Crop Coefficient Curves Research Articles

Irrigation Response, Water Use, and Lint Yield of Upland Cotton Cultivars

Highlights In-season crop water use and canopy temperatures after first square did not differ among cultivars. Lint yield was significantly affected by cultivar in all study years. Cultivar effects on lint yield were significant only at higher irrigation levels. Crop water productivity increased with increasing irrigation in two of the three study years. Abstract. Upland cotton (Gossypium hirsutum L.) production requires less irrigation compared with other crops and thus provides an opportunity to reduce risk and maintain profitability in areas where water is limited. Water use, canopy temperature, lint yield, and crop water productivity were evaluated for four early to medium maturity upland cotton cultivars under three levels (100%, 66%, and 33%) of alternate furrow subsurface drip irrigation (SDI) in a thermally limited environment. Crop evapotranspiration (ET) across cultivars and years averaged 627, 547, and 467 mm for the 100%, 66%, and 33% irrigation levels, respectively, and did not differ among cultivars (P > 0.05). Changes in stored soil water within each irrigation level were similar among cultivars, with significant differences occurring infrequently. Measured canopy temperatures from first white flower to two weeks after cutout did not significantly differ among cultivars (P > 0.10) within each irrigation level. Crop water use during boll maturation, as inferred from the developed crop coefficient curve, was considerably less than reported by other studies, signifying that irrigation could be terminated earlier without reducing lint yield. The cultivar effect on lint yield was significant in all study years (P < 0.001), but only at the 66% and 100% irrigation levels, with one cultivar exceeding the average yields of all evaluated cultivars by 13% across the three study years. Medium maturity cultivars usually yielded less than early maturity cultivars, especially for a year with less accumulation of thermal energy. Crop selection and late season irrigation water management were both key to improving cotton water productivity. Keywords: Upland cotton, Subsurface drip irrigation, Water productivity, Root water uptake, Canopy temperature.

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.

Accuracy of Estimated Crop Evapotranspiration Using Locally Developed Crop Coefficients against Satellite-Derived Crop Evapotranspiration in a Semiarid Climate

Actual crop evapotranspiration (ETa) is measured or estimated using different methods, and its accuracy is critical for water management under precision agriculture. The objective of this study was to compare maize ETa estimated by the two-step approach using a locally developed crop coefficient curve with satellite-retrieved evapotranspiration by six models incorporated in the OpenET to identify the best evapotranspiration estimation alternatives to the two-step approach for water management in northern New Mexico. Maize (Zea mays L.) was planted at the NMSU Agricultural Science Center at Farmington from 2017 to 2022 and uniformly managed across years. Water management in plants was based on maize’s actual evapotranspiration estimated as the product of the reference evapotranspiration and the local crop coefficient, which is described as a third-order polynomial function of the accumulated heat units by maize plants. For the same growing seasons, maize ETa was retrieved from satellite, and was estimated by six models listed within the OpenET from 2017 to 2022. The results show that maize daily ETa was consistently smaller when measured by SIMS and PT-JPL during maize initial and actively growing stages, while ETc(kc), SIMS and eeMETRIC showed similar maize daily ETa during maize full canopy development and mid-season, and which overcome the evapotranspiration estimated by DisALEXI, PT-JPL, geeSEBAL, and SSBop. ETc(kc) drastically dropped and became the lowest value among all ETa estimation models after the first fall snow or the first killing frost. Regarding the seasonal average, all six models included in OpenET showed smaller maize evapotranspiration. Maize seasonal evapotranspiration varied from 589.7 to 683.2 mm. eeMETRIC compares most similarly to the ETc(kc) model, followed by SIMS, with percent errors of 2.58 and 7.74% on a daily basis and 2.43 and 7.88% on a seasonal basis, with the lowest MBE and RMSE values, respectively, and could be used as an alternative for maize actual daily evapotranspiration for water management in northern New Mexico. The results of this study could be used by water managers and crop growers to improve water management in the Four Corners region, using eeMETRIC for crop water use to improve water management and conservation under sustainable agriculture.

