Grass Reference Crop Research Articles

Maize Crop Coefficients under Variable and Fixed (Uniform) Rate Irrigation and Conventional and Variable Rate Fertilizer Management in Three Soil Types

Maize (Zea mays L.) evapotranspiration crop coefficients (Kc) that are needed to estimate crop evapotranspiration (ETc) using the two-step approach for variable rate irrigation and nitrogen management under different soil types have not been investigated or quantified. In this research, alfalfa- and grass-reference crop coefficients (Kcr and Kco) curves were developed for fixed rate or uniform rate fertigation (FRF), variable rate fertigation (VRF) and pre-plant nitrogen (PP) management under fixed rate or uniform rate irrigation (FRI) and variable rate irrigation (VRI) for three soil types [Crete silt loam (S1), Hastings silty clay loam (2) and Hastings silt loam (S3)] in 2015, 2016 and 2017 growing seasons. Irrigation and nitrogen management strategies, as well as soil type, all influenced the Kcr and Kco values, which exhibited inter-annual variation. On average, greater variation in Kc curves between FRF, VRF and PP nitrogen treatment were observed under VRI treatments as compared with FRI. Results showed that Kc values are more dependent on the amount rather than the timing of the nitrogen application. In all three seasons, higher Kc values were observed in the FRI treatment than VRI with Kcr, ranging from 0.07 to 1.30 in FRI and 0.07 to 1.20 in VRI. Kc curves also differed between nitrogen treatments and the difference was more prominent in the VRI treatments than in the FRI in all years. In general, maximum Kc was observed in PP nitrogen treatment, followed by FRF and VRF. On a monthly average basis, maximum Kc values were observed in July and August in all soil types and minimum Kc values were observed in June. When soil types are considered, overall, the maximum Kcr value was observed in FRI-PP treatment in S1 (1.02), FRI-VRF treatment in S2 (1.06) and FRI-VRF treatment in S3 (1.02). The Kcr and Kco equations as a function of growing degree days were developed and monthly average Kcr and Kco values were tabulated for practical applications. To the best of the authors’ knowledge, this research is the first that investigated and quantified the impact of VRI and VRF strategies under FRF, VRF and PP fertilizer management strategies on maize Kc values. The Kcr and Kco values quantified in this research can aid irrigators, state agencies and other water management and agricultural professionals for more accurate crop water use determinations under different irrigation and nitrogen management strategies and different soil types.

Quantifying evapotranspiration and crop coefficients for cotton (Gossypium hirsutum L.) using an eddy covariance approach

Accurate quantification of consumptive water requirements (ETc, evapotranspiration) of cropping systems is a critical prerequisite for sustainable irrigation water management applications. For applying the ETc for irrigation scheduling across soils and climates other than the location in which it was measured, it is also critical to develop crop coefficients (Kc) that link a reference crop evapotranspiration computed from local weather data to ETc. A systematic study for deriving Kc for cotton (Gossypium hirsutum L.) - representing its growth stages from planting to harvest - in humid climates is lacking in the literature. In this study, we used an eddy covariance (EC) method to quantify ETc from irrigated cotton (Gossypium hirsutum L.) in a 250 ha field with a Tunica clay soil, in 2017 and 2018. In the EC experiment, an open-path infrared gas analyzer and a sonic 3-D anemometer were deployed in the constant flux layer above the cotton canopy for collecting crop-canopy water flux data. Using the measured ETc, Kc were derived for alfalfa (Kcr) and grass (Kco) reference crop ET computed from weather data. Cotton cv. Delta Pine Land 1522 was planted in the first week of May and harvested in the second week of September in both the years. Lint yield was 1269 kg ha−1 in 2017 and 1569 kg ha−1 in 2018. Measured monthly averaged daily ETc ranged between 2.5 mm in May/September to 4 mm in July in 2017, and between 2.9 mm in May and 4.4 mm in August in 2018. Maximum daily ETc in 2017 and 2018 crop seasons were 5.6 and 6.7 mm, respectively. Seasonal total ETc was 367 mm and 439 mm (on average 402 mm), respectively. Alfalfa (ETr) and grass reference crop ET (ETo) computed were 664 and 546 mm, respectively. Averaged across the two years, average daily Kcr ranged between 0.45 in May to 0.80 in August, and Kco ranged from 0.54 in May and 0.99 in August. On average, seasonal ETr was 18 % more than ETo. Seasonal ETr and ETo were, respectively, 39 % and 22 % more than ETc. The Kc data developed will be useful for irrigation scheduling in cotton grown in similar climates and soils.

