The physical processes on the soil surface include the redistribution of water and heat. Simulations of those processes are challenging because (1) the inertia of physical properties on soil surface is small, hence relatively small amounts of water and heat fluxes can induce significant changes of those physical properties; (2) the physical processes are subjected to specific weather conditions and agricultural management practices. In this study, we proposed an integrated model in MAIZSIM that performs those physical simulations on the soil surface. The proposed model divides the spatial domain near the soil surface into four layers, i.e., atmosphere, mulch, surface runoff and shallow soil. Physical processes within each layer follow their governing models. Especially for the mulch layer and the surface runoff layer, we applied a revised Philip-deVries model and Saint Venant equation, respectively. The mass and heat exchange along the interfaces of those four layers formulated the interior boundary conditions. Water was allowed to exchange in vapor and liquid phases; while heat flow followed diffusion, conduction, free/forced convection and radiation. To enable rapid changes of physical status near soil surface and ensure numerical stability, each layer had its own time scale, and those time scales were aligned with the time scale for soil water movement with a finite element domain. The proposed model was also developed in an adaptive way, such that if the mulch or surface runoff layer disappeared, the model could still be executed by omitting the missing layer. Evaluation of the proposed integrated model was performed with datasets from field measurements, and the proposed model was demonstrated as an effective tool in the simulations of physical processes on the soil surface.

The turnover rate (i.e., replacement frequency) of fine root biomass is a sensitive parameter in soil biogeochemical models because it regulates fluxes of organic matter. Most global estimates of fine root turnover are computed from algorithms that assume that fine root growth and dieback occur in separate phases. However, direct observations of fine root lifespans have shown that fine root cycling is continuous and both phases coexist in time. In this study, two models based on the growth-maintenance respiration paradigm (GMRP) are proposed for quantifying continuous fine root cycling by assuming that losses from dieback and maintenance respiration increase proportionally with fine root biomass. Both models predict exponential asymptotic growth, but have different hypotheses about whether plant energy is prioritized for growth (GP) or for maintenance (MP). Using existing sequential coring datasets (n=111) collected from 15 global grasslands, we found that the turnover and gross production rates predicted by the GP and MP models were more than 60% greater than those estimated by the traditional algorithms that neglect continuous fine root cycling. This suggests that below-ground carbon allocation and soil nutrient fluxes may have been previously underestimated, especially in grasslands where fine root cycling is rapid and where the growing conditions are favorable year-round. Although predicted fine root turnover was systematically faster using the GMRP models, reported patterns of turnover with climate were preserved: turnover rates increased exponentially with mean annual temperature by a Q₁₀ coefficient of 1.65 (R²=0.62), and by 1.6% (R²=0.34) per unit of natural logarithmic change in mean annual precipitation. These results will facilitate upscaling the GMRP model simulations to evaluate global fine root turnover impacts on soil organic matter accumulation and feedbacks on soil structural development.

Corn Response to Different Sulfur Application Rate in the Red River Valley of ND and MN

Diksha Goyal, D.W. Franzen and Amitava Chatterjee

Department of Soil Science, NDSU, Fargo, ND

Abstract

The occurrence of sulfur (S) deficiency is more common nowadays due to less deposition of atmospheric S in the soil, and more removal by crops. The objective of this study is to evaluate the effect of S fertilization on the corn (Zea mays L.) grain yield and S uptake. The effects of different S rates (0, 11, 22, 33, and 44 kg S ha^-1) on corn growth investigated at ten locations of the Red River Valley of North Dakota and Minnesota in 2018 and 2019 growing seasons. Granular ammonium sulfate was broadcasted as a form of S fertilizer, before corn planting using a randomized complete block design with four replicates. Corn grain yield was measured at harvest and S uptake was measured at maturity. In 2018 and 2019, the S application rate for the highest corn yield varied with sites. In 2018, highest S uptake varied with sites and in 2019, S uptake increased with 44 kg S ha^-1 fertilizer rate at one site. These experiments indicate that the response of corn to S vary among sites, and if no response of S observed, there was enough S in the soil for crop growth due to mineralization of S, and hence S fertilizer application had no effect on yield.

