• Energy Research

Predicting forage biomass yield is critical in managing livestock since it impacts livestock stocking rates, hay procurement, and livestock marketing strategies. Only a few biomass yield prediction studies on pasture and rangeland exist despite the need. Therefore, this study focused on developing a biomass yield prediction methodology through remote sensing satellite imagery (multispectral bands) and climate data, employing open-source software technologies. Biomass ground truth data were obtained from local pastures, where Kentucky bluegrass is the predominant species among other forages. Remote sensing data included spatial bands (6), vegetation indices (30), and climate data (16). The top-ranked features (52 tested) from recursive feature elimination (RFE) were short-wave infrared 2, normalized difference moisture index, and average turf soil temperature in the machine learning (ML) model developed. The random forest (RF) model produced the highest accuracy (R2=0.83) among others tested for biomass yield prediction. Applications of the developed methodology revealed that (i) the methodology applies to other unseen pasters (R2=0.79), (ii) finer satellite spatial resolution (e.g., CubeSat; 3 m) better-predicted pasture biomass, and (iii) the methodology successfully developed for a combination of Kentucky bluegrass and other forages, extended to high-value alfalfa hay crop with excellent yield prediction accuracy (R2=0.95). The developed methodology of RFE for feature selection and RF for biomass yield modeling is recommended for biomass and hay forage yield prediction.

Cropping systems in American agriculture are highly successful since World War II, but have become highly specialized, standardized, and simplified to meet the demands of an industrialized food system. Minimal attention has been given to the efficient exploitation of crop diversity and the synergistic and/or antagonistic relationships of crops in crop sequences. Objectives of our research were to determine if previous crop sequences have long-term benefits and/or drawbacks on spring wheat seed yield, seed N concentration, and seed precipitation-use efficiency in the semiarid northern Great Plains, USA. Research was conducted 6 km southwest of Mandan, ND using a 10 × 10 crop matrix technique as a research tool to evaluate multiple crop sequence effects on spring wheat (triticum aestivum L.) production in 2004 and 2005. Spring wheat production risks can be mitigated when second year crop residue was dry pea (Pisium sativum L.) averaged over all first year crop residues. When compared to spring wheat as second year crop residue in the dry year of 2004, dry pea as the second year residue crop resulted in a 30% spring wheat seed yield increase. Sustainable cropping systems need to use precipitation efficiently for crop production, especially during below average precipitation years like 2004. Precipitation use efficiency average over all treatments, during the below average precipitation year was 23% greater than the above average precipitation year of 2005. Diversifying crops in cropping systems improves production efficiencies and resilience of agricultural systems.

Grazing management effects on soil property dynamics are poorly understood. A study was conducted to assess effects of grazing management and season on soil property dynamics and greenhouse gas flux within semiarid rangeland. Grazing management treatments evaluated in the study included two permanent pastures differing in stocking rate (moderately and heavily grazed pastures) and a fertilized, heavily grazed crested wheatgrass (Agropyron desertorum [Fisch. ex. Link] Schult.) pasture near Mandan, North Dakota. Over a period of 3 yr, soil properties were measured in the spring, summer, and fall at 0‐5 cm and 5‐10 cm. Concurrent to soil-based measurements, fluxes of carbon dioxide, methane, and nitrous oxide were measured on 1-wk to 2-wk intervals and related to soil properties via stepwise regression. High stocking rate and fertilizer nitrogen (N) application within the crested wheatgrass pasture contributed to increased soil bulk density and extractable N, and decreased soil pH and microbial biomass compared to permanent pastures. Soil nitrate nitrogen tended to be greatest at peak aboveground biomass, whereas soil ammonium nitrogen was greatest in early spring. Drought conditions during the third year of the study contributed to nearly two-fold increases in extractable N under the crested wheatgrass pasture and the heavily grazed permanent pasture, but not the moderately grazed permanent pasture. Stepwise regression found select soil properties to be modestly related to soil‐atmosphere greenhouse gas fluxes, with model r 2 ranging from 0.09 to 0.76. Electrical conductivity was included most frequently in stepwise regressions and, accordingly, may serve as a useful screening indicator for greenhouse gas ‘‘hot spots’’ in grazing land.