Assessing Spatio-Temporal Dynamics of Deep Percolation Using Crop Evapotranspiration Derived from Earth Observations through Google Earth Engine

Excess irrigation may result in deep percolation and nitrate transport to groundwater. Furthermore, under Mediterranean climate conditions, heavy winter rains often result in high deep percolation, requiring the separate identification of the two sources of deep percolated water. An integrated methodology was developed to estimate the spatio-temporal dynamics of deep percolation, with the actual crop evapotranspiration (ETc act) being derived from satellite images data and processed on the Google Earth Engine (GEE) platform. GEE allowed to extract time series of vegetation indices derived from Sentinel-2 enabling to define the actual crop coefficient (Kc act) curves based on the observed lengths of crop growth stages. The crop growth stage lengths were then used to feed the soil water balance model ISAREG, and the standard Kc values were derived from the literature; thus, allowing the estimation of irrigation water requirements and deep drainage for independent Homogeneous Units of Analysis (HUA) at the Irrigation Scheme. The HUA are defined according to crop, soil type, and irrigation system. The ISAREG model was previously validated for diverse crops at plot level showing a good accuracy using soil water measurements and farmers’ irrigation calendars. Results show that during the crop season, irrigation caused 11 ± 3% of the total deep percolation. When the hotspots associated with the irrigation events corresponded to soils with low suitability for irrigation, the cultivated crop had no influence. However, maize and spring vegetables stood out when the hotspots corresponded to soils with high suitability for irrigation. On average, during the off-season period, deep percolation averaged 54 ± 6% of the annual precipitation. The spatial aggregation into the Irrigation Scheme scale provided a method for earth-observation-based accounting of the irrigation water requirements, with interest for the water user’s association manager, and at the same time for the detection of water losses by deep percolation and of hotspots within the irrigation scheme.

Modelling high-resolution Evapotranspiration in fragmented croplands from the constellation of Sentinels

The Sentinels for Evapotranspiration (Sen-ET) developed by the European Space Agency (ESA) provides a synoptic representation of actual ET (ETa) and other land-surface energy fluxes at high spatial (20 m) and temporal (daily) resolutions by the fusion of Sentinel-2 and Sentinel-3 datasets. However, the non-availability of optical sensor enabled Sentinel-2 images during the periods of high cloud cover such as monsoon limits the perennial use of Sen-ET. Here, we propose an alternate method to estimate ETa fluxes using locally observed weather data and the polarimetric parameters derived from Sentinel-1 with an application to fragmented, heterogeneous croplands. At first, the Sen-ET modelling approach with cloud-free datasets is validated using eddy covariance (EC) flux datasets (R2 = 0.74). Sen-ET derived ETa fluxes were used to develop crop coefficient (Kc) curves by considering reference ET (ET0) obtained from the weather data (Monteith, 1965b). Functional relations were then established between Sentinel-1 derived polarimetric parameters and Kc considering cloud-free datasets, and further translated to the periods of high cloud cover. We observed a strong dependency of VH on Kc for sugarcane and vegetables, and radar vegetation index (RVI) on Kc for rice (Rveg2=0.55,Rs.c.2=0.61,Rrice.2=0.51). The performance of Sentinel-1 derived ETa estimates were further validated with EC fluxes and found to be high during non-monsoon period (R2 = 0.74, RMSE = 1.25) than in monsoon period (R2 = 0.63, RMSE = 0.82). Our results indicate the suitability of Sentinel-1 in estimating ETa fluxes during the periods of high cloud cover for effective irrigation management.

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.

