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Highlights Losses for MESA and LESA were comparable on the day of irrigation and oftentimes greater for the subsequent day. Losses were greater due to incomplete canopy conditions for both MESA and LESA on both days. Evaporative losses from irrigation extended to at least the subsequent day following irrigation in most cases. Losses over two days accounted for as much as 39.5% and 28.0% of irrigation depth for MESA and LESA, respectively. Abstract. Effective irrigation systems that increase crop water productivity by minimizing evaporative losses are paramount for extending the longevity of finite groundwater resources in the semi-arid U.S. Southern High Plains (SHP). Although subsurface drip irrigation (SDI) acreage has increased in recent years, center-pivot sprinkler systems still account for greater than 85% of the irrigated area in the SHP. Modern sprinkler configurations are typically classified according to application height as either mid-elevation spray application (MESA) or low-elevation spray application (LESA). While application drift and evaporative losses are easily measured under fallow conditions, quantifying evaporative losses under cropped conditions is difficult. Lysimeter-derived daily evapotranspiration (ET) values for SDI-irrigated and sprinkler-irrigated fields planted to corn in 2016 (MESA) and 2018 (LESA) near Bushland, TX, were compared for days when sprinkler irrigation events occurred and for subsequent days, when possible. Differences (extra ET) were attributed to evaporative losses associated with MESA and LESA irrigation. Average daily extra ET values for both sprinkler irrigation methods were similar on the day of irrigation, although MESA was slightly larger than LESA at 1.4 and 1.2 mm, respectively. The average daily extra ET values for incomplete canopy conditions were 2.2 mm for MESA and 1.9 mm for LESA, while values were identical for both methods at 0.6 mm for full canopy conditions. Average daily extra ET values were also expressed as a percentage of daily standardized grass reference ET (ETos) values. Average values for MESA and LESA were 20.1% and 13.5%, respectively, for the season, with similar findings of 29.3% and 19.4% for incomplete canopy conditions. Average extra ET/ETos values for incomplete canopy conditions were similar at 7.5% and 7.7% for MESA and LESA, respectively. Evaporative irrigation losses, calculated as the percentage of extra ET to irrigation depth, were slightly larger overall for the day of irrigation for MESA (5.4%) than LESA (5.2%). Losses of 7.9% and 7.0% were observed for incomplete canopy conditions for MESA and LESA, respectively. Average losses for LESA (3.5%) under full canopy conditions were greater than those for MESA (1.9%). A comparison of extra ET values for days following irrigation revealed that evaporative losses from irrigation events extended beyond the day of irrigation. MESA extra ET values for the day following irrigations increased by 57.1% (2.2 mm) overall, 13.6% (2.5 mm) for incomplete canopy conditions, and 150.0% (1.5 mm) for full canopy conditions. The same was true for LESA, with increases of 125.0% (2.7 mm) overall, 78.9% (3.4 mm) for incomplete, and 216.7% (1.9 mm) for full canopy conditions. Summing of extra ET values for the day of irrigation and the subsequent day yielded average values more than double those for the day of irrigation only, at 3.9 and 4.3 mm for MESA and LESA, respectively. Similarly, values for extra ET as a percentage of irrigation depth were also more than double those for the day of irrigation only, with the greatest loss values of 39.5% for MESA and 28.0% for LESA. These findings suggest that although LESA appears to mitigate evaporative losses marginally more in corn than MESA on the day of irrigation, considerably more evaporative losses occurred for both methods during the subsequent day, with slightly increased losses for LESA, resulting in little difference between overall losses over the two days. This may in part be explained by the temporary cooling effect of the irrigation inside the canopy on the day of irrigation, which is diminished by the second day. A greater discrepancy between evaporative losses for MESA and LESA is likely to be observed for crops having shorter stature or lower leaf density, such as cotton, although more study is needed to corroborate this claim. Knowledge of these findings provides useful information for both producers and water managers when considering irrigation management and water planning strategies. Keywords: Evaporation, Evapotranspiration, LESA, MESA, Semi-arid, Sprinkler Irrigation, Subsurface Drip Irrigation, Transpiration, Weighing Lysimeters.

HighlightsLater planting and greater site elevation or latitude decreased seasonal growing degree days and cotton yield in Kansas.Higher irrigation capacity (rate) usually increased lint yield, which was probably due to increased early boll load.Strategies for splitting land allocations between high irrigation rates and dryland did not increase production.Cotton may reduce irrigation withdrawals from the Ogallala aquifer, but the Kansas growing season limits production.Abstract. Precipitation in the western Great Plains averages about 450 mm, varying little with latitude and providing 40% to 80% of potential crop evapotranspiration (ETc). Supplemental irrigation is required to fully meet crop water demand, but the Ogallala or High Plains aquifer is essentially non-recharging south of Nebraska. Pumping water from this aquifer draws down water tables, leading to reduced water availability and deficit irrigation to produce an alternate crop such as cotton (Gossypium hirsutum L.) with a lower peak water demand than corn (Zea mays L.). Our objective was to compare simulated cotton yield response to emergence date, irrigation capacity, and application period at three western Kansas locations (Colby, Tribune, and Garden City) with varying seasonal energy or cumulative growing degree days (CGDD) and compare split center pivot deficit irrigation strategies with a fixed water supply (i.e., where portions of the center pivot land area are managed with different irrigation strategies). We used actual 1961-2000 location weather records with the GOSSYM simulation model to estimate yields of cotton planted into soil at 50% plant-available water for three emergence dates (DOY 145, 152, and 159) and all combinations of irrigation period (0, 4, 6, 8, and 10 weeks beginning at first square) and capacity (2.5, 3.75, and 5.0 mm d-1). Simulated lint yield and its ratio to ETc, or water use efficiency (WUE), consistently decreased with delayed planting (emergence) as location elevation or latitude increased due to effects on growing season CGDD. Depending on location, simulated cotton lint consistently increased (p = 0.05) for scenarios with increasing irrigation capacity, which promoted greater early season boll load, but not for durations exceeding 4 to 6 weeks, probably because later irrigation and fruiting did not complete maturation during the short growing season. Cotton WUE generally increased, with greater yields resulting from earlier emergence and early high-capacity irrigation. We calculated lower WUE where irrigation promoted vigorous growth with added fruiting forms that delayed maturation and reduced the fraction of open bolls. The irrigation strategy of focusing water at higher capacities on a portion of the center pivot in combination with the dryland balance did not increase net yields significantly at any location because the available seasonal energy limited potential crop growth and yield response to irrigation. However, the overall net lint yield was numerically larger for focused irrigation strategies at the southwest Kansas location (Garden City). Based on lint yields simulated under uniform or split center pivot deficit irrigation, we conclude that cotton is poorly suited as an alternative crop for central western and northwestern Kansas because of limited growing season CGDD. Keywords: Cotton, Crop simulation, Deficit irrigation, Evapotranspiration, Irrigation capacity, Split center pivot irrigation, Water use efficiency, Yield limiting factors.

