field trials. This study demonstrated pyfao56 as a useful, flexible, customizable, and repeatable ET-based water balance model by showcasing its integration within a specific computational workflow for irrigation management field research at LIRF. The approach serves as an example for pyfao56 integration in other water management tools as conceived by water managers, researchers, and practitioners worldwide.

Spatio-temporal analysis of water requirement of maize (Zea mays L.) in Haryana state of India

Climate change have a considerable impact on crop water demand (ETc). The present study analyzed the spatio-temporal variation of the crop water requirement (ETc), crop water surplus deficit index (CWSDI), and the coupling degree of ETc and effective precipitation (Pe) for maize (Zea mays L.) crop grown in Haryana State of India using ArcMap 10.8 software. ETc was calculated using the Penman-Monteith method and crop coefficient (Kc) approach for 34 years (1985-2018) climatic data. Two statistical models, namely Mann–Kendall test and Sen’s slope estimator, were applied to understand the trend, while Pettitt's test was used to identify abrupt change points by using XLSTAT 2021 software.The result showed that for maize ETc ranged from 571 to 766 mm, CWSDI ranged from -29 to -74% and the degree of coupling of ETc and Pe ranged from 0.26 to 0.71 during 1985-2018. The decadal temporal variation of these indices indicated that ETc during 2007-2018 was relatively higher than it was from 1985-1995 and 1996-2006. The spatio-temporal values of CWSDI indicated that almost all the districts were under water deficit conditions for growing maize crop.Trend analysis showed significantly increasing trend of CWR for most of the districts except Jind, Mewat, Palwal, Panchkula, Rewari, Rohtak and Yamunanagar.The abrupt change point detection analysis of CWR indicates toward different change points, with maximum change points between the years 2004–2008.Such information will help to the farmers in the effective management of water for sustainable production under climate change in the near future.

Evaluating maize evapotranspiration using high-resolution UAV-based imagery and FAO-56 dual crop coefficient approach

Timely and accurate estimation of crop evapotranspiration (ETc) is essential for efficient irrigation management at the farmland scale. However, effective decision-making for irrigation scheduling requires high spatiotemporal-resolution data to provide within-field heterogeneity information. The objective of this study was to evaluate the use of optical and thermal information obtained from an unmanned aerial vehicle (UAV) to quantify maize (Zea mays L.) ETc within the framework of the FAO-56 dual crop coefficient approach. Canopy temperature-based crop water stress index (CWSI) and number of degrees above canopy threshold (DACT) were used to determine the stress coefficient (Ks). Three types of normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and enhanced vegetation index (EVI) were adopted to determine basal crop coefficient (Kcb). Then the two forms of stress coefficient in combination with three vegetation indices (VIs) were evaluated to estimate daily crop ETc under different irrigation treatments over two years in the southwest region of Inner Mongolia, China. The results demonstrated that the combination of NDVI and CWSI produced the best estimates of maize ETc, with R2 of 0.84 and root mean square error (RMSE) of 0.50 mm/day. The DACT-based model also performed well, with R2 of 0.77–0.80 and RMSE of 0.53 mm/day. Although the results varied with irrigation levels, the daily mean bias error of ETc predictions over different years indicated acceptable accuracy, with a mean bias error (MBE) of 0.48 mm/day. Sensitivity analyses indicated that ETc models were most sensitive to the slope of the linear regression between Kcb and VIs, followed by soil evaporation constant and influential factor in Ks_DACT. From this study, the combinations of vegetation index and CWSI, as well as DACT, were recommended as alternative approaches for estimating ETc due to intrinsic simplicity and easy interpretation. These ETc models relying on UAV-based multi-sensor data thus show promising potential in farmland-scale applications.

Evapotranspiration estimates of soybean using surface renewal: Comparison with crop coefficient approach