Influence of Different Drip Irrigation and Fertigation Levels on Yield and Economics of High-density Cotton

An experiment was conducted to study the effect of different drip irrigation and fertigation levels on yield and economics of high-density cotton at College of Agriculture, Professor Jayashankar Telangana State Agricultural University, Hyderabad, during kharif 2019 and 2020. The experiment consisted of twelve treatments laid out in Factorial randomised block design (FRBD) replicated thrice. Three irrigation levels (irrigation scheduled at 0.6 [I1], 0.8 [I2] and 1.0 [I3] Epan throughout the crop growth period) and four fertigation levels (application of 100% RDNK in differential dosage as per recommendation [F1], application of 100% RDNK in differential dosage as per crop coefficient curve [F2], application of 125% RDNK in differential dosage as per recommendation [F3], and application of 125% RDNK in differential dosage as per crop coefficient curve [F4],) were included as treatments in this study. Irrigation levels did not show any significant influence on yield and economics during 2020 and 2021. While among the four fertigation levels, application of 125% RDNK in differential dosage as per crop coefficient curve (F4) recorded significantly higher gross returns, net returns and B:C ratio which were on par with application of 125% RDNK in differential dosage as per recommendation (F3). Lower gross returns, net returns and B:C ratio were obtained with the application of 100% RDNK in differential dosage as per recommendation (F1) which were on par with application of 100% RDNK in differential dosage as per crop coefficient curve during both the seasons (F2). Further the gross returns and net returns produced under F3 was also on par with F2.

Energy balance and irrigation performance assessments in lemon orchards by applying the SAFER algorithm to Landsat 8 images

In the semiarid conditions of the São Francisco River Basin, Brazil, irrigated fruit crops are replacing natural vegetation, being lemon highlighted by incentives for the national and international markets. This paper aimed to support the rational water management of lemon orchards under different irrigation systems under these conditions. We present a methodology based on the use of the visible and near infrared images from Landsat 8 (L8) satellite together with weather and actual yield data (Ya), to assess the energy balance components and irrigation performance indicators (IPI) by applying the SAFER (Simple Algorithm for Evapotranspiration Retrieving) in six commercial farms under different irrigation systems inside the basin in the northern Minas Gerais state, Southeast Brazil. The ET rates averaged 2.7 mm d−1, 2.9 mm d−1, and 3.7 mm d−1, for drip, micro sprinkler, and pivot irrigated orchards, respectively. The evaporative fraction (latent heat flux by the available energy) reached above 1.00 for localized irrigation (drip and micro sprinkler), and 1.30 for pivots, during the lemon phenological stages from fruit growth to harvest peaks. Pivot irrigation systems were not recommended under the semi-arid conditions, due to large water direct evaporated from the soil and air close to the surface. For drip and micro sprinkler irrigated orchards, crop coefficient curves were modeled based on the accumulated degree-days (DDac) to estimate lemon crop water requirements. Drip irrigated orchards presented better water productivity levels being recommended together with deficit irrigation strategies which could allow good lemon yields with saving water savings. The most important findings of the current research are that the SAFER algorithm can be applied to estimate crop ET with satellite images without the thermal bands which together with modelled Ya data, irrigation assessments can be carried out at high spatial resolution following the principles of precision agriculture. For replication of the methods in other regions, simple calibrations of the modelling equations can be performed to infer the specific environmental conditions.

Evaluation of different methods of estimating ET for the performance assessment of irrigation schemes