Dwindling water resources and weather variability present two of the major limiting factors for irrigated cotton production in the Southern Great Plains (SGP) of the United States. Under these conditions, there is a dire need to understand the trends and fluctuations in cotton yields to help producers make better irrigation and crop management decisions. Crop models coupled with long-term weather data provide an opportunity for evaluating yield variabilities by simulating numerous potential scenarios. In this study, the AquaCrop model was calibrated and validated for cotton at two sites in the SGP. The validated model was then applied to investigate the effect of variable irrigation capacity (IC) on cotton yield during a 33-year period (1981–2013). The AquaCrop model performed with acceptable accuracy for simulating canopy cover, soil water content, evapotranspiration and yield indicating that it is a potential tool for evaluating variable cotton irrigation scenarios in the SGP. The response of cotton yield to IC was highly dependent on heat unit availability. Yields increased significantly with increase in water availability in years when total growing season heat units were above 1100 °C day. The yield response to irrigation diminished considerably as the magnitude of growing season heat units decreased. No significant increase in mean cotton yield was found at IC higher than 0.3 L s−1 ha−1.

HighlightsCrop coefficients for SDI scheduling for grain corn should be reduced by ~10% from those used for sprinkler irrigation.FAO 56 methods to calculate crop coefficients for surface drip irrigation under full-cover plastic mulch were applicable to SDI.A recent drought-resistant corn hybrid appeared to begin rapid leaf area development about 10 days earlier than older hybrids.A recent drought-resistant corn hybrid appeared to finish earlier than older hybrids by about 15 days.Abstract. Subsurface drip irrigation (SDI) has become an important irrigation application method in the U.S. Southern High Plains where pan evaporation exceeds 2,400 mm per year. Early research comparing SDI with spray sprinklers showed that SDI was over-applied when scheduling irrigations using crop coefficients developed using sprinkler irrigation. Thus, crop coefficients developed using SDI may be smaller than those developed using sprinkler irrigation. Grain corn was grown for two years on large, precision weighing lysimeters at Bushland, Texas, with two lysimeters irrigated by SDI and two by mid-elevation spray (MESA) irrigation. Data used in this study were for fields irrigated to replenish soil water in the top 1.5 m of the soil to field capacity, as indicated by weekly neutron probe readings (100% replenishment). Crop coefficients developed for SDI (Kc_SDI) were compared with those developed for MESA (Kc_MESA) using ASCE standardized reference ET equations. The value of Kc_SDI ranged from 0.83 to 0.89 of Kc_MESA for the two years. Values of Kc_SDI remained consistently less than Kc_MESA even after maximum leaf area index was reached, indicating that considerable evaporative loss from the soil surface occurred with MESA irrigation even after full canopy cover. When we shortened the initial period after planting from 30 to 20 d and followed FAO 56 recommendations for surface drip irrigation under full-cover plastic mulch, we calculated basal Kc (Kcb) values (ETo basis) that were reasonably close to our Kc values for SDI for the crop development and early mid-season periods but were greater than our data for the later mid-season and late season periods. Keywords: Crop coefficient, FAO56, MESA, SDI, Sprinkler irrigation, Subsurface drip irrigation.

HighlightsDaily maximum crop coefficient (Kc) values were similar for legacy hybrids and a modern drought-tolerant (DT) hybrid.Later planting dates for a DT hybrid resulted in average season lengths ~25 days shorter than those of legacy hybrids.Results illustrated the effects of environment, planting date, interannual variation in temperature, and the importance of climate-specific Kc functions.DT hybrids may be more effective at reproductive growth during periods of heat stress in semi-arid environments, although additional data are needed to support this conclusion.Abstract. Corn (Zea mays L.) is a major irrigated crop grown in the Southern High Plains including the Texas Panhandle. Irrigation from the Ogallala Aquifer is required to sustain profitable corn production in the region by supplementing inadequate and erratic rainfall. Effective irrigation scheduling works to extend limited groundwater resources by avoiding water losses associated with runoff and/or percolation below the root zone. The use of crop coefficient (Kc) and reference evapotranspiration (ETo) values to estimate daily crop water use (ETc) remains an effective scheduling tool that can complement other irrigation scheduling approaches. Both Food and Agriculture Organization (FAO-56) piecewise and curvilinear Kc values for corn are found in the literature. However, advances in corn genetics have led to questions about the applicability of Kc values developed using legacy corn hybrids to irrigation of modern drought-tolerant (DT) hybrids. Lysimeter-derived Kc values for legacy corn hybrids grown in large weighing lysimeter fields at the USDA-ARS Conservation and Production Research Laboratory at Bushland, Texas, were compared with those derived from a modern DT corn hybrid recently grown in the same fields. Results indicated that although midseason daily Kc values were similar for all hybrids, average season length was ~25 days shorter for the modern DT hybrid, characterized by a shortened initial growth period followed by more rapid increase of Kc during the development period. However, plots of Kc over thermal time illustrated that the differences in season length were likely attributable to later planting dates associated with the DT corn hybrids. Average seasonal water use was 730 and 811 mm for the legacy and modern DT hybrids, respectively (three years each), with corresponding average yields of 1.2 and 1.4 kg ha-1. Results suggest that published Kc and Kcb values developed with legacy corn hybrids remain largely applicable to modern DT corn hybrids when used with accurate estimates of effective canopy-based growth stages and climate-specific Kc functions. Keywords: Crop coefficients, Drought-tolerant, Evapotranspiration, Maize, Weighing lysimeters.

HighlightsSWAT soil water assessment was performed using soil water measurements.Dryland SWAT model soil water content was greater than the irrigated SWAT model.Using SWAT soil water estimates for real-time (daily) irrigation management purposes with the existing SWAT soil water subroutines and available soils data is considered risky.The surface layer showed the greatest soil water variability compared to deeper layers.Abstract. Soil water content (SWC) is a challenging measurement at the field, watershed, and regional scales. Soil and Water Assessment Tool (SWAT) soil water estimates were evaluated at three locations: the St. Joseph River watershed (SJRW) in northeast Indiana, the USDA-ARS Conservation and Production Research Laboratory (CPRL) at Bushland, Texas, and the USDA-ARS Limited Irrigation Research Farm (LIFR) at Greeley, Colorado. The soil water estimates were evaluated under two scenarios: (1) for the defined soil profile, and (2) by individual layer. Each site’s soil water assessment was performed based on the existing management conditions during each experiment, whether dryland or irrigated, and for various periods depending on SWC measurement availability at each site. The SWAT soil water was evaluated as follows: the Indiana site was evaluated under dryland conditions using daily soil water observations for one year; the Texas site was evaluated for a ten-year period under irrigated and dryland conditions using weekly soil water observations from four lysimeters; and the Colorado site was evaluated under irrigated conditions for a four-year period. The simulated soil water was evaluated by comparing the model simulations with observed daily and weekly soil water measurements at the three sites. Based on the results, even though all the SWAT models were considered to perform as good models following calibration (streamflow, ET, etc.), the soil water simulations were unacceptable for the defined soil profile and for individual layers at the three sites. Deeper soil layers had observations greater than field capacity values, indicating poor soil parameterization. The dryland model had greater water content than the irrigated model, contradicting the soil water measurements. This greater soil water simulation with the dryland model is a result of SWAT model uncertainties with ET reduction under dryland conditions due to water stress. This study indicated that soil water estimation using the default SWAT soil water equations has many sources of uncertainties. Two apparent sources resulted in the SWAT model’s poor performance: (1) SWAT soil water routines that do not fully represent soil water moving between layers to meet plant demand and (2) uncertainty in soil parameterization. Keywords: Hydrologic modeling, Soil moisture, Soil moisture sensor, Soil water, Soil and Water Assessment Tool.