Evapotranspiration (ET) is widely considered the main consumptive water use in agricultural production and its accurate determination enables crop producers to make informed decisions. Field experiments were conducted in KwaZulu-Natal, South Africa to estimate soybean ET from sensible and latent heat flux obtained using the surface renewal (SR) method. Two versions of the SR method (SR2) were used. One version combines SR analysis with Monin-Obukhov similarity theory (MOST), hereinafter referred to as SRMOST. The other is a combination of SR analysis and dissipation theory (DT) referred to as SRDT. The ET estimated using SRMOST and SRDT (ETSRMOST and ETSRDT respectively) were compared to the ET obtained using the standard crop coefficient (Kc) approach (ETKc). During flowering, pod formation and seed filling, both SRMOST and SRDT methods slightly overestimated ET obtained using Kc approach with an average normalised root mean square error (NRMSE) of 23.4% and an average normalised mean absolute error (NMAE) of 10.1% for SRMOST and 21.7 and 9.4% for SRDT respectively. During senescence and at maturity, SRMOST and SRDT slightly underestimated ET compared to Kc approach. The average statistical measures for SRMOST were NRMSE = 21.0% and NMAE = 9.2%. Correspondingly, the statistics for SRDT were 17.5 and 7.1% respectively. Both SR2 methods estimated the minimum ET more accurately compared to the maximum. The SRDT method was more in agreement with Kc approach. Surface renewal is robust, less expensive than other micrometeorological techniques and a reliable method for deriving evapotranspiration of soybean when crop coefficients are problematic.

Water Requirement of Naked Oats under Subsurface Drip Irrigation in the North Foot of Yinshan Mountain Based on Single Crop Coefficient Approach

In order to accurately and rapidly calculate the water requirement of naked oats under drip irrigation in the north foot of Yinshan Mountain, the study adopted the K c and calculation method for potential evapotranspiration recommended in document FAO56 and applied SCCA to calculate the water requirement of crops based on the deficient irrigation experiments. The results indicated that the water requirements of naked oats without drought influence was 383.8 mm, and the maximum daily average water requirement intensity was heading flowering, which was the critical period of crop water requirement, and the mean value of E T 0 in the whole growth period was 3.89 mm/d. After correction, K C of naked oats in the initial growth stage, crop development growth stage, mid-season growth stage, and late season growth stage was 0.34, 0.94, 1.05, and 0.36. The average K C of naked oats in the whole growth stage was 0.71. SCCA does not consider the influence of wetting depth in the initial growth period of crops and the correction of local irrigation in the mid-season and late season stage of crops. Therefore, when the irrigation quota of naked oats is 315 mm, the crop water requirement calculated by SCCA is 363.84 mm, which is about 10% different from the measured value. The results show that SCCA can be used to calculate the water requirement of naked oats under drip irrigation at the northern foot of Yinshan Mountain in Inner Mongolia, and the calculation error is within the allowable range. However, it is also necessary to consider the influence of different irrigation forms and plant height of crops on calculation value.

Misconceptions of Reference and Potential Evapotranspiration: A PRISMA-Guided Comprehensive Review

One of the most important parts of the hydrological cycle is evapotranspiration (ET). Accurate estimates of ET in irrigated regions are critical to the planning, control, and regulation of agricultural natural resources. Accurate ET estimation is necessary for agricultural irrigation scheduling. ET is a nonlinear and complex process that cannot be calculated directly. Reference evapotranspiration (RET) and potential evapotranspiration (PET) are two primary forms of ET. The ideas, equations, and application areas for PET and RET are different. These two terms have been confused and used interchangeably by researchers. Therefore, terminology clarification is necessary to ensure their proper use. The research indicates that PET and RET concepts have a long and distinguished history. Thornthwaite devised the original PET idea, and it has been used ever since, although with several improvements. The development of RET, although initially confused with that of PET, was formally defined as a standard method. In this study, the Preferred Reporting Item for Systematic reviews and Meta-Analysis (PRISMA) was used. Equations for RET estimation were retrieved from 44 research articles, and equations for PET estimation were collected from 26 studies. Both the PET and RET equations were divided into three distinct categories: temperature-based, radiation-based, and combination-based. The results show that, among temperature-based equations for PET, Thornthwaite’s (1948) equation was mentioned in 12,117 publications, whereas among temperature-based equations for RET, Hargreaves and Samani’s (1985) equation was quoted in 3859 studies. Similarly, Priestley (1972) had the most highly cited equation in radiation-based PET equations (about 6379), whereas Ritchie (1972) had the most highly cited RET equations (around 2382) in radiation-based equations. Additionally, among combination-based PET equations, Penman and Monteith’s (1948) equations were cited in 9307 research studies, but the equations of Allen et al. (1998) were the subject of a significant number of citations from 23,000 publications. Based on application, PET is most often applied in the fields of hydrology, meteorology, and climatology, whereas RET is more frequently utilized in the fields of agronomy, agriculture, irrigation, and ecology. PET has been used to derive drought indices, whereas RET has been employed for single crop and dual crop coefficient approaches. This work examines and describes the ideas and methodologies, widely used equations, applications, and advanced approaches associated with PET and RET, and discusses future enhancements to increase the accuracy of ET calculation to attain accurate agricultural irrigation scheduling. The use of advanced tools such as remote sensing and satellite technologies, in addition to machine learning algorithms, will help to improve the accuracy of PET and RET estimates. Researchers will be able to distinguish between PET and RET in the future with the use of the study’s results.