ESTIMATING OF EVAPOTRANSPIRATION USING ARTIFICIAL NEURAL NETWORK

This study investigates the application of artificial neural networks (ANNs) on the prediction of daily grass reference crop evapotranspiration (ET0) and compares the performance of ANNs with the conventional method (Penman–Monteith). The use of ANNs was examined number of hidden layers and the activation function were also tested. The best ANN architecture for estimation of daily ET0 was obtained for different data set for Nubaria. Using these data, the networks were trained with daily climatic data (maximum and minimum temperature, dew point and wind speed) as input and the Penman– Monteith (PM) estimated ET0 as output. The analysis was carried out with MATLAB software. Feed forward one-layer networks with sigmoid function were used. Performance evaluation of the models have been carried out by calculating root mean square error (RMSE), The network were selected based on maximized R and R2 value and minimized RMSE values which were 0.98, 0.957 and 0.44 mm/day, respectively in testing. The optimal ANN (4-12-1) for Nubaria regions showed a satisfactory performance in the ET0 estimation. These ANN models may therefore be adopted for estimating ET0 in the study area with reasonable degree of accuracy.

Discussion of “Actual Crop Evapotranspiration and Alfalfa- and Grass-Reference Crop Coefficients of Maize under Full and Limited Irrigation and Rainfed Conditions” by K. Djaman and S. Irmak

Discussion of “Actual Crop Evapotranspiration and Alfalfa- and Grass-Reference Crop Coefficients of Maize under Full and Limited Irrigation and Rainfed Conditions” by K. Djaman and S. Irmak

Parameter Variability and Drought Models: A Study Using the Agricultural Reference Index for Drought (ARID)

Water stress is one of the crucial factors that determine crop yield losses. The Agricultural Reference Index for Drought (ARID) is a generic plant water stress index that estimates the level of water stress as the ratio of actual evapotranspiration to reference evapotranspiration, on a daily time step, considering a hypothetical grass reference crop. The objective of this study was to determine the effect of soil inter‐ and intraclass variability, plant characteristics, and climate locations on ARID output using global sensitivity and uncertainty analysis. Root zone depth, maximum uptake factor, the wilting point (θwp), field capacity (θfc), drainage coefficient, and runoff curve number were the ARID model parameters, while inputs consisted of rainfall and reference evapotranspiration. Global sensitivity analysis was performed for five locations in the southeastern United States and four soil types, sandy loam, silt loam, sandy clay loam, and silty clay, using three methods, the method of Sobol’, partial correlation coefficients, and partial rank correlation coefficients. Sensitivity analyses results indicated the monotonic and linear nature of ARID. The effect of location on the results of this study was minimal. Root zone depth was found to be the most influential model parameter, followed by soil hydraulic properties (i.e., θfc and θwp). Uncertainty in ARID outputs varied from soil to soil (intersoil variability), with soils having similar sand content showing similar results. Considerable uncertainty was seen in the sandier soils, indicating the need for accurate estimation of soil hydraulic properties in such soils.

Actual Crop Evapotranspiration and Alfalfa- and Grass-Reference Crop Coefficients of Maize under Full and Limited Irrigation and Rainfed Conditions

AbstractThe evapotranspiration crop coefficients (Kc) that are used to estimate actual evapotranspiration (ETa) using a two-step approach (i.e., Kc×reference ET) for limited irrigated and rainfed m...

