Kendall C. DeJonge

Minimizing instrumentation requirement for estimating crop water stress index and transpiration of maize

AbstractnResearch was conducted in northern Colorado in 2011 to estimate the crop water stress index (CWSI) and actual transpiration (Ta) of maize under a range of irrigation regimes. The main goal was to obtain these parameters with minimum instrumentation and measurements. The results confirmed that empirical baselines required for CWSI calculation are transferable within regions with similar climatic conditions, eliminating the need to develop them for each irrigation scheme. This means that maize CWSI can be determined using only two instruments: an infrared thermometer and an air temperature/relative humidity sensor. Reference evapotranspiration data obtained from a modified atmometer were similar to those estimated at a standard weather station, suggesting that maize Ta can be calculated based on CWSI and by adding one additional instrument: a modified atmometer. Estimated CWSI during four hourly periods centered on solar noon was largest during the 2xa0h after solar noon. Hence, this time window is recommended for once-a-day data acquisition if the goal is to capture maximum stress level. Maize Ta based on CWSI during the first hourly period (10:00–11:00) was closest to Ta estimates from a widely used crop coefficient model. Thus, this time window is recommended if the goal is to monitor maize water use. Average CWSI over the 2xa0h after solar noon and during the study period (early August to late September, 2011) was 0.19, 0.57, and 0.20 for plots under full, low-frequency deficit, and high-frequency deficit irrigation regimes, respectively. During the same period (50xa0days), total maize Ta based on the 10:00–11:00 CWSI was 218, 141, and 208xa0mm for the same treatments, respectively. These values were within 3xa0% of the results of the crop coefficient approach.

Water productivity of maize in the US high plains

Maize water production functions measured in a 4-year field trial in the US central high plains were curvilinear with 2.0xa0kgxa0m−3 water productivity at full irrigation that resulted from 12.5xa0Mgxa0ha−1 grain yields with 630xa0mm of crop evapotranspiration, ETc. The curvilinear functions show decreasing yield but relatively constant water productivity up to 25% ETc reduction. Water productivity declined rapidly with ETc reductions greater than 25% and was zero at about 40% of full ETc because about 270xa0mm of ETc was required to produce the first unit of grain yield. These results corroborate those of previous studies that show reduction in irrigated area rather than deficit irrigation will usually provide higher net returns if water consumption (ETc) is limited. Water balance techniques adequately estimated ETc when precision irrigation was carefully scheduled and seasonal precipitation was low. Water productivity relationships based on ETc are more transferable than those based on irrigation water applied.

Sensitivity analysis of reference evapotranspiration to sensor accuracy

Sensor inaccuracy can result in reference evapotranspiration inaccuracy.We performed local and global (Morris and eFAST) sensitivity analysis.Both semi-arid Colorado and humid Florida were evaluated.Sensitivity method results were strongly correlated with each other.Local sensitivity analysis is recommended to weather network managers. Meteorological sensor networks are often used across agricultural regions to calculate the ASCE Standardized Reference Evapotranspiration (ET) Equation, and inaccuracies in individual sensors can lead to inaccuracies in ET estimates. Multiyear datasets from the semi-arid Colorado Agricultural Meteorological (CoAgMet) and humid Florida Automated Weather Network (FAWN) networks were evaluated using a local sensitivity analysis (LSA) method which calculated the total error range of each individual sensor, as well as Morris and eFAST global sensitivity analysis (GSA) methods which simultaneously evaluated the full accuracy range of each sensor. Sensitivity of inputs (i.e., temperature, humidity, wind speed, and solar radiation) generally had values within the same range for the FAWN network with solar radiation being the most influential input in the summer, while sensitivity to wind speed for the CoAgMet network was much higher than the other inputs. Due to its simplicity and ease of application, LSA is suggested as a minimal screening method for evaluating input sensor sensitivity. GSA results were highly correlated with each other, but local sensitivity was poorly correlated to GSA methods regarding wind input in Colorado. Uncertainty analysis showed the current configuration of sensors in the CoAgMet network to have a higher range of ET values between 5% and 95% confidence intervals, as compared to the FAWN network. The eFAST GSA method was applied using a hypothetical set of best case sensors in both stations (i.e., sensors with the best accuracy between both sites), showing solar radiation to be the most influential input in the high ET months of summer, and the sensitivity in Colorado to wind to be vastly decreased, suggesting that the CoAgMet network could benefit from an upgrade to more accurate anemometers.