A SWAT model equipped with an alternative auto-irrigation algorithm was used to evaluate the effects of planting date on hybrid corn yield and seasonal water use in the Texas High Plains. Research field data from the USDA-ARS Conservation and Production Research Laboratory at Bushland, TX and the Texas A&M AgriLife North Plains Research Field near Etter, TX were used for model calibration. A long-term weather data set was used to simulate continuous corn using five planting dates (15-April, 1-May, 15-May, 1-June, and 15-June) for both long- and short-season corn hybrids. Results suggested that delayed planting resulted in a reduction of seasonal water use for both hybrids. Reductions in seasonal irrigation between the 15-April and 15-June were 28 % and 31 % for long- and short-season hybrids, respectively, using an application depth of 25.4 mm. Corresponding reductions in yield were considerably less at 8.9 and 8.8 % for long- and short-season hybrids. Reduced irrigation was attributed to decreased temperature stress and lower evapotranspiration of the later growing season. However, simulation of long season corn for the 15-June planting resulted in late season cold temperature stress. Further analysis of 19.1 mm and 31.8 mm irrigation depths revealed the latter resulted in an average of 4.4 and 4.7 % reductions in seasonal irrigation for long- and short-season hybrids, respectively. Results from this assessment study suggest the delayed planting of corn may result in decreased irrigation while maintaining profitable yields, potentially reducing withdrawals from the Ogallala Aquifer in the Texas High Plains region.

Modeling the effects of climate change on hydrology and crop yield provides opportunities for choosing appropriate crops for adapting to climate change. In this study, climate change impacts on irrigated corn and sorghum, dryland (rainfed) sorghum, and continuous fallow in the Northern High Plains of Texas were evaluated using an improved Soil and Water Assessment Tool (SWAT) model equipped with management allowed depletion (MAD) irrigation scheduling. Projected climate data (2020–2099) from the Coupled Model Intercomparison Project Phase 5 (CMIP 5) of 19 General Circulation Models (GCMs) were used. Climate data were divided into four 20-year periods of near future (2020–2039), middle (2040–2059), late (2060–2079), and end (2080–2099) of the 21st century under two Representative Concentration Pathway (RCP) emission scenarios (RCP 4.5 and RCP 8.5). For irrigated corn, median annual crop evapotranspiration (ET) and irrigation decreased by 8%–25% and 15%–42%, respectively, under the climate change scenarios compared to the historical period (2001–2010). The median yield was reduced by 3%–22% with exponentially decreases in the latter half of the 21st century. For sorghum, the reduction of median annual crop ET ranged from 6%–27%. However, the decline in the median annual irrigation was within 15%, except for the 2060–2079 and 2080–2099 periods under RCP 8.5 scenarios with 30% and 49% reductions in median annual irrigation. The median irrigated sorghum yield declined by 6%–42%. The median annual crop ET of dryland sorghum decreased by 10%–16%. The reduction in median yield was within 10% of the historical dryland sorghum yield. The decrease in median annual evaporation varied from 15%–23% under future continuous fallow conditions. The elevated CO2 level of future climate scenarios was the primary factor for the decrease in the ET and irrigation. The reduction in future crop yield was mainly attributed to the shortening of the maturity period caused by increased future temperature.

High quality estimates of evapotranspiration (ET) are needed for water-limited agriculture where irrigation is necessary to efficiently grow crops. Eddy covariance (EC) systems are observational tools used to measure water and heat fluxes, but a tendency to underestimate fluxes causes a lack of surface energy balance closure. Surface energy budgets are useful for verifying ET estimates, especially in advective conditions that affect energy partitioning. Here, we explored the effect of heat storage and advective conditions on surface energy balance closure for the 2014 and 2015 growing seasons in Bushland, Texas. Storage components were estimated near the center of an irrigated sorghum field using an array of soil and surface layer measurements. A comparison of EC estimated turbulent fluxes and available energy consisting of net radiation, soil heat flux, and storage was used to identify advective conditions and assess surface energy balance closure. Our results indicated daytime mean heat storage of approximately 40 W m−2 with a diurnal pattern featuring a midday peak that exceeded the evening minimum in magnitude, mostly reflective of soil heat storage but also affected by air, water, and biomass heat storage during the morning hours and photosynthesis storage during midday hours. Daytime advective conditions were associated with higher heat storage frequently under stable atmospheric conditions. The surface energy balance was more closed in 2014 than 2015; the 2014 energy balance exhibited a hysteretic pattern with a surplus of turbulent energy in the afternoon, whereas systematic underestimation was common in 2015. This finding is related to a larger proportion of data in 2014 being advective, characterized by a lower Bowen ratio and greater ET, particularly during the late afternoon. Regardless of advection classification, heat storage was demonstrated to be important for daytime energy balance in this irrigated cropland.

Greater than 80% of the irrigated area in the Southern High Plains is served by center-pivot irrigation, but the area served by subsurface drip irrigation (SDI) is increasing due to several factors including declining well yields and improved yields and crop water productivity (CWP), particularly for cotton. Not as well established is the degree to which the reduced soil water evaporation (E) in SDI systems affects the soil water balance, water available to the crop, and overall water savings. Grain corn ( L.) and sorghum ( L. Moench) were grown on four large weighing lysimeters at Bushland, Texas, in 2013 (corn), 2014 and 2015 (sorghum), and 2016 (corn). Evapotranspiration (ET) was measured using the lysimeters and using a neutron probe in the surrounding fields. Two of the lysimeters and surrounding fields were irrigated with SDI, and the other two were irrigated with mid-elevation spray application (MESA). The lysimeter-measured evaporative losses were 149 to 151 mm greater from sprinkler-irrigated corn fields than from SDI fields. When growing sorghum, the lysimeter-measured evaporative losses were 44 to 71 mm greater from sprinkler-irrigated fields than from SDI fields. The differences were affected by plant height and became smaller when plant height reached the height of the spray nozzles, indicating that the use of LEPA or LESA nozzles could decrease the evaporative losses from sprinkler-irrigated fields in this region with its high evaporative demand. Annual weather patterns also influenced the differences in evaporative loss, with increased differences in dry years. SDI reduced overall corn water use by 13% to 15%, as determined by neutron probe, while either not significantly affecting yield (2016) or increasing yield by up to 19% (2013) and increasing CWP by 37% (2013) to 13% (2016) as compared with MESA full irrigation. However, sorghum yield decreased by 15% and CWP decreased by 14% in 2014 when using SDI compared with MESA full irrigation due to an overly wet soil profile in the SDI fields and deep percolation that likely caused nutrient losses. In 2015, there were no significant sorghum yield differences between irrigation methods. Sorghum CWP was significantly greater (by 14%) in one SDI field in 2015 compared with MESA fully irrigated sorghum. Overall, sorghum CWP increased by 8% for SDI compared with MESA full irrigation in 2015. These results indicate that SDI will be successful for corn production in the Texas High Plains, but SDI is unlikely to benefit sorghum production. Keywords: Corn, Crop water productivity, Evaporative loss, Evapotranspiration, Irrigation application method, Sorghum, Water use efficiency, Weighing lysimeter.