Interannual water vapor and energy exchange in an irrigated maize-based agroecosystem

In this paper, we present results from 4 years (May 2001–May 2005) of water and energy flux measurements made in a no-till, irrigated maize–soybean rotation system in eastern Nebraska, USA. The peak green leaf area index (LAI) reached 6.0 and 5.5 in maize (2001 and 2003, respectively) and 5.7 and 4.4 in soybean (2002 and 2004, respectively). The dependence of evapotranspiration (ET) on leaf area was consistent with previous studies. There was a nearly linear relationship between the daily ET/ET o (where ET o is the reference evapotranspiration over a grass reference crop) and LAI until a threshold LAI (between 3 and 4). Above this threshold LAI, the ET/ET o was virtually independent of LAI. The cumulative growing season (planting to harvest) evapotranspiration was 544 and 578 mm for maize, and 474 and 430 mm for soybean. The interannual variability in the growing season ET totals correlated very well with the number of days when the LAI was greater than 3. The non-growing season period (harvest to subsequent planting) contributed between 20 and 25% of the annual ET totals for both crops. The maximum canopy surface conductance ( G smax) was 29 mm s −1 for maize in both years, 41 mm s −1 for soybean in 2002 (peak LAI = 5.7) and 36 mm s −1 for soybean in 2004 (peak LAI = 4.4). The variability in G smax was largely explained by the leaf nitrogen concentration, consistent with the literature.

Estimating Evapotranspiration using Artificial Neural Network

This study investigates the utility of artificial neural networks (ANNs) for estimation of daily grass reference crop evapotranspiration (ETo) and compares the performance of ANNs with the conventional method (Penman–Monteith) used to estimate ETo. Several issues associated with the use of ANNs are examined, including different learning methods, number of processing elements in the hidden layer(s), and the number of hidden layers. Three learning methods, namely, the standard back-propagation with learning rates of 0.2 and 0.8, and backpropagation with momentum were considered. The best ANN architecture for estimation of daily ETo was obtained for two different data sets (Sets 1 and 2) for Davis, Calif. Using data of Set 1, the networks were trained with daily climatic data (solar radiation, maximum and minimum temperature, maximum and minimum relative humidity, and wind speed) as input and the Penman–Monteith (PM) estimated ETo as output. The best ANN architecture was selected on the basis of weighted sta...

Forecasting and optimizing furrow irrigation management decision variables

Furrow irrigation can be better managed if the management decision variables (irrigation time and amount; inflow rate and cutoff) can be determined ahead of time. In this study, these decision variables were forecast and optimized using 1 day ahead grass reference crop evapotranspiration (ET0) forecasts, based on the ARMA (1,1) time-series model, with a seasonal furrow irrigation model for both homogeneous and heterogeneous infiltration conditions. Heterogeneity in infiltration characteristics was restricted to variations along the furrow length as opposed to variations between furrows. The results obtained were compared with their counterparts using the observed ET0 for the same period during the 1992 cropping season. Seasonal performance (application efficiency, inflow, runoff and deep percolation volumes) and economic return to water (yield benefits minus seasonal water related and labor costs) were affected by infiltration conditions, while irrigation requirement and bean yield were unchanged. In a given infiltration case, seasonal performance, irrigation schedules, bean yield and economic return to water were comparable (lower than 4% difference) for the two ET0 conditions. For each ET0 condition, individual irrigation events resulted in different irrigation designs (inflow rate and cutoff time) except inflow rates with heterogeneous infiltration. Differences in inflow volume were less than 2% and 5%, respectively, for homogeneous infiltration and heterogeneous infiltration. For the conditions studied, furrow irrigation management decision variables can be forecast and optimized to better manage the irrigation system, because irrigation performance was the same for both (forecast and observed) ET0 cases.

Converting from Pan Evaporation to Evapotranspiration

Evapotranspiration is an essential component of the hydrologic cycle, and its accurate estimation is necessary for many hydrologic studies. Via the pan coefficient (\iK\ip), pan evaporation data are widely used to estimate grass reference crop evapotranspiration (\IETo\N). An equation for predicting \iK\ip that includes both categorical (wind run and relative humidity) and quantitative (fetch length) data was developed using the indicator regression approach. The developed equation predicted \iK\ip values better than the existing equations.