Changes and determining factors of crop evapotranspiration derived from satellite-based dual crop coefficients in North China Plain

Evaluating actual crop evapotranspiration (ETc) variations and their determining factors under changing climates is crucial for agricultural irrigation management and crop productivity improvement in non-humid regions. This study analyzed the spatiotemporal characteristics and detected the determining factors of ETc for winter wheat and summer maize rotation system from 2000 to 2017 in the North China Plain (NCP), by combining the FAO-56 dual crop coefficient approach with remotely sensed vegetation indices (VIs). The results indicated that daily air temperature increased in varying degrees while wind speed and sunshine hours decreased slightly during the growing season of winter wheat and summer maize over the study period. The trends of relative humidity and effective precipitation varied in crop growing seasons. Based on the validated relationship of dual crop coefficients and VIs, the estimated multi-year average ETc of winter wheat (370.29 ± 31.28 mm) was much higher than summer maize (281.85 ± 20.14 mm), and the rotation cycle was 652.43 ± 27.67 mm. Annual ETc of winter wheat and the rotation cycle increased by 2.96 mm a−1 and 1.77 mm a−1, respectively. However, the ETc of summer maize decreased with distinct spatial variation. Spatially, winter wheat ETc increased significantly in the northeast NCP, covering the Beijing-Tianjin-Hebei areas. Meanwhile, significant increases in summer maize ETc were detected in the southwest NCP. The sensitivity and contribution analysis showed that ETc of winter wheat and summer maize was positively sensitive to temperature, wind speed, and sunshine hours while negatively to relative humidity. Moreover, wind speed and sunshine hours contributed most to changes in ETc (around 20%–40%).

Influence of different levels of irrigation and black plastic mulch on the performance of banana under drip irrigation

India, a leading fruit producing country, ranks first in banana cultivation. The crop is highly water demanding and that appears as a limitation in several areas. However, micro-irrigation has the potential to address the issue. Moreover, mulching with black plastic sheet further facilitates efficient water use. The combined application of micro-irrigation and black plastic mulch is potentially important for enhancing water use efficiency and crop productivity. Based on the above facts, a field trial was conducted to evaluate the performance of micro-irrigation levels and black plastic mulch on the performance of banana yield in the sub humid climatic condition in lateritic belt during 2019–2020. Modified penman-Montieth equation was used to estimate the crop water requirement considering the recorded local climatic data. Water requirement of banana under drip and plastic mulch may vary with non-mulched banana. Therefore, the dual crop coefficient approach has been used to estimate the crop coefficients (K) of banana under drip with black plastic mulch and without mulch conditions. The total water requirement of banana was estimated to be 1089 mm and 919 mm under non-mulched and black plastic mulched banana, respectively. To determine the optimum quantity of irrigation water requirement of banana, eight treatments were considered consisting of four irrigation levels, namely, 1.0 volume with drip (VD), 0.8 VD, 0.6 VD and 0.4 VD in combination with black plastic mulch and non-mulch. The results revealed that 0.8 VD along with black plastic mulch recorded better crop growth and yield parameters with higher yield in comparison to other treatments. Further, the treatment also resulted in maximum benefit cost ratio of 4.97.

Estimation of Crop Coefficients Based on Normalize Difference Vegetation Index

Crop coefficient is one of the most important parameters used for the estimation of crop evapotranspiration (ETc). Crop coefficient (Kc)-based estimation of crop evapotranspiration is most commonly used methods for irrigation water management. However, crop coefficient approach used for estimation ETc using the generalized crop coefficients mentioned in Irrigation and Drainage Paper No. 56 of the Food and Agricultural Organization of the United Nations can contribute to crop evapotranspiration estimates that are substantially different from actual crop evapotranspiration. The colinear relationship between the crop coefficient curve and a satellitederived Normalized Difference Vegetation Index (NDVI) showed potential for modeling a crop coefficient as a function of the NDVI, which is also one among the methods used for estimation of ETc in irrigation water management. The present study was conducted with objectives to present the techniques and procedures to develop and estimates Kc based on vegetation index (NDVI) extracted from satellite data. The relationships between and NDVI and crop coefficients (Kc) of wheat and chickpea for corresponding months were developed. The regression models developed are: (Kc) NDVI = 6.3268*NDVI-1.4207 for wheat and (Kc) NDVI = 5.7866 * NDVI-1.6699 for chickpea. The models showed strong relationships with R2= 0.86 and R2=0.84 for wheat and chickpea, respectively. The model and techniques to develop and estimate crop coefficients can be used in other regions in the global, and hence estimate crop evapotranspiration. The crop coefficients (Kc) estimated based on NDVI are useful for irrigation scheduling, evaluating irrigation performance, irrigation water management, and estimation of water use efficiency.