In the assessment of irrigation schemes, the accuracy of performance indicators related to the water balance could be improved by estimating crop evapotranspiration (ETc) using remote sensing techniques. The two main remote sensing approaches to estimating ETc are the surface energy balance and the FAO56-based approach, that uses the ability of vegetation indices (VI) to trace the crop coefficient. Both approaches were evaluated comparatively at the Río Dulce irrigation scheme in Argentina (where the predominant crops are cotton, alfalfa, and maize) using products from the Landsat 7 and 8 sensors provided by the EEFlux application. The first analysis used field-specific, VI-derived basal crop coefficients obtained for 1743 fields using series of 9–29 satellite images along the 2014−15 irrigation campaign. The second analysis used 30 fields (grown with cotton and maize) where the actual irrigation schedules in the 2014−15 irrigation campaign were known. A root zone soil water balance was computed in these fields using the FAO56 dual approach with field-specific, VI-derived basal crop coefficients. The ETc obtained from the water balance was compared with the ETc estimated using a single crop coefficient approach that uses field-specific VI and takes into account soil evaporation (herein called synthetic approach), and with the ETc obtained with the METRIC surface energy balance model as facilitated by the EEFlux application. The third analysis was a simulation analysis of errors in the estimation of the ETc due to the interpolation to daily values of single crop coefficients and basal crop coefficients determined at hypothetical satellite overpass intervals of longer than one day. The VI-derived basal crop coefficient curves obtained for the 1743 fields of the first analysis were below the locally adopted standard (not field-specific) basal crop coefficient. Crop evapotranspiration in the 8005 ha covered by this analysis was about 20 % higher when applying standard non-field specific curves than when applying VI-derived curves. This difference pointed to the importance of using field-specific estimations of ETc. In the analysis carried out on the 30 selected fields, the ETc estimated using the VI-based approach agreed well with the ETc obtained from the water balance except under water deficit conditions. The crop coefficients obtained for these fields using the METRIC model correlated with those obtained by applying the VI-based method, although the former tended to be higher than the latter in the lower value range. The analysis of interpolation errors showed that when satellite overpass frequency is greater than one week and water deficit is mild or inexistent, the interpolation of crop coefficients (for instance, of those derived from an energy balance) gives errors of ETc estimations that are greater than those resulting from the VI-based approach. Under water deficit conditions, the VI-based approach systematically overestimates evapotranspiration.

An Attempt to Modify the Crop Coefficient Value of Wheat and Chickpea for Humid Subtropical Climate of Varanasi District, Uttar Pradesh

Crop water requirement is one of the essential components that should always be taken into account wherever water management strategies are adopted for effective utilization of water. In India agriculture is considered as the backbone of economy but nothing technical or advance technology has being adopted for its advancement and still major portion of agriculture are dependent on the verge of monsoon. Therefore crop coefficient (Kc) value for wheat and Chickpea as a function of relative humidity, wind speed and crop height has been formalized in this study for efficient use of available water. The empirical formula was applied for estimating Kc(mid) and Kc(end) values for humid subtropical climate of Varanasi. The corrected crop coefficient value of wheat for Kc(mid) was found to be 1.12 and for end season Kc(end) corrected was found to be 0.42. Mean maximum height of wheat crop in mid-season was obtained as 0.80 m and for the end season mean maximum height was found to be 0.99 m. The corrected crop coefficient value of Chickpea for midseason was found to be 0.98 whereas it was found out to be 0.38 at Kc(end). Mean maximum height of chickpea crop in mid-season was obtained as 0.39 m and for the end season of the crop mean maximum height was found to be 0.52 m. Crop coefficient curve for wheat crop was prepared in which X-axis represents time in days and Y- axis represents crop coefficient value for wheat. A third-order polynomial equation (y = -3E - 06x3 + 0.0005x2 - 0.0039x + 0.3702) has been obtained for wheat crop with correlation coefficient of 0.97. Similarly, graphical plot was constructed for chickpea crop and a third-order polynomial equation (y = -2E - 06x3 + 0.0002x2 - 0.0004x + 0.3732) has been obtained with correlation coefficient of 0.98. Furthermore, third-order polynomials were fitted well to predict the crop coefficient values as function of growing degree-days (GDD).

Evaluation of Crop Water Requirements for Yazagyo Irrigated Area, Myanmar

Accurate estimation of crop water requirements (ETc) is essential for the irrigation scheduling and water management of Yazagyo irrigated area, Myanmar. FAO defined the crop water requirements as the depth of water needed to meet the water loss through evapotranspiration of a crop, being disease-free, growing in large fields under non-restricting soil conditions, including soil water and fertility, and achieving full production potential under the given growing environment. It is essential to have a clear idea about the optimum water requirement for proposed crops under adaptable climatic conditions. The optimal crop water requirement mainly depends upon the accurate estimation of evapotranspiration and crop coefficient. To perform in this study, firstly Meteorological data of Kalay Station, in Myanmar are collected from 1995 to 2018. On the other hand, the different kinds of crops such as paddy, groundnut, sesame, sunflower, bean, and pea are considered for the estimation of seasonal crop water requirements for this area. Based on the crop growth stages in this area, crop coefficient curves are developed and crop coefficients are determined for each crop according to FAO manual. By using monthly crop coefficients and CROPWAT 8.0 software, reference crop evapotranspiration, crop water requirements and total irrigation requirements are presented for the selected area.

