Enhancement of agricultural systems models for limited irrigated cropping systems research

2013 Spring.Includes bibliographical references.To view the abstract, please see the full text of the document

Post-processed data and graphical tools for a CONUS-wide eddy flux evapotranspiration dataset

Large sample datasets of in situ evapotranspiration (ET) measurements with well documented data provenance and quality assurance are critical for water management and many fields of earth science research. We present a post-processed ET oriented dataset at daily and monthly timesteps, from 161 stations, including 148 eddy covariance flux towers, that were chosen based on their data quality from nearly 350 stations across the contiguous United States. In addition to ET, the data includes energy and heat fluxes, meteorological measurements, and reference ET downloaded from grid- MET for each flux station. Data processing techniques were conducted in a reproducible manner using open-source soft- ware. Most data initially came from the public AmeriFlux network, however, several different networks (e.g., the USDA- Agricultural Research Service) and university partners pro- vided data that was not yet public. Initial half-hourly energy balance data were gap-filled and aggregated to daily frequency, and turbulent fluxes were corrected for energy balance closure error using the FLUXNET2015/ONEFlux energy balance ratio approach. Metadata, diagnostics of energy balance, and interactive graphs of time series data are included for each station. Although the dataset was developed primarily to benchmark satellite-based remote sensing ET models of the OpenET initiative, there are many other potential uses, such as validation for a range of regional hydrologic and atmospheric models

Post-processed data and graphical tools for a CONUS-wide eddy flux evapotranspiration dataset

Large sample datasets of in situ evapotranspiration (ET) measurements with well documented data provenance and quality assurance are critical for water management and many fields of earth science research. We present a post-processed ET oriented dataset at daily and monthly timesteps, from 161 stations, including 148 eddy covariance flux towers, that were chosen based on their data quality from nearly 350 stations across the contiguous United States. In addition to ET, the data includes energy and heat fluxes, meteorological measurements, and reference ET downloaded from gridMET for each flux station. Data processing techniques were conducted in a reproducible manner using open-source software. Most data initially came from the public AmeriFlux network, however, several different networks (e.g., the USDA-Agricultural Research Service) and university partners provided data that was not yet public. Initial half-hourly energy balance data were gap-filled and aggregated to daily frequency, and turbulent fluxes were corrected for energy balance closure error using the FLUXNET2015/ONEFlux energy balance ratio approach. Metadata, diagnostics of energy balance, and interactive graphs of time series data are included for each station. Although the dataset was developed primarily to benchmark satellite-based remote sensing ET models of the OpenET initiative, there are many other potential uses, such as validation for a range of regional hydrologic and atmospheric models

Do maize crop models catch the impact of future [CO2] on maize yield and water use?

Do maize crop models catch the impact of future [CO2] on maize yield and water use?. iCROPM2016 International Crop Modelling Symposiu

The AgMIP Coordinated Climate-Crop Modeling Project (C3MP): Methods and Protocols

Climate change is expected to alter a multitude of factors important to agricultural systems, including pests, diseases, weeds, extreme climate events, water resources, soil degradation, and socio-economic pressures. Changes to carbon dioxide concentration ([CO2]), temperature, andwater (CTW) will be the primary drivers of change in crop growth and agricultural systems. Therefore, establishing the CTW-change sensitivity of crop yields is an urgent research need and warrants diverse methods of investigation. Crop models provide a biophysical, process-based tool to investigate crop responses across varying environmental conditions and farm management techniques, and have been applied in climate impact assessment by using a variety of methods (White et al., 2011, and references therein). However, there is a significant amount of divergence between various crop models’ responses to CTW changes (R¨otter et al., 2011). While the application of a site-based crop model is relatively simple, the coordination of such agricultural impact assessments on larger scales requires consistent and timely contributions from a large number of crop modelers, each time a new global climate model (GCM) scenario or downscaling technique is created. A coordinated, global effort to rapidly examine CTW sensitivity across multiple crops, crop models, and sites is needed to aid model development and enhance the assessment of climate impacts (Deser et al., 2012)..

Comparing CSM-CROPGRO and APSIM-OzCot Simulations for Cotton Production and Eddy Covariance-Based Evapotranspiration in Mississippi

Optimizing irrigation water use efficiency (WUE) is critical to reduce the dependency of irrigated cotton (Gossypium spp.) production on depleting aquifers. Cropping system models can integrate and synthesize data collected through experiments in the past and simulate management changes for enhancing WUE in agriculture. This study evaluated the simulation of cotton growth and evapotranspiration (ET) in a grower’s field using the CSM-CROPGRO-cotton module within the Decision Support System for Agrotechnology Transfer (DSSAT) and APSIM (Agricultural Production Systems simulator)-OzCot during 2017–2018 growing seasons. Crop ET was quantified using the eddy covariance (EC) method. Data collected in 2017 was used in calibrating the models and in 2018 validating. Over two cropping seasons, the simulated seedling emergence, flowering, and maturity dates were varied less than a week from measured for both models. Simulated leaf area index (LAI) varied from measured with the relative root mean squared errors (RRMSE) ranging between 20.6% to 38.7%. Daily ET deviated from EC estimates with root mean square errors (RMSEs) of 1.90 mm and 2.03 mm (RRMSEs of 63.1% and 54.8%) for the DSSAT and 1.95 mm and 2.17 mm (RRMSEs of 64.7% and 58.8%) for APSIM, during 2017 and 2018, respectively. Model performance varied with growing seasons, indicating improving ET simulation processes and long-term calibrations and validations are necessary for adapting the models for decision support in optimizing WUE in cotton cropping systems