Agronomic Outcomes of Precision Irrigation Management Technologies with Varying Complexity

HighlightsCotton yield and water productivity were measured for different precision irrigation management solutions.Agronomic improvements from site-specific irrigation based on spatial FAO-56 crop coefficient data were minor.Thermal remote sensing data from unoccupied aircraft systems were able to identify crop water limitations.Integrated sensing and modeling tools that can achieve intended agronomic outcomes should be prioritized.Abstract. Diverse technologies, methodologies, and data sources have been proposed to inform precision irrigation management decisions, and the technological complexity of different solutions is highly variable. Additional field studies are needed to identify solutions that achieve intended agronomic outcomes in simple and cost-effective ways. The objective of this study was to compare cotton yield and water productivity outcomes resulting from different solutions for scheduling and conducting precision irrigation management. A cotton field study was conducted at Maricopa, Arizona, in 2019 and 2020 that evaluated the outcomes of four management solutions with varying technological complexity: (1) a stand-alone evapotranspiration-based soil water balance model with field-average soil parameters (MDL), (2) using site-specific soil data to spatialize the modeling framework (SOL), (3) driving the model with spatial crop coefficients estimated from an unoccupied aircraft system (UAS), and (4) using commercial variable-rate irrigation technology for site-specific irrigation applications (VRI). Soil water content data and thermal UAS data were also collected but used only in post hoc data analysis. Applied irrigation, cotton fiber yield, and water productivity were statistically identical for MDL and SOL. As compared to MDL, the UAS crop coefficient approach significantly reduced applied irrigation by 7% and 14% but also reduced yield by 5% and 26% in 2019 and 2020, respectively (p = 0.05). In 2019 only, the VRI approach maintained yield while significantly reducing applied irrigation by 8% compared to MDL, and water productivity was significantly increased from 0.200 to 0.211 kg m-3 when one outlier datum was removed (p = 0.05). Post hoc data analysis showed that crop water stress information, particularly from UAS thermal imaging data, would likely benefit the irrigation scheduling protocol. Efforts to develop integrated sensing and modeling tools that can guide precision irrigation management to achieve intended agronomic outcomes should be prioritized and will be relevant whether irrigation applications are site-specific or uniform. Keywords: Cotton, Crop coefficient, Drone, FAO-56, Irrigation scheduling, Remote sensing, Site-specific irrigation, Soil mapping, Unoccupied aircraft system, Variable-rate irrigation, Water stress.

Estimating and partitioning maize evapotranspiration as affected by salinity using weighing lysimeters and the SIMDualKc model

Water scarcity and saline stress are primary threats for water use and agricultural production in arid and semi-arid regions, such as the Hetao Irrigation District, China. The current study, using irrigation with saline water, developed through three seasons (2017–2019) of field experimental surveillance of mulched maize water use in two cropped weighing lysimeters. Measurements included crop characteristics (height, LAI, dates of development stages), soil and water salinity, and daily actual crop evapotranspiration (ETc act). Deficit surface irrigation was scheduled in 2017 and 2018 seasons, while in 2019 drip irrigation was used aimed at satisfying crop water requirements. Both approaches aimed at improving irrigation management while controlling impacts of salinity. The collected information was used to parameterize and calibrate the soil water balance model SIMDualKc. The model uses the FAO56 dual crop coefficient approach and considers the combined effect of water and salinity stresses due to salinity of both soil and irrigation water. Results show that the SIMDualKc model adequately simulates the dynamics of the observed ETc act throughout the three growing seasons. The goodness-of-fit indicators show highly appropriate model fitting of observed ETc act with low RMSE of 0.42 mm d-1 for the calibration and 0.53 mm d-1 for validation. The calibrated standard basal crop coefficients (Kcb) of maize for the initial, mid, and end stages were respectively 0.15, 1.15 and 0.25, which agree with those recently proposed by Pereira et al. (2021). Due to the impacts of both water and salt stress in 2017 and 2018, ETc act was much below the potential value ETc, ranging 64–83%, while in 2019 that percentage increased to 92% due to avoiding water stress by then. The average Kcb mid act range 0.58–1.06, therefore lower than Kcb mid, with the lower values occuring in the lysimeter that was irrigated with a large deficit. The irrigation scheduling practiced was assessed using water use and productivity indicators, which have shown the advantage in using drip irrigation with small time intervals between irrigations. Moreover, the current study and the calibrated model provide for adopting irrigation management practices that may save water and control salinity.