Harvested hay or biomass are traditionally baled for better handling and they are transported to the outlet for final utilization. For better management of bale logistics, producers often aggregate bales into stacks so that bale-hauling equipment can haul multiple bales for improved efficiency. Objectives of this research include simulation of bale collection logistics after forming subfield stacks, evaluation of location effects of bale stack and field outlet, the number of stacks, transported bales/trip, and other field parameters on logistics distances (aggregation, transportation, and total). The software ‘R’ performed the simulation, statistical analysis, and data visualization. Formation of bale stacks decoupled aggregation and transportation components. Stacks formation thus allows for aggregation and transportation to be performed at different times. Increasing the number of subfield stacks and the number of transported bales/trip significantly reduced the total logistics distances. The order for the best bale stack and outlet locations was: middle, near middle, mid-edge along the length, mid-edge along the width, and finally, corners. Except for swath and windrow variation, the studied field variables had a highly significant influence on the logistics distances. Increased bales/trip (≥6) reduced the variations of outlet locations. Locating the field outlet at or near the center of the field along with an appropriate number of square subfields with stacks at the middle, and increased bales/trip will be the most efficient infield logistics strategy.

The capacity of perennial grasses to affect change in soil properties is well documented but information on switchgrass (Panicum virgatum L.) managed for bioenergy is limited. An on-farm study (10 fields) in North Dakota, South Dakota, and Nebraska was sampled before switchgrass establishment and after 5 years to determine changes in soil bulk density (SBD), pH, soil phosphorus (P), and equivalent mass soil organic carbon (SOC). Changes in SBD were largely constrained to near-surface depths (0–0.05 m). SBD increased (0–0.05 m) at the Nebraska locations (mean=0.16 Mg m−3), while most South Dakota and North Dakota locations showed declines in SBD (mean=−0.18 Mg m−3; range=−0.42–0.07 Mg m−3). Soil pH change was significant at five of the 10 locations at near surface depths (0–0.05 m), but absolute changes were modest (range=−0.67–0.44 pH units). Available P declined at all sites where it was measured (North Dakota and South Dakota locations). When summed across the surface 0.3 m depth, annual decreases in available P averaged 1.5 kg P ha−1 yr−1 (range=0.5–2.8 kg P ha−1 yr−1). Averaged across locations, equivalent mass SOC increased by 0.5 and 2.4 Mg C ha−1 yr−1 for the 2500 and 10 000 Mg ha−1 soil masses, respectively. Results from this study underscore the contribution of switchgrass to affect soil property changes, though considerable variation in soil properties exists within and across locations.

AbstractSimulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi‐species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi‐model ensembles to predict productivity and nitrous oxide (N2O) emissions for wheat, maize, rice and temperate grasslands. Using a multi‐stage modelling protocol, from blind simulations (stage 1) to partial (stages 2–4) and full calibration (stage 5), 24 process‐based biogeochemical models were assessed individually or as an ensemble against long‐term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N2O emissions. Results showed that across sites and crop/grassland types, 23%–40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N2O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N2O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2–4) markedly reduced prediction errors of the full model ensemble E‐median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N2O emissions. Yield‐scaled N2O emissions (N2O emissions divided by crop yields) were ranked accurately by three‐model ensembles across crop species and field sites. The potential of using process‐based model ensembles to predict jointly productivity and N2O emissions at field scale is discussed.

Understanding how agricultural management and climate change affect soil organic carbon (SOC) stocks is particularly important for dryland agriculture regions that have been losing SOC over time due to fallow and tillage practices, and it can lead to development of agricultural practice(s) that reduce the impact of climate change on crop production. The objectives of this study were: (i) to simulate SOC dynamics in the top 30 cm of soil during a 20‐yr (1993–2012) field study using CQESTR, a process‐based C model; (ii) to predict the impact of changes in management, crop production, and climate change from 2013 to 2032; and (iii) to identify the best dryland cropping systems to maintain or increase SOC stocks under projected climate change in central North Dakota. Intensifying crop rotations was predicted to have a greater impact on SOC stocks than tillage (minimum tillage [MT], no‐till [NT]) during 2013 to 2032, as SOC was highly correlated to biomass input (r = 0.91, P = 0.00053). Converting from a MT spring wheat (SW, Triticum aestivum L.)–fallow rotation to a NT continuous SW rotation increased annualized biomass additions by 2.77 Mg ha−1 (82%) and SOC by 0.22 Mg C ha−1 yr−1. Under the assumption that crop production will stay at the 1993 to 2012 average, climate change is predicted to have a minor impact on SOC (approximately −6.5%) relative to crop rotation management. The CQESTR model predicted that the addition of another SW or rye (Secale cereale L.) crop would have a greater effect on SOC stocks (0‐ to 30‐cm depth) than conversion from MT to NT or climate change from 2013 to 2032.Core Ideas Improved estimates of SOC responses to management are needed in dryland regions. Soil organic C stocks were highly correlated to biomass input. Crop rotation was predicted to have a greater impact on SOC stocks than tillage. Anticipated climate change as of 2032 was predicted to have a minor impact on SOC stocks. Addition of another spring wheat or rye crop would increase SOC by 2032.