Capturing precipitation as soil moisture is essential for successful dryland crop production, especially in semi-arid regions. Runoff is a loss of precipitation that does not result in increased soil moisture. Therefore, understanding the factors affecting runoff from cropped fields is important to successful dryland farming. Runoff frequency and volume were assessed using data from 1990 to 2009 from a long-term wheat-sorghum-fallow rotation near Bushland, Texas (USA), an area with an annual precipitation of ~500 mm and pan evaporation rate of over 2,000 mm. The likelihood that a rainfall event generated runoff increased with increasing rainfall volume such that all storms in excess of 50 mm yielded runoff. Runoff volume also increased with storm intensity. Rainfall in the preceding week was positively related to runoff volume. Runoff tended to be greater in no-till plots as compared to stubble mulched plots. During El Nino phase of the El Nino-Southern Oscillation the number of precipitations during the months of December through February tended to be higher. During La Nina phase of the El Nino-Southern Oscillation the number of precipitations during the months of November through January, respectively, tended to be lower. There also was a tendency for rainfall events during the El Nino phase to be of greater volume. However, the data did not support the hypothesis that greater winter rainfall with the El Nino event was associated with greater runoff. Therefore, greater rainfall during the El Nino phases should have been available for crop production. These results are discussed with regarding crop management practices for the Texas High Plains.

Over semi-arid agricultural regions such as the U.S. Southern High Plains (SHP) producers of dryland crops need to know which management practices increase yields and decrease production risk. Here, a modeling approach is used to explore management options (MO) that increase dryland cotton yields and estimate those practice's yield risk effects under current SHP climate conditions. To simulate current dryland yield variability, dense distributions of lint yield outcomes were generated using the CROPGRO-Cotton crop model driven by weather inputs from 21 SHP weather stations during 2005–2016. Management effects were explored by repeating simulations over 32 MOs defined by 4 planting dates, 4 planting densities, and applying or not applying nitrogen. Both earlier planting date and decreased plant density increased median simulated yields, with earlier planting having the greatest positive yield effects. The simulated MO that produced the highest median lint yields planted on the earliest planting date (May 15), at the lowest density (3 plants m−1), and applied no nitrogen. Recent SHP field studies generally confirm the earlier planting date effect, but suggest insignificant yield effects for different seeding rates. Even so, negligible yield effects and lower input costs favor lower seeding densities from a profit standpoint. These crop simulations demonstrate a modeling-based method for climate-related agricultural risk management, and suggest mid-May planting dates and low plant densities as part of management practices that increase yields and profits in dryland SHP cotton production.

Maize (Zea mays L.) is an important cereal crop grown worldwide. With the increase in human food demand but limited land and water resources, precise spatially explicit knowledge about the maize production capacity through agricultural management practices (e.g., using recommended nutrient and water inputs, RNWI, by local agronomists) is essential to guide the future policy, research, development, and investment. Here, we used a well-validated crop model (APSIM-Maize) for 1981–2010 combined with actual climatic and soil data to estimate maize yield improvements under RNWI in three main cropping regions in China (the North China Spring Maize Region, NCS; the Huanghuaihai Summer Maize region, HS; and the Southwest China Mountain Maize Region, SCM). Compared with the county-level maize actual yield in the three main cropping regions, the average maize yield could be increased by 33 % (4 Mg ha−1) through RNWI, while the improvements in the coefficients of variation (CVs) of grain yield and reliable grain production (RGP) were 0.11 and 32 % (69 million Mg), respectively. Except for RNWI, the average yield, CVs of yield, and RGP could still be increased by 28 % (3 Mg ha−1), 0.10, and 36 % (80 million Mg) through other management and technologies (OMT). Further analysis in four types of yield level-stability zones (high-stable, low-stable, high-unstable, and low-unstable zones) showed that greater contributions of using RNWI and OMT to improve maize grain yield, yield stability, and RGP were found in zones with low/unstable yield across the three regions. The findings highlighted the focus on increasing maize yield in low/unstable-yield zones could provide a greater return.

Although standardized evapotranspiration (ET) methods have been available for decades, they are commonly misunderstood, miscommunicated, and misused, especially within the agroecosystem modeling community. Some models misapply or misname standardized ET methods unbeknownst to users, and there is confusion in communication between applied ET practitioners and agroecosystem modelers. By highlighting some of these issues, we demonstrate and suggest the need for improved and consistent communication and application of standardized ET methodology.

The semi-arid Texas High Plains has experienced decreasing well capacities due to decades of pumping with negligible recharge. Improvements in irrigation system efficiency and advances in drought tolerant crop varieties have improved water use efficiency. However, a gradual transition of irrigated lands to dryland management systems is expected for many areas in the region within the coming decades. Producers may elect to allocate more acreage to dryland crops such as winter wheat during this transition. Precipitation forecasting approaches may aid producers when considering planting acreage and additional inputs. Classifications of the El Nino Southern Oscillation (ENSO) periods have been associated with seasonal fluctuations of precipitation in North America. In this study, the Oceanic Nino Index (ONI), used to classify El Nino and La Nina phases of the ENSO, was evaluated for predicting growing season precipitation for winter wheat using measured data from the USDA-ARS Conservation and Production Laboratory (CPRL) for 1950–2015. Although not statistically significant, probability exceedance plots revealed a tenable correlation between precipitation and ONI classifications. Corresponding winter wheat yields were also simulated using the Soil and Water Assessment Tool (SWAT) model using both continuous and single-year scenarios designed to determine the effects of antecedent soil water resulting from inter-seasonal precipitation and residual soil water. The sporadic and uncertain nature of precipitation appeared to outweigh the ONI signal for prediction of precipitation for the winter wheat growing season in the Texas High Plains. However, simulated minimum yield values associated with El Nino phase classifications were nearly three times those of La Nina and phase neutral values, suggesting that ONI-based predictions of El Nino conditions have a lower probability of reduced yields. This finding in part supports the use of the ONI as a decision support tool for the planting of increased acreage and/or additional inputs for winter wheat crops in the Texas Panhandle. Additional analysis using long term precipitation data from multiple sites in the region would provide a more comprehensive determination of the efficacy of the ONI as a management tool.

Evapotranspiration (ET), is a major component of the hydrologic budget and therefore it requires accurate estimation. The Agricultural Policy/Environmental eXtender (APEX), a hydrologic and water quality model developed for evaluating the effect of agricultural production management practices on the environment. It has five different methods to simulate ET. The objectives of this study were to: determine the impact of using different ET methods in APEX on sensitive ET parameters in semi-arid environments, determine reasonable range of values for sensitive parameters, and compare performance of these methods using multiple criteria. ET and crop yield data measured in the Lysimeter fields located in the USDA-ARS Conservation and Production Research Laboratory, Bushland, Texas were used to calibrate and validate the model. Results indicated that selection of statistical model performance measure and ET method affects the number of sensitive parameters and parameter rank. The number of sensitive parameters remained relatively stable among ET simulation methods but there was large variability in parameter sensitivity ranks. It is also important that users carefully choose appropriate statistical measure to use depending on the goals of their study. With the exception of maximum rainfall intercept, exponent coefficient rainfall, SCS index coefficient, rain intercept coefficient, and root growth soil strength, all methods had similar ranges of values for the sensitive parameters. In general there were no large differences in the performance of ET methods. However, Penman-Monteith method simulated ET relatively better than the other methods, which may be explained by the fact that it is a physically-based method with many weather variables whose data was available for the study area. However, the study findings indicate that the other ET methods can provide satisfactory results in regions with limited weather data.