Keywords: Corn, sulfur, yield, S uptake

Winter cereal rye (Secale cereale L.) could be an alternative grain crop to include in a corn-soybean rotation, and to provide soil coverage during the fall and springtime when corn and soybean do not. Little or old research has investigated N fertilization requirement for winter cereal rye seed production in the state of Iowa. The objectives of this study were to evaluate N response in cereal rye seed production, determine rye optimal fertilizer N application rate, and study the impact of a rye crop and inter-seeded red clover crop on a following corn crop N response. In the first phase, two cereal rye varieties (ND Dylan and Elbon) were no-till planted following soybean harvest in the fall 2017 and 2018 at two locations each year. Six total N rates (0, 25, 50, 75, 100 and 125 lb/acre) were fall-spring split-applied. Rye varieties were split with and without late winter inter-seeded red clover. Rye crop canopy sensing was collected multiple times during the growing season, and grain yield and profile soil nitrate measured. In the second phase, corn was no-till planted in the year following rye harvest. The red clover was terminated prior to corn planting. Six N rates (0, 50, 100, 150, 200 and 250 lb/acre) were split-applied at planting and sidedress. Corn canopy sensing was collected at mid-vegetative growth. The inter-seeded red clover had no effect on rye N response or yield. The mean agronomic optimum N rate for both rye varieties was similar (88 lb N/acre) but yield higher for ND Dylan at 50 bu/acre versus 34 bu/acre for Elbon. Post-rye harvest soil profile nitrate-N was low with all N rates. Red clover supplied corn with an average 40 lb N/acre (only first year data available).

High school students from Boca Raton Community High School, in collaboration with the Joint Genome Institute, the University of Florida, and Texas A&M investigate the Florida Everglades through the usage of Next Generation DNA sequencing, water and soil analysis in order to better understand the microbiome of this relatively unexplored region.

Metagenomically assembled genomes (MAGs): Overall, 122 medium quality and 6 high quality MAGs were reported representing many of the abundant microbial phyla across the samples. Significant correlations were observed between estimated gene frequencies and environmental factors of both the water and sediment.

Weather conditions in the southwestern U.S. are variable and influence crop growing periods with late spring and early fall frosts which significantly impact cropping seasons. With the development of new maize hybrids, grain yield, evapotranspiration, and water use efficiency can be substantially impacted by planting density and planting date. Thus, it is critical that the optimum plant density and planting date for maximum grain yield be determined for local conditions. Field experiments were conducted to evaluate six plant densities (54700, 64600, 74600, 88000, 101700 and 120100 pph) under seven planting dates (from April 23 to June 5 in 2019 and from April 21 to June 10 in 2020) to determine the planting window and the optimum density. Plots were sprinkler irrigated and crop management was similar across all planting dates during the two growing seasons. The results showed that crop height and leaf area index varied with plant density and planting date. Grain yield also varied with plant density and planting date. The highest grain yield (16.8 Mg ha^-1) was observed under the density 101,700 pph and the first planting trended to provide the best grain yield in 2019. In 2020, the highest grain yield (17.77 Mg ha^-1) was obtained under the density 88,000 pph and the May 18 planting provided the best grain yield and the greatest WUE. Plant density 88,000 plant /ha was revealed the optimum density that maximized grain yield and WUE and maize planting after May 25 is not recommended.