Adapting a Cloud-Based Irrigation Scheduler for Sugar Beets in the High Plains

Highlights An existing sugar beet crop coefficient curve (K cr ) was modified to better represent canopy development in northeast Colorado conditions. The modified K cr curve improved the estimated soil water deficits (net irrigation requirements) calculated by the cloud-based Water Irrigation Scheduler for Efficient Application (WISE App). Feedback from sugar beet growers and agronomists helped expand WISE applicability in the northern High Plains with access to additional weather station networks and functionality to aggregate irrigation data across multiple sugar beet fields or regions. Abstract . The convergence of agrometeorological network, database, and cloud-computing technologies has enabled greater accessibility of irrigation management tools for growers. The goal of this research and outreach project was to adapt an existing cloud-based irrigation scheduler (WISE) for use by sugar beet (Beta vulgaris L.) producers in eastern Colorado and a wider area of a cooperative operating in Colorado, Nebraska, Wyoming, and Montana. Four center pivot sugar beet fields in northeast Colorado were monitored during the 2013 and 2014 growing seasons. Soil water, leaf area index (LAI), and weather data were used to estimate the soil water deficit (net irrigation requirement) and to modify a crop coefficient (Kcr) curve originally reported in the literature based on growing degree days (GDD). The average cumulative GDDs for sugar beets to mature (100% maturity) was 2,944°C·d. The localized Kcr had a peak value (Kcr,mid) occurring between 43% and 69% of maturity, which corresponded to effective full cover (LAI = 3) and start of leaf senescence, respectively. In contrast, the original Kcr curve from literature had a longer duration of Kcr,mid spanning 33% to 83% of maturity. Use of the modified Kcr curve in lieu of the original Kcr curve in WISE reduced the relative error of soil water deficits by 12% to 35%. Feedback and collaborations from representative sugar beet growers and agronomists in the Western Sugar Cooperative led to expansion of WISE weather data access in the High Plains to include sugar beet growing areas in western Nebraska, eastern and northern Wyoming, and southern Montana. Keywords: Crop coefficient, Evapotranspiration, Irrigation scheduling, Soil water balance, Soil water deficit, Sugar beets.

A hybrid approach combining the FAO-56 method and the complementary principle for predicting daily evapotranspiration on a rainfed crop field

In this study, we proposed a hybrid approach combining the standard FAO-56 method with the complementary principle of evapotranspiration (ET) to predict actual ET (ETa) on a rainfed crop field. A generalized complementary relationship (GCR) was employed to quantify the water stress coefficient (Ks) for the FAO-56 method with no use of soil moisture data. Then, the Ks values were multiplied to the crop ET (ETc) estimated with the corrected air temperatures for the assumption of adequate land-surface moisture. The proposed hybrid method applied to a rotational maize-soybean field where an eddy-covariance flux tower was installed. Results showed that the reference ET (ETo) estimated by the corrected temperatures was within a range of 89–95% of that from non-corrected ones, implying that ETo could be inflated when using the mean air temperatures measured under sub-humid conditions. The Ks values estimated by GCR showed consistent patterns with temporal rainfall variation on the field. The multiplication of the Ks from GCR and ETc showed higher performance in ETa prediction than when the GCR was solely applied. By inverting the hybrid approach, a local crop coefficient curve for the rainfed maize field was developed, and led to further improvement in ETa prediction. This study suggests that combination of the complementary principle and the FAO-standard method, which are seemingly in separate paradigms, may improve ETa prediction on crop fields.