The NTT (Nutrient Tracking Tool) was designed to provide an opportunity for all users, including producers, to run complex simulation models, such as APEX (Agricultural Policy Environmental eXtender), with the associated required databases. The APEX model currently nested within NTT provides estimates of the changes in nitrogen (N), phosphorus (P), and sediment losses that are associated with management practices specified by the user. Five methods (Penman-Monteith, Penman, Priestley-Taylor, Hargreaves-Samani, and Baier-Robertson) for determining potential evapotranspiration (PET) are available as inputs for estimating actual ET. This study was conducted to evaluate the accuracy of the ET values obtained from the five PET equations currently available in APEX using both onsite measured climate data and data from the NTT standard databases. The mean daily, monthly, and annual ET values predicted by each of the equations in APEX for a lysimeter field at the USDA-ARS Conservation and Production Research Laboratory at Bushland, Texas, was compared to values measured for the 2001-2010 period. APEX generally underestimated ET with all PET methods (mostly during growing seasons) at both the daily and monthly levels but overpredicted for years when cotton was grown as the major cash crop due to overprediction of leaf area index during the senescing stage for cotton. The underprediction of ET in growing seasons was possibly from underprediction of rainfall due to estimation of rainfall for missing data. Overall, APEX was able to adequately (R2 = 0.82 and NSE = 0.80) predict mean monthly ET for major crops grown in the semi-arid Texas High Plains region. These results should reinforce confidence in APEX’s ability to simulate ET accurately for fully irrigated farms. ET predictions with the Hargreaves-Samani and Priestley-Taylor methods, which require limited data compared to the Penman and Penman-Monteith methods, were similar (p > 0.05, one-way ANOVA), with mean errors within 8.7% for measured weather data and 12.6% for NTT-generated weather data for both methods. This is encouraging because of the limited availability of measured climate data for the majority of locations in the world, including the U.S. Keywords: APEX, Evapotranspiration (ET), Irrigation, Lysimeters, NTT, Semiarid regions.

Accurate quantification and management of crop evapotranspiration (ET) are critical to optimizing crop water productivity for both dryland and irrigated agriculture, especially in the semiarid regions of the world. In this study, four weighing lysimeters in Bushland, Texas, were planted to maize in 1994 with two fully irrigated and two non-irrigated for measuring crop ET. The Root Zone Water Quality Model (RZWQM2) was used to evaluate soil water balance and crop production with potential evapotranspiration (PET) estimated from either the Shuttleworth-Wallace method (PTSW) or the ASCE standardized alfalfa reference ET multiplied by crop coefficients (PTASCE). As a result, two water stress factors were defined from actual transpiration (AT) and were tested in the model against the lysimeter data, i.e., AT/PTSW and AT/PTASCE. For both water stress factors, the simulated daily ET values were reasonably close to the measured values, with underestimated ET during mid-growing stage in both non-irrigated lysimeters. Root mean squared deviations (RMSDs) and relative RMSDs (RMSD/observed mean) values for leaf area index, biomass, soil water content, and daily ET were within simulation errors reported earlier in the literature. For example, the RMSDs of simulated daily ET were less than 1.52 mm for all irrigated and non-irrigated lysimeters. Overall, ET was simulated within 3% of the measured data for both fully irrigated lysimeters and undersimulated by less than 11% using both stress factors for the non-irrigated lysimeters. Our results suggest that both methods are promising for simulating crop production and ET under irrigated conditions, but the methods need to be improved for dryland and non-irrigated conditions. Keywords: ET, RZWQM modeling, Stress factor, Weighing lysimeter.

The SWAT model equipped with an improved auto-irrigation function was used to assess the impacts of cultivation practices on irrigated and dryland cotton yield and water conservation in the Texas Panhandle. Results showed the largest irrigation depth led to reductions in irrigation and crop evapotranspiration (ETc) with slightly increased cotton yields compared to the baseline scenarios under different hydroclimatic regimes. However, soil water content and surface runoff values were increased when using the largest irrigation depth. The opposite results were observed for the small irrigation depth. Early planting of cotton resulted in decreased irrigation and ETc, and increased cotton yields under both irrigated and dryland conditions, particularly in normal and wet years. By contrast, the late planting scenarios indicated the opposite for those variables. Simulated hydrologic variables were relatively stable using various maturity cultivars. Nevertheless, greater than 10% reductions in irrigated cotton yield under diverse hydroclimatic years and dryland yields during normal and wet years were identified in the long-season cotton. The opposite was determined for the short-season cotton. These outcomes suggest that a larger irrigation depth, earlier planting date, and short-season cultivar are promising cultivation practices for improving cotton yield and water conservation in the Texas Panhandle.

Simulating the impacts of future climate change on water footprints and crop production allows for selecting alternative crops for mitigating climate change effects. In this study, climate change impacts on irrigated grain corn, grain sorghum, winter wheat, and dryland (rainfed) winter wheat in the Palo Duro watershed of the Texas High Plains were assessed using an improved Soil and Water Assessment Tool (SWAT) model with an enhanced irrigation representation of management allowed depletion (MAD) irrigation scheduling. Climate change analyses in this study used the Coupled Model Intercomparison Project Phase 5 (CMIP5) climate projections of 11 General Circulation Models (GCMs) under four Representative Concentration Pathway (RCP) emission scenarios of RCP2.6, 4.5, 6.0, and 8.5 during two 30-year periods of the middle (2040-2069) and end (2070-2099) of the 21st century to compare to a baseline period of 1970-1999. For the irrigated summer crops of corn and sorghum, all 11 GCMs predicted the reductions of future irrigation, crop evapotranspiration (ETc), and yields compared to the baseline period. According to an ensemble of 11 GCMs, the simulated reductions in average annual irrigation, ETc, and yield for the irrigated corn were 63%, 34%, and 13%, respectively, at the end of the 21st century under the severe emission scenario of RCP8.5. Those values were 80%, 34%, and 34% under the irrigated sorghum land use. As for the irrigated winter wheat, the decreases in future irrigation and ETc were also identified in all GCMs relative to the baseline period. However, irrigated wheat yields were increased in the future climate. The changes in dryland wheat ETc were consistent with the rainfall variation under all climate change scenarios. Generally, the future climate could benefit the dryland wheat yields. A large uncertainty was found for the surface runoff simulations under both irrigated and dryland wheat according to various GCMs.