Assessing corn water stress using spectral reflectance

ABSTRACT Multiple remote-sensing techniques have been developed to identify crop-water stress; however, some methods may be difficult for farmers to apply. If spectral reflectance data can be used to monitor crop-water stress, growers could use this information as a quick low-cost guideline for irrigation management, thus helping save water by preventing over-irrigating and achieving desired crop yields. Data was collected in the 2013 growing season near Greeley, Colorado, where drip irrigation was used to irrigate 12 corn (Zea mays L.) treatments with varying water-deficit levels. Ground-based multispectral data were collected and three different vegetation indices were evaluated. These included the normalized difference vegetation index (NDVI), the optimized soil-adjusted vegetation index (OSAVI), and the Green normalized difference vegetation index (GNDVI). The three vegetation indices were compared to water stress as indicated by the stress coefficient (Ks), and water deficit in the root zone was calculated using a soil water balance. To compare the indices to Ks, vegetation ratios were developed from vegetation indices in the process of normalization. Vegetation ratios are defined as the non-stressed vegetation index divided by the stressed vegetation index. Results showed that vegetation ratios were sensitive to water stress as indicated by the good coefficient of determination (R2 > 0.46) values and low root mean square error (RMSE < 0.076) values when compared to Ks. To use spectral reflectance to manage crop-water stress, an example irrigation trigger point of 0.93 for the vegetation ratios was determined for a 10–12% loss in yield. These results were validated using data collected from a different field. The performance of the vegetation ratio approach was better than when applied to the main field giving higher goodness of fit values (R2 > 0.63), and lower error values (RMSE < 0.043) between Ks and the vegetation indices.

Simulating Unstressed Crop Development and Growth Using the Unified Plant Growth Model (UPGM)

Since initial development of the EPIC model in 1989, the EPIC plant growth component has been incorporated into other erosion and crop management models (e.g., WEPS, WEPP, SWAT, ALMANAC, and GPFARM) and subsequently modified to meet research objectives of the model developers. This has resulted in different versions of the same base plant growth component. The objectives of this study are the following: (1) describe the standalone Unified Plant Growth Model (UPGM), initially derived from the WEPS plant growth model, to be used for merging enhancements from other EPIC-based plant growth models; and (2) describe and evaluate new phenology, seedling emergence, and canopy height sub-models derived from the Phenology Modular Modeling System (PhenologyMMS V1.2) and incorporated into UPGM. A 6-year (2005–2010) irrigated maize (Zea mays L.) study from northeast Colorado was used to calibrate and evaluate UPGM running both the original (i.e., based on WEPS) and new phenology, seedling emergence, and canopy height sub-models. Model statistics indicated the new sub-models usually resulted in better simulation results than the original sub-models. For example when comparing original and new sub-models, respectively, for predicting canopy height, the root mean square error (RMSE) was 53.7 and 40.7xa0cm, index of agreement (d) was 0.84 and 0.92, relative error (RE) was 26.0 and −1.26xa0%, and normalized objective function (NOF) was 0.47 and 0.33. The new sub-models predict leaf number (old sub-models do not), with mean values for 4xa0years of 2.43 leaves (RMSE), 0.78 (d), 18.38xa0% (RE), and 0.27 (NOF). Simulating grain yield, final above ground biomass, and harvest index showed little difference when running the original or new sub-models. Both the new phenology and seedling emergence sub-models respond to varying water deficits, increasing the robustness of UPGM for more diverse environmental conditions. Future research will continue working to incorporate existing enhancements from other EPIC-based plant growth models to unify them into one model such as multispecies competition and N cycling.