Assessing the Effects of Agronomic Management Practices on Soybean (<i>Glycine max</i> L.) Post-Grain Harvest Residue Quality in the Lower Mississippi Delta

Livestock producers often resort to either baling or grazing of crop residues due to high hay prices and reduced supply of other forages and silage in the markets. Soil-water-crop management practices can affect residue nutrient qualities for its use as cattle feedstock. A two-year study (2018–2019) was conducted to investigate the effects of irrigation (AI, all row-irrigation; ARI, alternate row irrigation; and RF, rainfed) and planting pattern, PP (SR, single row; and TR, twin-row) on soybean (maturity group IV cv. 31RY45 Dyna-Gro) post-grain harvest residue quality such as crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), acid detergent lignin (ADL), net energy for maintenance (NEM), net energy for gain (NEG), net energy for lactation (NEL), total digestible nutrients (TDN), and relative feed value (RFV). Irrigation has a significant effect on CP, ADF, NDF, and TDN while PP affected only NDF. All the above parameters were significantly affected except NEM by the contrasting climate conditions, particularly during July through August coinciding with early crop reproductive stages and maturity. The RFV values ranged from 70.4 to 81.6 and this lower range was attributable to nutrient translocation to seeds and higher lignification during plant senescence towards the grain filling stage of the crop as good quality hay records over 120 RFV. These results indicate that both irrigation and weather during soybean seed development can alter post-grain harvest residue quality parameters, thereby playing critical roles in its RFV

Growing season variability in carbon dioxide exchange of irrigated and rainfed soybean in the southern United States

Measurement of carbon dynamics of soybean (Glycine max L.) ecosystems outside Corn Belt of the United States (U.S.) is lacking. This study examines the seasonal variability of net ecosystem CO2 exchange (NEE) and its components (gross primary production, GPP and ecosystem respiration, ER), and relevant controlling environmental factors between rainfed (El Reno, Oklahoma) and irrigated (Stoneville, Mississippi) soybean fields in the southern U.S. during the 2016 growing season. Grain yield was about 1.6 t ha−1 for rainfed soybean and 4.9 t ha−1 for irrigated soybean. The magnitudes of diurnal NEE (~2-weeks average) reached seasonal peak values of −23.18 and−34.78 μmol m−2 s−1 in rainfed and irrigated soybean, respectively, approximately two-months after planting (i.e., during peak growth). Similar thresholds of air temperature (Ta, slightly over 30 °C) and vapor pressure deficit (VPD, ~2.5 kPa) for NEE were observed at both sites. Daily (7-day average) NEE, GPP, and ER reached seasonal peak values of −4.55, 13.54, and 9.95 g C m−2 d−1 in rainfed soybean and −7.48, 18.13, and 14.93 g Cm−2 d−1 in irrigated soybean, respectively. The growing season (DOY 132–243) NEE, GPP, and ER totals were−54, 783, and 729 g C m−2, respectively, in rainfed soybean. Similarly, cumulative NEE, GPP, and ER totals for DOY 163–256 (flux measurementwas initiated on DOY 163, missing first 45 days after planting) were−291, 1239, and 948 g C m−2, respectively, in irrigated soybean. Rainfed soybean was a net carbon sink for only two months, while irrigated soybean appeared to be a net carbon sink for about three months. However, grain yield and the magnitudes and seasonal sums of CO2 fluxes for irrigated soybean in this study were comparable to those for soybean in the U.S. Corn Belt, but they were lower for rainfed soybean

Effects of Irrigation and Planting Geometry on Soybean (Glycine max L.) Seed Nutrition in Humid Climates

This study investigates the effect of irrigation (FI, all rows-irrigation; HI, alternate row irrigation; RF, rainfed) and planting geometry (PG) (SR, single-row; TR, twin-row) on soybean seed constituents. Results showed that most of these seed components were significantly affected by crop season due to contrasting precipitation and solar radiation patterns, particularly during July-August, coinciding with early reproductive and seed development stages. Both seed protein and oil levels responded positively to irrigation, while most of the amino acids were nonresponsive. The protein content ranged between 36.3 and 37.6% in 2018, while it was between 36.4 and 38.3% in 2019. Total seed oil content varied between 24.2 and 26.1% in 2018 and between 25.3 and 26.5% in 2019. Among amino acids, glycine, alanine, valine, and methionine levels were significantly higher in both FI and HI treatments. Among sugars, only sucrose was higher in response to the RF treatment, and irrigation did not affect both stachyose and raffinose. Oleic acid was higher in RF, while no significant differences were observed for linolenic and linoleic acids. Similarly, seasonal variation was significant for stearic acid content, but the 2019 season had relatively higher accumulation (stearic acid: between 4.1 and 4.5% in 2018 and from 4.6 to 4.9% in 2019). These results indicate that both irrigation and climate during seed development can alter some seed composition constituents and play critical roles in determining seed nutritional qualities