Water scarcity due to drought and groundwater depletion has led to increased interest in deficit irrigation strategies that reduce irrigation requirements while maintaining profitable yields. This has resulted in an increase in the number of modeling studies aimed at evaluating crop response to limited irrigation strategies. However, the ability of widely used crop simulation models to accurately represent responses to limited irrigation has not been thoroughly evaluated. The primary objective of this study was to determine the efficacy of DSSAT-CERES-Maize (ver. 4.6.1.0) to simulate leaf area index (LAI), crop evapotranspiration (ET), and yield response to full (100%) and limited (75% and 50%) irrigation regimes for two corn varieties. Comparisons of simulated and measured data from full and limited irrigation treatments of` two drought-tolerant corn hybrids (DuPont Pioneer AQUAmax P1151HR and Pioneer 33D49) grown in the Texas Panhandle in 2013 and 2014 were evaluated. Simulated in-season daily crop ET values for P1151HR grown in 2013 were also compared to those measured by precision large weighing lysimeters at Bushland, Texas. Additionally, a comparison of simulated and measured soil water content (SWC) within the root zone was performed for P1151HR grown in 2013. Simulated LAI for fully irrigated treatments approximated measured values reasonably well, although manipulation of plant genetic parameters failed to match measured LAI during the period between maximum LAI and the beginning of crop senescence in the 50% irrigation treatments. Similarly, simulated yield values approximated measured values for the fully irrigated treatments, while considerable overestimation of yield occurred in the limited irrigation treatments for both varieties. However, consistent overestimation of both LAI and yield for the limited irrigation treatments suggests a functional relationship between LAI and yield. Further, DSSAT overestimated crop ET by 16% for fully irrigated P1151HR and by 40% for limited irrigation treatments in 2013 as compared to measured lysimeter values. Corresponding underestimations of SWC were also observed in neutron probe measurements for both treatments. Overestimation of ET and yield and corresponding underestimation of SWC in limited irrigation treatments were mainly due to overestimation of LAI in those treatments, indicating a potential deficiency in the water stress algorithms. Additional comparisons of agronomic and lysimeter-based water balance data are needed to corroborate the findings of this study. Further investigations into the calculation of reference evapotranspiration (ETo), crop coefficients, and water stress functions in DSSAT are needed in order to provide suggestions for model improvement. Keywords: TCERES-Maize, Crop modeling, DSSAT, Evapotranspiration, Limited irrigation, Lysimeters, Maize, Semi-arid, Sprinkler irrigation, Weighing lysimeters.

Evapotranspiration (ET) is an important component in the water budget and used extensively in water resources management such as water planning and irrigation scheduling. In semi-arid regions, irrigation is used to supplement limited and erratic growing season rainfall to meet crop water demand. Although lysimetery is considered the most accurate method for crop water use measurements, high-precision weighing lysimeters are expensive to build and operate. Alternatively, other measurement systems such as eddy covariance (EC) are being used to estimate crop water use. However, due to numerous explicit and implicit assumptions in the EC method, an energy balance closure problem is widely acknowledged. In this study, three EC systems were installed in a field containing a large weighing lysimeter at heights of 2.5, 4.5, and 8.5 m. Sensible heat flux (H) and ET from each EC system were evaluated against the lysimeter. Energy balance closure ranged from 64% to 67% for the three sensor heights. Results showed that all three EC systems underestimated H and consequently overestimated ET, however, the underestimation of H was greater in magnitude than the overestimation of ET. Analysis showed accuracy of ET was greater than energy balance closure with error rates of 20%&ndash, 30% for half-hourly values. Further analysis of error rates throughout the growing season showed that energy balance closure and ET accuracy were greatest early in the season and larger error was found after plants reached their maximum height. Therefore, large errors associated with increased biomass may indicate unaccounted-for energy stored in the plant canopy as one source of error. Summing the half-hourly data to a daily time-step drastically reduced error in ET to 10%&ndash, 15%, indicating that EC has potential for use in agricultural water management.

A calibrated SWAT model equipped with an improved auto-irrigation function was used to evaluate the impacts of agricultural production practices on water balances and crop yields of corn, sorghum, and winter wheat for the Palo Duro watershed located in the Texas High Plains (THP). Fourteen scenarios were simulated including alternative irrigation application depths of 12.7 mm and 38.1 mm for irrigated corn, sorghum, and wheat and with different planting dates for irrigated corn, sorghum, wheat, and dryland wheat. Results indicated the greater irrigation depth (38.1 mm) led to reductions in seasonal irrigation requirements and crop evapotranspiration (ETc) when compared to the baseline scenarios using an irrigation depth of 25.4 mm for corn, sorghum, and wheat. However, soil water content, surface runoff, and percolation were increased. The opposite was observed for simulations of the same hydrologic variables but with an irrigation depth of 12.7 mm. Crop yields associated with the alternative irrigation depths were similar to those achieved with the baseline. Delayed planting of corn and sorghum resulted in the decrease of all the studied hydrologic parameters relative to the baseline. By contrast, the early planting scenarios showed the increase in those variables generally. Simulated corn yields were relatively stable, but a 3.7% reduction in irrigated sorghum yield was found with late planting. Notably, the early planting of wheat resulted in a clear increase in both irrigated and dryland yields of 11.2% and 13.5%, respectively. However, the yields of irrigated and dryland wheat were reduced by 28.8% and 2.7%, respectively, for the late planting. These findings suggest the greater irrigation application depth is promising for maintaining crop yields and reducing groundwater use from the Ogallala Aquifer. Also, the late planting of corn may benefit water conservation. Nevertheless, the early planting of wheat might be warranted to enhance yield in the THP.

Accurate quantification of crop evapotranspiration (ET) is critical to optimizing irrigation water productivity, especially, in the semiarid regions of the world where limited rainfall is supplemented by irrigation for crop production. In this context, cropping system models are potential tools for predicting ET or crop water requirements in agriculture across soils and climates and assist in developing decision support tools for irrigation. The objective of this study was to evaluate the accuracy of RZWQM2 simulated ET for fully irrigated silage (2006 and 2007) and grain corn (1990) against measured crop water use and soil evaporation with large weighing lysimeters in the Texas High Plains. An extended Shuttleworth and Wallace method was used to estimate potential crop ET (E and T) demand in RZWQM2. The Nimah and Hanks approach was used for crop water uptake and Richard’s Equation for soil water redistribution modeling. Simulations of biomass, leaf area index, soil water storage, and ET were reasonably close to the measured data. Root Mean Squared Deviation (RMSD) for corn biomass was between 1 and 2.1 MT ha−1, LAI between 0.33 and 0.88, water in the soil between 2 and 2.9 cm for a 190 cm soil profile, and actual daily crop ET between 1.0 to 1.5 mm across the three years of measured data. Arithmetic mean deviation (MD) for ET ranged from −0.10 to 0.40 mm. Fallow soil evaporation before and after corn planting was simulated within MD of −0.03–0.003 mm. The crop coefficients (Kc) calculated with measured ET and the short grass or alfalfa crop reference ET methods varied from year to year. The Kc values obtained by using the simulated ET and alfalfa reference ET were close to Kc values using measured ET, within RMSD of 0.17, and could be used to obtain long-term average Kc values for scheduling irrigation.