The influence of climate variability, soil and sowing date on simulation-based crop coefficient curves and irrigation water demand

Modern global agriculture depends heavily on irrigation water to maintain crop productivity. The widely used crop evapotranspiration estimation approach (i.e. FAO-56) can be applied to estimate the irrigation water demand of crops under optimal growth conditions by multiplying the reference evapotranspiration with often empirically determined crop-specific crop coefficients (Kc). To improve the transferability of the Kc approach while keeping the Kc concept as a relatively simple and visual tool for irrigation scheduling, a simulation-based approach to estimate site-specific continuous Kc curves was developed. The presented modeling framework consists of the process-based soil–crop–atmosphere system simulation model DAISY calibrated and validated against an extensive field data set and stochastic weather data to consider climate variability. The simulation-based Kc curves were exemplary estimated for common bean (two sowing dates) and white cabbage for several soils at an experimental site in Eastern Germany. The Kc curve ensembles (each 300 growth periods) generated by the modeling framework provided information on the probability of the Kc curves caused by the site-specific climate variability under recent climate conditions. By changing factors while fixing others (soil characteristics, sowing date), the influence of these factors on the Kc curves and the irrigation water requirement were evaluated. We found that the influence of the factors varied between the crops. The two tested common bean sowing dates had an influence on the Kc curves mainly due to varying growth rates and growth durations. The presented Kc curves may support farmers to make accurate in-season but also long-term estimations of the daily irrigation water demand under local conditions. The probability distribution of the curves provides additional information on the variability of the irrigation water demand due to climate variability.

Evaluation of single crop coefficient curves derived from Landsat satellite images for major crops in Iran

Improving the accuracy of single crop coefficient (Kc) for major crops at regional scales would significantly result in optimizing the consumption of agriculture water. In this study, a new approach is applied to generate Kc curves from satellite images based on the Priestley-Taylor equation. The equation mainly consists of shortwave radiation and thermal terms. Therefore in order to meet the equation’s requirements, the Landsat optical data was considered as the essential satellite-based input of the approach, yet thermal data was obtained from the MODIS imagery which was primarily downscaled using the TsHARP algorithm to fit the 30 m spatial resolution of Landsat; therefore, in this study the method is referred to as the MultiSensor Data Fusion for potential EvapoTranspiration calculation (MSDF-ET). The Kc trends were first evaluated for annual crops (forage maize, sugar beet, wheat, and barley) in two different regions (the Qazvin and Tehran-Karaj plains) using field observation data of Leaf Area Index (LAI). The method was then applied to the major crops in 31 selected plains with three different climatic conditions in Iran. Climatic conditions were categorized into cold (North West region), arid (Central region), and warm and humid (South West region). Eventually, crop water requirements (CWR) in different regions were compared considering long-term air temperature, wind, and relative humidity (RH) variations. Evaluation of the method in the Qazvin and Tehran-Karaj plains showed promising results. The Kc trend varied according to the LAI and temperature variability. Crop growth duration and especially Kc curves were successfully extracted using remotely sensed data in the studied areas. CWR in the zone with higher values of air temperature, notably in warm seasons, was higher in comparison with other climatic zones. Also lengths of the growing seasons were shorter. In conclusion, surface and groundwater resources management would be improved as the result of taking satellite data into account for Kc and subsequently CWR calculations.

Crop Coefficients and Transpiration of a Super Intensive Arbequina Olive Orchard using the Dual Kc Approach and the Kcb Computation with the Fraction of Ground Cover and Height