SEBAL-A: A Remote Sensing ET Algorithm that Accounts for Advection with Limited Data. Part II: Test for Transferability

Because the Surface Energy Balance Algorithm for Land (SEBAL) tends to underestimate ET when there is advection, the model was modified by incorporating an advection component as part of the energy usable for crop evapotranspiration (ET). The modification involved the estimation of advected energy, which required the development of a wind function. In Part I, the modified SEBAL model (SEBAL-A) was developed and validated on well-watered alfalfa of a standard height of 40–60 cm. In this Part II, SEBAL-A was tested on different crops and irrigation treatments in order to determine its performance under varying conditions. The crops used for the transferability test were beans (Phaseolus vulgaris L.), wheat (Triticum aestivum L.) and corn (Zea mays L.). The estimated ET using SEBAL-A was compared to actual ET measured using a Bowen Ratio Energy Balance (BREB) system. Results indicated that SEBAL-A estimated ET fairly well for beans and wheat, only showing some slight underestimation of a Mean Bias Error (MBE) of −0.7 mm·d−1 (−11.3%), a Root Mean Square Error (RMSE) of 0.82 mm·d−1 (13.9%) and a Nash Sutcliffe Coefficient of Efficiency (NSCE) of 0.64. On corn, SEBAL-A resulted in an ET estimation error MBE of −0.7 mm·d−1 (−9.9%), a RMSE of 1.59 mm·d−1 (23.1%) and NSCE = 0.24. This result shows an improvement on the original SEBAL model, which for the same data resulted in an ET MBE of −1.4 mm·d−1 (−20.4%), a RMSE of 1.97 mm·d−1 (28.8%) and a NSCE of −0.18. When SEBAL-A was tested on only fully irrigated corn, it performed well, resulting in no bias, i.e., MBE of 0.0 mm·d−1; RMSE of 0.78 mm·d−1 (10.7%) and NSCE of 0.82. The SEBAL-A model showed less or no improvement on corn that was either water-stressed or at early stages of growth. The errors incurred under these conditions were not due to advection not accounted for but rather were due to the nature of SEBAL and SEBAL-A being single-source energy balance models and, therefore, not performing well over heterogeneous surfaces. Therefore, it was concluded that SEBAL-A could be used on a wide range of crops if they are not water stressed. It is recommended that the SEBAL-A model be further studied to be able to accurately estimate ET under dry and sparse surface conditions.

Validation of a Decision Support System for Improving Irrigation System Performance

AbstractTo address water shortage and improve water delivery operations, decision support systems (DSSs) have been developed and utilized throughout the United States and the world. One critical aspect that is often neglected during the development and implementation of DSSs is validation, which can result in flawed water distribution and rejection of the DSS by water users and managers. This paper presents the results of a significant validation effort for a DSS in the Middle Rio Grande Conservancy District (MRGCD). The validation resulted in a refined application efficiency of 45%, a refined readily available water for farmers to irrigate to a value of 20%, and a Nash-Sutcliffe modeling efficiency of 0.86 for soil moisture depletion patterns. Overall, the validation and refinement of input parameters resulted in a DSS model that accurately predicts evaportranspiration and can be used to schedule water delivery. The refinement of the DSS input parameters resulted in an increased 15,600 acre-ft diversion ...

Improved soil water deficit estimation through the integration of canopy temperature measurements into a soil water balance model