The Ogalalla Aquifer is used to supplement insufficient precipitation for agricultural production in the semiarid Texas High Plains. However, decades of pumping combined with minimal recharge has resulted in decreased well capacity in most areas. A calibrated Soil and Water Assessment Tool (SWAT) model was used to compare simulated yields, crop water use, and required irrigation for crop rotations of the region using measured long-term (90 years) historical weather data. Crop rotations included continuous corn and cotton, corn–cotton, sorghum–cotton, cotton–winter wheat, and corn–winter wheat. Results demonstrated that a calibrated SWAT model simulated crop water use and yields well for all listed crops except cotton. The plant growth algorithms in SWAT appear unable to simulate representative cotton yields typical of cotton management in the Texas High Plains. A work-around for a limitation of the auto-irrigate function in SWAT to be suspended during the dormancy period of winter wheat was also used. Summary statistics for crop yield, crop water use, and irrigation were presented for all rotations. Long-term water use of simulations and irrigation probability exceedance statistics are presented for all simulated crops. These data may serve as a decision support tool for producers considering crop rotation strategies.

Irrigation from the Ogallala aquifer is used to supplement insufficient precipitation for agricultural crop production in the semi-arid Texas High Plains. Decreased pumping capacity has compelled many producers to “pre-water” fields to field capacity prior to planting to hedge against pumping limitations later in the season. However, the direct measurement of evaporative losses from preseason irrigation of bare soil is not commonly studied. The quantification of evaporative losses from effective precipitation, or the net amount of water that infiltrates into the soil following a precipitation event, can be used as a surrogate for estimating losses from preseason irrigation. We identified 35 precipitation events that occurred over lysimeter fields under fallow conditions in 2002, 2005, and 2009. Events were categorized into four bins of precipitation magnitude ranging from 3 mm to 35 mm. Subsequent evaporation was measured for a period of up to seven days following rainfall events using large weighing lysimeters at the USDA-ARS Conservation and Production Research Laboratory in Bushland, TX. An exponential decay function was used to characterize bare soil evaporation using maximum cumulative measured evaporation ( E Cmax ), soil water transfer constant ( k ), and cumulative grass reference evapotranspiration (ET Cos ). The wide range of E Cmax values and k values demonstrated the sensitivity of evaporative losses to both antecedent soil water content and evaporative demand. We also present measured average daily evaporation values for a range of evaporative demand regimes for each precipitation bin. From data analyzed in this study, nearly all of the water from precipitation events of 10 mm and less were lost to evaporation within the following day under moderate to high grass reference evapotranspiration (ET os ) conditions. Nearly all water from precipitation events between 20 and 30 mm was lost to evaporation between three to four days following the event under similar evaporative demand. The considerable potential evaporative losses from preseason irrigation call into the question the prudence of the preseason irrigation, particularly for regions with limited groundwater resources.

The Soil Water Assessment Tool (SWAT) is a widely used watershed model for simulating stream flow, overland flow, and sediment, pesticide, and bacterial loading in response to management practices. All SWAT processes are directly dependent on accurate representation of hydrology. Evapotranspiration (ET) is commonly the most significant portion of the hydrologic cycle, especially for irrigated lands in semiarid environments when ET demands are met or exceeded. However, no studies using long-term data to evaluate the SWAT model‘s capacity to estimate daily, monthly, and seasonal ET have been performed. In this study, daily and monthly ET values were simulated using ArcSWAT 2012 for an irrigated lysimeter field at the USDA-ARS Conservation and Production Research Laboratory (CPRL) at Bushland, Texas, and compared to measured ET values from 2001-2010. Crops grown during the study period included cotton, soybean, grain and silage sorghum, sunflower, and corn silage. A one-year warm-up (2000) and equal division of the remaining years were used for the calibration (2001-2005) and validation (2006-2010) periods. SWAT achieved a Nash-Sutcliffe efficiency (NSE) of 0.67 for daily ET during the calibration period, resulting in a “good” performance rating. An NSE of 0.78 resulted in a “very good” rating for the validation period. NSE values for simulated average monthly ET were improved, at 0.77 and 0.85 for the calibration and validation periods, respectively. Analysis of simulated versus measured ET values both during and outside of the growing season revealed better agreement during the former than the latter. The SWAT model generally underestimated ET at both daily and monthly levels but overestimated annual cotton ET due to overestimation of leaf area index during the senescing stage. Overall, SWAT was able to simulate daily and average monthly ET reasonably well for major summer crops grown in the semiarid Texas High Plains. These results should reinforce confidence in the SWAT model‘s capacity to accurately simulate ET in fully irrigated watersheds. However, limitations in accuracy appear to exist for certain crops, such as cotton and sunflower, and particularly under limited irrigation conditions. These deficiencies may be related to issues with the embedded Leaf Area Index (LAI) crop growth model and default plant parameter values in the crop database in SWAT.

In 1987-1989, the first irrigated crops were grown on the four large, precision weighing lysimeters at the USDA-ARS Conservation and Production Research Laboratory at Bushland, Texas, on the Southern High Plains (SHP). Thus began >25-years of full- and deficit-irrigated crop growth, energy and water balance, evapotranspiration (ET), yield, and water use efficiency (WUE) studies of major SHP crops, including alfalfa, corn and sorghum for both grain and forage, cotton, soybean, sunflower, and winter wheat. Alfalfa studies supported development of the ASCE Standardized Reference ET methodology. The lysimeter effort, led by Terry Howell, Sr., co-designed with Lynne Ebling and Thomas Marek and constructed by Arland Schneider, eventually grew to include a separate lysimeter to study short grass ET, again for the ASCE standard, and a 48-lysimeter facility to study soil type effects on crop water uptake, ET, and WUE using monoliths of four soils typical of SHP irrigated soils. The large lysimeters were managed to be representative of sprinkler-irrigated fields so as to develop crop coefficients used for irrigation scheduling by clients of ET networks developed by Texas A&M AgriLife in collaboration with the USDA-ARS. In addition, the lysimeters were used to test and further develop several technologies important to irrigation science, including soil water sensors, eddy covariance and Bowen ratio systems, scintillometers, thermal remote sensing based ET models, and hydrologic and crop simulation models. With the installation of subsurface drip irrigation systems on two of the lysimeter fields in 2013, the Bushland lysimeters are entering a new phase of advanced irrigation method and management studies.

Evapotranspiration (ET) is one of the largest data gaps in water management due to the limited availability of measured evapotranspiration data, and because ET spatial variability is difficult to characterize at various scales. Satellite-based ET estimation has been shown to have great potential for water resource planning and for estimating agricultural water use at field, watershed, and regional scales. Satellites with low spatial resolution, such as NASA&rsquo, s MODIS (Moderate Resolution Imaging Spectroradiometer), and those with higher spatial resolution, such as Landsat (Land Satellite), can potentially be used for irrigation water management purposes and other agricultural applications. The objective of this study is to assess satellite based-ET estimation accuracy using measured ET from large weighing lysimeters. Daily, seven-day running average, monthly, and seasonal satellite-based ET data were compared with corresponding lysimeter ET data. This study was performed at the USDA-ARS Conservation and Production Research Laboratory (CPRL) in Bushland, Texas, USA. The daily time series Landsat ET estimates were characterized as poor for irrigated fields, with a Nash Sutcliff efficiency (NSE) of 0.37, and good for monthly ET, with an NSE of 0.57. For the dryland managed fields, the daily and monthly ET estimates were unacceptable with an NSE of &minus, 1.38 and &minus, 0.19, respectively. There are various reasons for these results, including uncertainties with remotely sensed data due to errors in aerodynamic resistance surface roughness length estimation, surface temperature deviations between irrigated and dryland conditions, poor leaf area estimation in the METRIC model under dryland conditions, extended gap periods between satellite data, and using the linear interpolation method to extrapolate daily ET values between two consecutive scenes (images).