Optimization of furrow irrigation schedules, designs and net return to water

A seasonal furrow irrigation model consisting of irrigation scheduling and kinematic-wave-based hydraulic submodels was modified to incorporate an economic optimization submodel. The model used a systematic simulation technique to optimize furrow irrigation schedules and designs assuming 80% irrigation adequacy at cutoff time. The irrigation schedules and designs were optimized for the homogeneous and heterogeneous infiltration under the mean and observed ET o (grass reference crop ET) conditions. The optimal management allowable depletion (MAD) level changed with the variation in ET o condition, and with the consideration of spatial and temporal (seasonal) variability in infiltration characteristics. Irrigation design changed with both infiltration conditions and MAD level. Infiltration variability did not influence the bean yield. However, the return to water decreased when spatial variability in infiltration conditions was considered. Using mean ET o resulted in slightly higher yield and net return to water as compared to using observed ET o. A small variation in daily mean ET o values with respect to daily observed ET o values caused a change in both irrigation schedules and designs. Therefore, mean ET o cannot be used to forecast irrigation schedules and designs at the beginning of crop season. The net return to water increased (1.7 to 3.6%), and the seasonal inflow, losses, and bean yield decreased in the case of variable interval scheduling (holding MAD constant) as compared to the fixed interval scheduling (MAD varies).

Economic Optimization of Furrow Irrigation

A seasonal furrow irrigation model was constructed from soil moisture, kinematic-wave hydraulic, and economic optimization models to study the effects of heterogeneity in water balance, soil water holding properties and rooting depth, and infiltration functions on furrow irrigation design (flow rate and cutoff time), bean yield, and net return to water. Irrigation designs achieving nearly 100% irrigation adequacies were unchanged by heterogeneity in water balance, soil water properties, and rooting depth, but both bean yield and net return to water decreased with increasing heterogeneity. Irrigation designs were sensitive to infiltration characteristics and to irrigation interval. At a given irrigation interval, bean yield was insensitive to infiltration characteristics but consistently decreased with increasing irrigation interval. Net return to water was less for spatially variable infiltration functions as compared to homogeneous infiltration conditions. Using mean evapotranspiration (ET) of a grass reference crop (ETo) resulted in slightly higher bean yield and net return to water as compared to observed ETo (1992 season values of grass reference crop ET). Spatial and temporal variability in infiltration gave the same optimal irrigation interval (10 d) under both ETo conditions for the chosen irrigation criteria (80% irrigation adequacy at cutoff time). Both ETo conditions led to essentially the same design inflow rates. Errors in inflow volume were less than 12 and 3.5%, respectively, for individual irrigations and on a seasonal basis. Irrigation scheduling and surface irrigation design can be forecast at the beginning of the growing season for the conditions studied using historical mean ETo if spatial and temporal variability of infiltration are considered.

EVAPOTRANSPIRATION AND CROP COEFFICIENTS FOR YOUNG BLUEBERRIES IN FLORIDA

A three-year study of water use by two species of cultivated blueberry was conducted. The experiment was performed on two rabbiteye (Vaccinium ashei Reade) varieties, Premier and Powderblue, and one highbush (Vaccinium corymbosum L.) variety, Sharpblue. Water was applied to the plants using a microirrigation system. Irrigations were triggered at three soil water tension levels: 10-kPa, 15-kPa, and 20-kPa. Water use for both species was monitored using drainage-type lysimeters and a water budget method. Grass reference crop modified coefficients for the modified Penman equation (Burman, 1980) during the first three years of establishment were developed based on the 10-kPa treatments for both species. Modified crop coefficients for microirrigated highbush blueberries were below 0.2 during the first two years of plant establishment and increased to 0.35 in the third year as the plants rapidly increased in size. Coefficients for the rabbiteye varieties were consistently higher during all three years, reaching 0.5 in the third year.

Equation for Evaporation Pan to Evapotranspiration Conversions

The evaporation rate from a class evaporation pan located in a site surrounded by low-growing vegetation can be used to estimate potential evapotranspiration for a grass reference. The evaporation rate for pans and the evapotranspiration rate of grass differ depending on siting and weather conditions. Weather-based pan coefficients for the conversion of pan evaporation to evapotranspiration of a tall grass reference crop have been published and equations have been developed to estimate the published coefficients. However, the equations used to estimate pan coefficients are complex. A simpler equation to estimate potential evapotranspiration for a grass reference from evaporation pan data, upwind fetch of low-growing vegetation, mean daily wind run, and mean daily relative humidity is presented. The equation can be used to automate the computation of evapotranspiration from electronically recorded evaporation pans or to analyze historical evaporation pan data. The equation can be substituted for more c...