The total available water in the soil root zone (TAWr), which regulates the plant transpiration, is a critical parameter for irrigation management and hydrologic modeling studies. However, the TAWr was not well-investigated in current hydrologic or agricultural research for two reasons: (1) there is no direct measurement method of this parameter; and (2) there is, in general, a large spatial and temporal variability of TAWr. In this study, we propose a framework to improve TAWr estimation by incorporating the crop water stress index (CWSI) from canopy temperature into the Food and Agriculture Organization of the United Nations (FAO) paper 56 water balance model. Field experiments of irrigation management were conducted for maize during the 2012, 2013 and 2015 growing seasons near Greeley, Colorado, USA. The performance of the FAO water balance model with CWSI-determined TAWr was validated using measured soil water deficit. The statistical analyses between modeled and observed soil water deficit indicated that the CWSI-determined TAWr significantly improved the performance of the soil water balance model, with reduction of the mean absolute error (MAE) and root mean squared error (RMSE) by 17 and 20%, respectively, compared with the standard FAO model (with experience estimated TAWr). The proposed procedure may not work under well-watered conditions, because TAWr may not influence the crop transpiration or crop water stress in both daily and seasonal scales under such conditions. The proposed procedure potentially could be applied in other ecosystems and with other crop water stress related measurements, such as surface evapotranspiration from remote sensing methodology.

Leaf temperature of maize and Crop Water Stress Index with variable irrigation and nitrogen supply

Crop canopy temperature and Crop Water Stress Index (CWSI) are used for assessing plant water status and irrigation scheduling, but understanding management interactions is necessary. This study evaluated whether nutrient deficiencies would confound interpretation of plant water status from leaf temperature. Leaf temperature and CWSI in maize (Zea mays L.) were evaluated with different irrigation strategies and varying nitrogen (N) supply for replicated glasshouse and field studies. Glasshouse treatments consisted of well-watered or simulated drought and sufficient, intermediate, or deficient N. Field study treatments consisted of well-watered, controlled deficit irrigation, or simulated drought and sufficient, sufficient delayed, or deficient N. Average CWSI values varied across irrigation treatments, with 0.37 and 0.54 for glasshouse well-watered and drought and 0.34, 0.47, and 0.51 for field well-watered, drought, and controlled deficit treatments, respectively. Nitrogen levels created widely different leaf chlorophyll contents without affecting leaf temperature or CWSI. Canopy water stress measurements were robust across varying N levels, but CWSI did not correlate well with leaf area due to confounding effects of irrigation timing and nitrogen levels. Leaf temperature and CWSI are useful for evaluating crop water status, but nutrient status and timing of water stress must also be considered for crop growth prediction.

Global sensitivity and uncertainty analysis of nitrate leaching and crop yield simulation under different water and nitrogen management practices

Abstract Assessing the sensitivity and uncertainty of soil-crop models is beneficial to model calibration and development of best water and N management practices. This study adopted the Morris screening method and the Sobol’ variance-based method, combined with an agricultural system model (WHCNS), to analyze the global sensitivity and uncertainty of nitrate leaching and crop yield to model input parameters under different water and N management practices. A two-year field experiment was conducted in a desert oasis of Inner Mongolia, China using a factorial combination of standard (Istd, standard, 750xa0mm per season; Nstd, standard, 138xa0kgxa0Nxa0ha−1) and conservation (Icsv, conservation, 570xa0mm per season; Ncsv, conservation, 92xa0kgxa0Nxa0ha−1) levels of irrigation and N fertilization: IstdNstd, IstdNcsv, IcsvNstd and IcsvNcsv. Sensitivity analysis (SA) based on this experiment showed that nitrate leaching demonstrated significant sensitivity to soil hydraulic and crop parameters, but generally low sensitivity to N transformation parameters. Based on Sobol’ SA, crop parameters accounted for 64.3%, 63.2%, 39.2% and 39.2% of simulated nitrate leaching variability for the IstdNstd, IstdNcsv, IcsvNstd and IcsvNcsv treatments, respectively. The greater the crop water and N stress, the stronger the parameters interaction. Uncertainty analysis showed the average amount of nitrate leaching under Istd (135.3xa0kgxa0Nxa0ha−1) to be 2.3 times greater than under Icsv (58.0xa0kgxa0Nxa0ha−1); however, the distributions of yield between the four treatment combinations did not show significant differences. Overall, irrigation practice was the main factor influencing the parameter sensitivities and the uncertainty of nitrate leaching and crop yield simulation.