Accurate calculations of evapotranspiration (ET) are highly important for agroecosystem model simulations, and improvement of ET algorithms is an on-going model development goal. The objective of this study was to evaluate and compare six ET methods in the Decision Support System for Agrotechnology Transfer (DSSAT) Cropping System Model (CSM) using agronomic and weighing lysimetry data from cotton field studies at Bushland, Texas. Three options were tested for estimating potential ET as required by the DSSAT-CSM: 1) a Priestley-Taylor method, 2) a Penman-Monteith combination equation estimate of grass reference ET with a DSSAT-specific single crop coefficient equation, and 3) the ASCE Standardized Reference ET Equation combined with a dual crop coefficient method for non-stressed conditions. The latter two reference ET methods were adapted to provide reasonable estimates for DSSAT-required potential ET. Additionally, two methods for calculation of soil water evaporation were tested, including both the original and updated formulations of Ritchie approaches for DSSAT-CSM. The combinations of the three potential ET and two soil water evaporation approaches led to six possible ET simulation options in the model. A computationally-intensive multiobjective optimization method was used to select among model parameterization options and ensure that modeler bias did not influence ET method comparisons. Among 23 agroecosystem metrics that included lysimeter-based ET, various cotton growth variables, and soil water content in multiple soil layers, the original Ritchie soil water evaporation approach performed statistically equivalent to or better than the more recent Ritchie method ( p ⩽ 0.05 ). The default ET method in the model, which involved Priestley-Taylor potential ET with the more recent Ritchie soil water evaporation method, was outperformed by other ET methods for 14 of 23 agroecosystem metrics ( p ⩽ 0.05 ). When the original Ritchie soil water evaporation method was combined with potential ET from the ASCE reference ET and dual crop coefficient method, the model performed statistically equivalent to or better than the other five ET options for all but 1 of 23 agroecosystem metrics ( p ⩽ 0.05 ). Based on three years of cotton data from the Bushland lysimetry fields, a DSSAT-CSM ET approach based on the standardized ET methodologies described by ASCE and FAO-56 combined with the original Ritchie soil water evaporation method provided holistic improvements to model simulations among multiple agroecosystem metrics.

Global sensitivity analysis (GSA) is useful for evaluating the responsiveness of agroecosystem models to input parameter adjustments. Evaluations of model sensitivity for diverse water status conditions and evapotranspiration (ET) algorithms will facilitate better use of models to provide water management recommendations. The objective of this study was to conduct a GSA to identify influential parameters in the Decision Support System for Agrotechnology Transfer (DSSAT) Cropping System Model (CSM), specifically using the CROPGRO-Cotton module with data from cotton field studies conducted in 2000, 2001, and 2008 at Bushland, Texas. The field studies tested fully-irrigated, deficit-irrigated, and dryland cotton production in a semi-arid environment. Using high performance computing resources, a GSA was conducted to evaluate the sensitivity of 24 model outputs with respect to 37 model input parameters. The GSA was conducted for six different ET methodologies available in the model. With first-order sensitivity indices 0.05 , nearly half of the tested input parameters did not influence any model output for any of the tested simulation scenarios. Among the parameters with first-order sensitivity indices > 0.05 , eleven were cultivar parameters that controlled crop development and growth, and five were soil parameters that specified initial soil water conditions, soil water limits, drainage rate, and root growth characteristics. The influences of another soil parameter and one ET parameter were relevant only for the ET methods that required them. Large differences in sensitivity indices were found based on the choice between two soil water evaporation methods. In addition to providing insights for other applications of this model, the results specifically informed further efforts to evaluate the model using measured data from the Bushland cotton studies to compare performance among the six ET methods, as reported in a companion paper.

In agriculture, evapotranspiration (ET) is a major consumptive use of irrigation water and precipitation on cropland. Global interest in sustainable management of limited freshwater supplies to meet increased food demand has resulted in increased reporting of ET measurement and modeling methods in the literature. Direct measurements of ET by large weighing lysimeters are commonly used to test other ET measurement and estimation methods. Numerous studies have emphasized the importance of proper lysimeter design and management for accurate ET measurement. However, equally important and noticeably absent from the literature are guidelines for data collection, processing, analysis, and quality assurance and quality control (QA/QC) measures. Omission and/or inadequate attention to these processes can have significant impacts on the accuracy of ET derived from large weighing lysimeters. Improper processing of rainfall, irrigation, snowfall, dew and frost accumulation, wind, and management events can also lead to substantial errors. In this study, we present data processing strategies and techniques for ET datasets measured using large weighing lysimeters. Measurements from two large lysimeters built and managed by the USDA-ARS Conservation and Production Laboratory (CPRL) in Bushland, Texas, were used for this purpose. Examples used in the study demonstrate that indiscriminate application of smoothing functions, misidentification, and misinterpretation of changes in lysimeter mass can lead to significant errors and erroneous conclusions. Comprehensive record keeping is paramount in documenting lysimeter status and operations. Errors associated with data processing are dataset specific, corresponding to the magnitude, duration, and frequency of occurrences of the aforementioned events. Understanding and prudent application of the presented techniques and QA/QC procedures can help to minimize errors in processing lysimeter datasets.

Greenhouse gases (GHG) have been implicated in global warming and climate change. While life cycle assessments (LCA) and GHG studies have been conducted for numerous agricultural commodities, there has been little effort to estimate GHG (CO2, N2O, and CH4) from beef finishing systems of the Southern High Plains (SHP) region, which produces approximately 30% of the United States beef. The objective of this research was to quantify the carbon footprint of five beef-finishing systems using a dynamic, systems-based model that calculated CO2e emissions attributable to both animal gain and manure management. The systems consisted of native grass pasture (NGP, System 1); native grass pasture with feedyard finishing (NGP-FY, System 2); wheat pasture with feedyard finishing (WP-FY, System 3); feedyard-only (FY, System 4); and native grass pasture, wheat pasture, and feedyard finishing (NGP-WP-FY, System 5). Although rarely used, the NGP was included as a baseline. Variables in the model and associated management decisions were based on feed type, nutritional content, feed source, and hauling distance. The starting point of the model was a weaned steer (250 kg) and the endpoint was a steer which would grade “choice” (28% body fat) or 30 months in age, whichever came first. Overall CO2e kg−1 gain decreased when cattle were fed high-quality diets and were intensively managed for production in the shortest time possible. The FY produced the desired carcass in the shortest time with the lowest cumulative emissions. The FY also had the highest average daily gain, lowest dry matter and water intake, as well as manure production. Net GHG emissions from FY were 4.84 kg CO2e kg−1 gain (1799 kg CO2e animal−1). Net GHG emissions from NGP-FY, WP-FY, NGP-WP-FY, and NGP were 1.62, 1.81, 2.08, and 3.69 times that of FY, respectively. These results suggest that intensive feeding and management of beef cattle in the FY system result in the lowest overall CO2e emissions to produce a mature steer. Consequently, feeding systems that include native grass and wheat pasture have proportionately larger amounts of CO2e emissions.