Yield and evapotranspiration characteristics of potato-legume intercropping simulated using a dual coefficient approach in a tropical highland

An accurate estimation of crop evapotranspiration and soil water balance under potato-legume intercropping is important for improving the crop water productivity of rainfed potato. This study quantified the evapotranspiration, yield, and soil water balance of potato (Solanum tuberosum L.) intercropped with lima bean (Phaseolus lunatus L.) or dolichos (Lablab purpureus L.) in comparison to the same crops under monocropping. The experiment was set up in three agroecological zones of Kenya: upper midland (with an altitude range of 1500–1653 m above sea level (masl)), lower highland (1892–1923 masl) and upper highland (2502–2594 masl). The dual crop coefficient approach was adopted with the SIMDualKc model to estimate crop evapotranspiration and compute the soil water balance. The dual crop coefficient partitions crop evapotranspiration into crop transpiration and soil evaporation by applying both soil evaporation and basal crop coefficient. The model was calibrated and validated using field data observed along four crop growth seasons. The basal crop coefficients, the ratios of soil evaporation to evapotranspiration, and that of transpiration to evapotranspiration were determined. The yields of different intercrops were converted into equivalent yield of potato based on price of the produce. Good agreement between the observed and simulated data for available soil water and crop evapotranspiration was found with modeling efficiency > 0.8. The residual mean square error was low and ranged from 0.01−0.08 m3 m-3 for available soil water and 0.03−0.09 mm d-1 for crop evapotranspiration. The transpiration to actual evapotranspiration ratio of potato-legume intercropping was 6−28% greater than that of the sole potato because legume intercrops fully covered the ground by mid-season, thus limiting the energy available for soil evaporation. Crop yields were significantly greater under intercropping as transpiration occurred near its potential rate, thus not limiting yields. These results support the dual crop coefficient method as an appropriate tool to estimate and accurately partition crop evapotranspiration under potato-legume intercropping.

Dual Crop Coefficient Approach in Vitis vinifera L. cv. Loureiro

Vineyard irrigation management in temperate zones requires knowledge of the crop water requirements, especially in the context of climate change. The main objective of this work was to estimate the crop evapotranspiration (ETc) of Vitis vinifera cv. Loureiro for local conditions, applying the dual crop coefficient approach. The study was carried out in a vineyard during two growing seasons (2019–2020). Three irrigation treatments, full irrigation (FI), deficit irrigation (DI), and rainfed (R), were considered. The ETc was estimated using the SIMDualKc model, which performs the soil water balance with the dual Kc approach. This balance was performed by calculating the basal coefficients for the grapevine (Kcb crop) and the active soil ground cover (Kcb gcover), which represent the transpiration component of ETc and the soil evaporation coefficient (Ke). The model was calibrated and validated by comparing the simulated soil water content (SWC) with the soil water content data measured with frequency domain reflectometry (FDR). A suitable adjustment between the simulated and observed SWC was obtained for the 2019 R strategy when the model was calibrated. As for the vine crop, the best fit was obtained for Kcb full ini = 0.33, Kcb full mid = 0.684, and Kcb full end = 0.54. In this sense, the irrigation schedule must adjust these coefficients to local conditions to achieve economically and environmentally sustainable production.