The SIMDualKc model was used to simulate crop water requirements for a super high density olive orchard in the region of Alentejo, Portugal. This model uses the dual crop coefficient approach to estimate and partitioning the actual crop evapotranspiration (ETc act) and therefore to perform the soil water balance. The model was calibrated with 2011 tree transpiration using trunk sap flow measurements and was validated using similar data from 2012 and tested with 2013 data. Low root mean square errors (RMSE < 0.53 mm·d−1) and acceptable modelling efficiency indicators (EF > 0.25) were obtained. Further validation was performed comparing modelled ETc act with eddy covariance measurements. These indicators support the appropriateness of using SIMDualKc to guide irrigation management. The basal crop coefficient (Kcb) curves obtained with SIMDualKc for those 3 years were compared with the Kcb values computed with the Allen and Pereira approach (A&P approach) where Kcb is estimated from the fraction of ground cover and plant height considering an adjustment factor for crop stomatal control (Fr). Fr values were obtained through a trial and error procedure through comparing the Kcb estimated with this approach and with SIMDualKc. The Kcb curves obtained by both methods resulted highly correlated, which indicates that the A&P approach may be used in the irrigation management practice to estimate crop water requirements. Results of performing the soil water balance with SIMDualKc have shown that soil evaporation is a large fraction of ETc act, varying between 41% and 45% for the 3 years under study. Irrigation, applied with a drip system, represented 39 to 56% of ETc act, which shows the great importance of irrigation to achieve the water requirements of super intensive olive orchards. Nevertheless, the analysis has shown that the irrigation management adopted at the orchard produces a water deficit larger than desirable, with a ratio of ETc act to non-stressed crop evapotranspiration (ETc) varying from 70% to 94% during the mid-season, when that ratio for a eustress irrigation management could be around 90%.

Dynamics of crop coefficients for citrus orchards of central India using water balance and eddy covariance flux partition techniques

Accurate quantification of crop water demand and characterizing its temporal variability is essential in evaluating the role of water saving strategies for sustainable production. Representing evapotranspiration (ET) as a proxy to crop water needs is often misleading, particularly during the periods of high non-stomatal exchanges. This study is aimed at modeling crop water requirements for citrus orchards of central India using single and dual crop coefficient approaches. ET fluxes derived from eddy-covariance (EC) technique were used to develop single crop coefficient (Kc) curves at daily, weekly, and seasonal scales. Site-specific Kc values for initial, mid, and late season were found to be 0.43, 0.78, and 0.80 respectively. ET partitioning was done by estimating soil evaporation coefficient (Ke), and basal crop coefficient in the presence of water stress (Ks×Kcb) using soil-water balance (SIMDualKc) and EC flux partitioning (EC FP) methods. SIMDualKc model was calibrated against electrical resistivity tomography (ERT) derived soil water contents (R2 = 0.81, RMSE = 0.021 cm3 cm−3). Energy based flux partitioning was done by considering the correlation between high-frequency water vapour and carbon fluxes and applying flux variance similarity principles. Direct measurement of evaporation (E) and transpiration (T) at four citrus trees (using micro-lysimeters and sap flow meters) was used to assess the performance of two models. Three-stage basal crop coefficients from SIMDualKc and EC FP methods were found to be 0.18, 0.57, 0.63 and 0.26, 0.51, 0.59 respectively. Both methods were effectively partitioning the ET fluxes at daily scale (R2 > 0.5, RMSE < 0.6 mm, NSE > 0.3), however, variability in estimated fluxes between the two methods is high during the initial stage and gradually diminished with time.

Determination of maize water requirement using remote sensing data and SEBAL algorithm

Efficient agricultural water demand management in arid and semi-arid regions is key in continuing crop production in a changing climate. As such, there’s a need for further investigation on various crops to identify the optimum water requirements to avoid water wasting in regions that are already facing water shortage. The focus of this work is to determine water requirement maize farming Mazandaran Province in Northern Iran, located on the southern side of the Caspian Sea, using Landsat satellite data. In order to use SEBAL algorithm, the images were atmospheric calibrated. Evapotranspiration maps with RMSE values equals to 0.73, 1.38 and 0.74 mm/day were produced and compared to Reference Book (RB), National Water Document (NWD) and FAO56 values. Furthermore, by computing reference evapotranspiration, crop coefficient curve was evaluated. In order to prepare maize water requirement maps, ET0 in 58 weather stations throughout the province were computed and interpolated. By using the daily ET0 maps and resulted Kc values, maize water requirement maps for the cultivated area of the province were 345.16–383.99 mm. The maize water requirement in the observation station of Sari compared to RB and NWD values showed −%20 and +%41 differences and RMSE was 76 and 156 mm, respectively.