Pharmaceutical compounds (PCs) are introduced to the agro-environment mainly via intensive irrigation with treated wastewater and application of organic fertilizers such as biosolids, manure and compost. PCs are of potential ecotoxicological concern because they are designed to induce specific biochemical effects and/or they are highly adsorbable, resulting in a tendency to accumulate in solid matter such as soil, sediment and plant tissues. Due to their increasing ubiquity, it is essential to understand the fate of PCs in soils and to evaluate the effects of organic amendment on their behavior in the environment. Highly mobile PCs have the potential to leach to the groundwater, whereas strongly sorbing PCs can accumulate in the top layer of soils. In addition, the influx of dissolved organic matter (DOM) originating from reclaimed wastewater and/or from sludge application into the soil can affect the behavior of the PCs.

The objective of this study was to evaluate the effect of biosolids application and different soil solution parameters on the transport behavior of selected PCs in loess soil. The studied PCs were sulfapyridine (SPD), sulfamethoxazole (SMX), carbamazepine (CBZ), naproxen (NAP), diclofenac (DCF) and gemfibrozil (GFB).

Our results show that the addition of biosolids to the soil reduces the mobility of the less polar PCs (i.e., CBZ, DCF and GFB). In addition, the application of secondary treated waste water (STWW) as background solution increased the mobility of PCs classified as weak acids (NAP, DCF and GFB) compared to DOM-free solution. The main factors identified as possible causes to this difference in behavior are solution pH, DOM content and concentration of mono- and divalent cations.

Nonpoint source phosphorus (P) loss from agriculture represents an important source of excess P delivered to Lake Champlain, whose shores connect New York, Vermont, and Quebec. Much of the crop land in the Lake Champlain Basin consists of poorly drained marine and lacustrine clays, resulting in high rates of tile drainage adoption by the region’s dairy farms. Understanding how crop and manure management, soil type, tile drainage, and the cold, wet climate interact to influence P and nitrogen (N) losses is crucial to achieving regional conservation efforts. No-till crop production has increasingly been adopted in the region as a means to improve soil structure and limit erosion-induced P losses. In 2018, an edge-of-field monitoring project was initiated at Miner Institute (Chazy, NY) to investigate how no-till corn (Zea mays, L.) silage production impacts P and N losses from surface runoff and tile drainage. A two-year calibration period with continuous monitoring from October 2018 through September 2020 identified strong relationships between the two experimental fields (2.4 and 2.5 ha) for all response variables (runoff, dissolved reactive P, total P, nitrate, ammonium, total N, and total suspended solids) measured on an event basis. These data will provide the basis for comparison to the treatment period (2020-2023) during which one field will transition to no-till corn production with surface-applied manure and the second will continue with the current management of disk tillage following fall manure application and prior to spring planting.

The GSS will have effects on agriculture research, broad and specific.

Livestock production has created local, regional and global environmental concerns. Many options exist, or are proposed, for mitigating the environmental impacts of livestock production, but finding practical and economically feasible solutions is challenging. Process level simulation of production systems provides a rapid, cost-effective method for estimating environmental impacts and assessing the potential benefits of mitigation strategies. Simulations supported by real farm data and published research provide useful information and guidance toward more sustainable production systems. The Integrated Farm System Model is a tool that is used to evaluate environmental and economic benefits and costs of alternative crop, dairy and beef production systems. Simulations over many years of weather provide estimates of long-term performance, resource use, nutrient losses, gaseous emissions, nutrient balances, costs of production and net returns to management. Evaluation of dairy farms in the northeastern U.S. has shown that a combination of changes in feeding, manure and crop management practices can potentially reduce the intensity of greenhouse gas emission, reactive N and total P losses by about 40%, while increasing milk production and maintaining profitable operations. For cow-calf production systems, a combination of improved feed efficiency, calf implants, improved weaning rate, reduced cow body size, terminal cross, and improved fiber digestion is found to reduce the intensity of greenhouse gas emissions, fossil energy use, water consumption and reactive nitrogen losses each by about 15%. These data are being integrated across production systems to study regional and national benefits of these and other mitigation strategies. By working together, experimental and modeling data can be used to develop, assess and implement strategies for improving the sustainability of our livestock production systems.