Transpiration and Water Use of an Irrigated Traditional Olive Grove with Sap-Flow Observations and the FAO56 Dual Crop Coefficient Approach

The SIMDualKc model was applied to evaluate the crop water use and the crop coefficient (Kc) of an irrigated olive grove (Olea europaea L.) located in Sicily, Italy, using experimental data collected from two crop seasons. The model applies the FAO56 dual Kc approach to compute the actual crop evapotranspiration (ETc act) and its components, i.e., the actual tree transpiration (Tc act), obtained through the basal crop coefficient (Kcb), and soil evaporation according to an evaporation coefficient (Ke). Model calibration was performed by minimizing the difference between the predicted Tc act and the observed daily tree transpiration measured with sap flow instrumentation (TSF field) acquired in 2009. The validation was performed using the independent data set of sap flow measurements from 2011. The calibrated Kcb was equal to 0.30 for the initial and non-growing season stages, 0.42 for the mid-season, and 0.37 for the end season. For both seasons, the goodness-of-fit indicators relative to comparing TSF field with the simulated Tc act resulted in root mean square errors (RMSE) lower than 0.27 mm d−1 and a slope of the linear regression close to 1.0 (0.94 ≤ b0 ≤ 1.00). The olive grove water balance simulated with SIMDualKc produced a ratio between soil evaporation (Es) and ETc act that averaged 39%. The ratio between actual (ETc act) and potential crop evapotranspiration (ETc) varied from 84% to about 99% in the mid-season, indicating that the values of ETc act are close to ETc, i.e., the adopted deficit irrigation led to limited water stress. The results confirm the suitability of the SIMDualKc model to apply the FAO56 dual Kc approach to tree crops, thus assessing the water use of olives and supporting the development of appropriate irrigation management tools that are usable by farmers. A different way to estimate Kcb is based on the approach suggested in 2009 by Allen and Pereira (A&P), which involves the measured fraction of ground covered (shaded) by the crop and the height of the trees. Its application to the studied grove produced the mid-season Kcb values ranging from 0.40–0.45 and end-season Kcb values ranging from 0.35–0.40. The comparison between the A&P-computed Tc act A&P and TSF field shows RMSE values ranging from 0.27 to 0.43 mm d−1, which demonstrates the adequacy of the latter approach for parameterizing water balance models and for irrigation scheduling decision making.

Application of reflectance based vegetation index to derive dual crop co-efficient for summer sesame

Dual crop co-efficient approach was applied to estimate seasonal water requirement for summer sesame using reflectance based vegetation indices. Field experiment was conducted to collect the required various crop physiologic parameters and NDVI data for the study crop during 2018 and 2019. Basal crop co-efficients and soil evaporation co-efficients collected from FAO-56 for initial mid and end stages of summer sesame were adjusted for study area using local weather parameters. Spectrum® Field Scout CM 1000 NDVI Meter were used to collect the NDVI data at various stages of study crop. The NDVI was measured from crop canopy and soil surface at 7 days intervals between 12.00 to 13.00 clocks. NDVI Based Basal Crop Co-efficient and Soil evaporation co-efficient were derived using standard methods. FAO estimated crop co-efficients were compared with NDVI based crop co-efficients. The co-efficient of determination of the fitted regression equation was found to be 0.836 and 0.765 for drip irrigation and 0.783 and 0.867 for surface control irrigation system for summer sesame during 2018 and 2019, respectively. Crop growing stage wise water requirement per unit area was estimated for both treatments. Results indicates that among these two methods, NDVI method estimate lowest water requirement in both cases i.e. total water requirement and during all the crop growth stages for both irrigation systems while daily crop water requirement was lower for all growth stages in drip system as compare with control system.

Predictions of soybean harvest index evolution and evapotranspiration using STICS crop model

AbstractIn the last century, soybean [Glycine max (L.) Merr.] genetic improvements have resulted in increased yield partly due to an increase in harvest index (HI). To account for these genetic improvements, an update of the soybean calibration of the STICS soil‐crop model was carried out. The model was calibrated and evaluated for two sets of soybean plant parameters using datasets from the Ottawa region (ON, Canada); a low HI cultivar calibrated using datasets (1993–2008) of cultivars of maturity groups (MGs) 00 and 0 and a high HI cultivar calibrated with more recent datasets (2016–2017) of cultivars of MGs 0 and I. The model succeeded in reproducing the HI increase. Leaf area index (LAI), shoot biomass, and yield were also well predicted for the high HI cultivars with a normalized root mean square error (NRMSE) of 34, 10, and 14%, respectively, which was a great improvement compared to the default parametrization proposed in STICS for soybean. Under rainfed conditions, accurate simulation of evapotranspiration is a critical point to achieve good model performance. A comparison of the two crop evapotranspiration approaches available in STICS was also carried out. It showed that the resistive approach (NRMSE of 36%) was more efficient than the crop coefficient approach (NRMSE of 67%). This good performance of the model in predicting evapotranspiration allowed the model to perform equally well under water stress and non‐water stressed conditions. The model could therefore be used in future studies to simulate the impact of water stress on soybean growth in eastern Canada.