Crop Evapotranspiration, Irrigation Water Requirement and Water Productivity of Maize from Meteorological Data under Semiarid Climate

Under the semiarid climate of the Southwest United States, accurate estimation of crop water use is important for water management and planning under conservation agriculture. The objectives of this study were to estimate maize water use and water productivity in the Four Corners region of New Mexico. Maize was grown under full irrigation during the 2011, 2012, 2013, 2014 and 2017 seasons at the Agricultural Science Center at Farmington (NM). Seasonal amounts of applied irrigation varied from 576.6 to 1051.6 mm and averaged 837.7 mm and the total water supply varied from 693.4 to 1140.5 mm. Maize actual evapotranspiration was estimated using locally developed crop coefficient curve and the tabulated United Nations Food and Agriculture Organization (FAO) crop coefficients, and from this maize water productivity was determined. Maize actual daily evapotranspiration (ETa) varied from 0.23 to 10.2 mm and the seasonal ETa varied with year and ranged from 634.2 to 697.7 mm averaging 665.3 mm by the local Kc curve, from 687.3 to 739.3 mm averaging 717.8 mm by the non-adjusted FAO Kc values, and from 715.8 to 779.6 mm averaging 754.9 mm with the FAO adjusted Kc values. Maize irrigation requirements varied from 758.4 to 848.3 mm and averaged 800.2 mm using the local developed Kc and varied from 835.5 to 935.6 mm and averaged 912.2 mm using FAO Kc. The net irrigation requirement varied from 606.8 to 678.6 using local Kc curve, and from 682.78 to 748.5 mm when adopting the FAO Kc values. Average irrigation requirement was 641 mm under the local Kc option and 730 mm under FAO Kc values option. Maize crop water use efficiency (CWUE) ranged from 1.3 to 1.9 kg/m3 and averaged 1.53 kg/m3, evapotranspiration water use efficiency (ETWUE) values were higher than CWUE and varied from 2.0 to 2.3 kg/m3, averaging 2.1 kg/m3. Maize irrigation water use efficiency (IWUE) was varied with years and averaged 1.74 kg/m3. There were strong relationships between maize CWUE and maize seasonal irrigation amounts of IWUE and the seasonal irrigation amounts with R2 of 0.97 and 0.92, respectively. Maize CWUE increased linearly with maize IWUE with a coefficient of determination R2 of 0.99, while IWUE showed a strong quadratic relationship with ETWUE (R2 = 0.94). The results of this study can be used as a guideline for maize water management under the semiarid conditions in northwestern New Mexico and other locations with similar climate and management conditions. Irrigation requirements for maize should be adjusted to the local meteorological conditions for optimizing maize irrigation requirement and improving maize water productivity.

Necesidades hídricas de estevia calculadas con el coeficiente del cultivo

This study aimed to determine the crop coefficient (Kc) curve for stevia (Stevia rebaudiana Bertoni) in Candelaria, Valle del Cauca, Colombia. The experiment was conducted during the first half of 2015. In a plot planted with stevia, were located three drainage lysimeters arranged randomly and a portable weather station to determine climatic variables necessary for calculating an evapotranspiration reference (ETo), using the Penman Monteith equation. Soil eld capacity moisture was determined and regular monitoring of both, soil moisture and drainage water depth were performed. Irrigation was applied using an exhaustion coefficient of 10% to bring it back to field capacity moisture. Statistical analyzes were performed and Kc was calculated from the ratio of crop evapotranspiration (ETc) to ETo, for each plant in phenological phase. With condidence of 95% Kc for stevia were: growth stage (54 days after transplantation, ddt) 0.86 ± 0.12, mature stage (55-72 ddt) 1.24 ± 0.10 and senescence stage (72-96 ddt) 0.85 ± 0.14. Water consumption of Stevia rebaudiana B., was 4753 m3/ha during its growing cycle of 96 days after transplantation.