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.

Cotton (Gossypium hirsutum L.) is an important cash crop in the United States, grown mainly for fiber production, and plays a significant role in the feed and oil industries. Therefore, increasing cotton lint yields for food security and agricultural sustainability is an important objective for cotton breeders. Over the years, breeding practices have significantly improved cotton yield by considering aboveground growth only. However, there is minimal information available regarding how root structure has changed during the breeding process. Therefore, a field study was conducted at EV Smith (EVS) Research Center, Alabama, in 2020 and 2021 to understand how breeding and management practices have indirectly influenced the root distribution and architecture of cotton cultivars. A randomized complete block design (RCBD) with four replications was employed to study 20 cotton cultivars released over the past 70 years. The Shovelomics method was utilized to collect root crown samples at the peak flowering stage, which were subsequently phenotyped using the innovative RhizoVision Crown platform. Additionally, the Giddings machine was employed to excavate root samples at different soil depths, and the cleaned roots were scanned using a WinRhizo desktop scanner to calculate various root parameters. Early results indicate that newer cultivar produces shallow but complex root systems. More research findings will be added here.

Recent enhancements in farming practices and analytical indicators for soil health overshadow the fact that concepts of living soil and related lab methods emerged in the literature decades earlier than commonly cited. We employ the example of soil biological CO₂ respiration showing that as early as 1923 concerns were published encouraging measuring soil biology factors and not just inorganic factors for yield stability.

After WWII standardization of analytical methods emerged such as CO₂ respiration as a tool to monitor deleterious influences from modern, intensive farming practices. Evidence exists in proposals for biological indicators (1976), healthy soil (1978), soil microbial enzymology (1984), among others. Noteworthy is the early careful recognition in the literature of artifacts due to soil lab grinding and sieving, disputed even today.

A re-appraisal would be intriguing from an analytical standpoint and should aid current discussions on robustness. Early projects related to farm system effects ignited profound interest in broader comparisons of soil quality with farm performance. Examples published show combinations of tests as successful indexes for earthworm counts, enzymes and overall soil CO₂ respiration. Data from a German Ministry of Agriculture 1976 comprehensive report on paired conventional vs biological farming clearly shows most current recommended “soil health indicators” were successfully employed and their limitations recognized decades ago. One obvious conclusion would be the absence of authentic innovation in the present.

Possible explanations for the disconnect of this early body of science with current USA trends include unfortunate gaps in language/culture and apparent continued institutional bias in the USA against organic farming. Full recognition and due credit for the early work should secure progress towards diversity essential for monitoring and solving pressing farming issues and to encourage appropriate analytical progress independently of interests for commercial soil lab practices mostly designed for large scale production and industrial development interests.

On-farm precision experiments (OFPE) help farmers and agronomists to quantify within field spatial variability of crop response. Spatially dense crop and soil data are now cheap and easily available. Although there are numerous studies about relationships between site-specific characteristics and yield aimed to get yield predictions or develop management zones, few studies have addressed the causes of spatial variability of crop yield response to controllable inputs. Once estimated these spatial crop functions, two common question are frequently asked: What are the causes of those patterns? Are those patterns repeatable through time? The aim of this work was to identify key soil attributes that governs the spatial distribution of the crop response to inputs within fields. The data from four nitrogen and seed rate trial of Data-Intensive Farm Management (DIFM) project were employed. Yields and as applied nitrogen rate (NR) and seed rate (SR) treatments data were processed using the geographically weighted regression (GWR) technique to get spatially variable crop response functions. Soil electrical conductivity (ECa) and landscape covariates (elevation, slope, curvature, topographic wetness index) were spatially joined to optimal rates estimated from spatially variable crop functions estimates. Relationships were explored using non-parametric tree based methods. Soil and terrain covariates explained ~25% to 70% of variability of optimum NR and SR. ECa explained SR and NR only in one field. Among terrain attributes, slope and curvature contributed to explain variability of NR and SR in two out of the for fields. This field-to-field variation makes the criteria applied for management zone delineation in on field may not work for another.

Although soil harbors the most diverse groups of microorganisms among global ecosystems, the bulk soil contains little bioavailable food to support active growth of indigenous microbes. To cope with a nutritionally depleted habitat, most soil microbes exist in a dormant state, or other states of low metabolic activity. Such low microbial activity may limit some metabolic functions beneficial to soil health. The amendment of soil with microbial foods, aiming to enhance soil microbial growth and activity, has been an attractive option. However, few studies have been conducted to examine the direct impact on the soil microbiome of common soil amendment products. Here, we developed a laboratory-based system and used it to quantify the impact of soil amendments on soil microbial communities. Our system consists of a 16-oz Mason jar containing approximately 100 g of a field soil sample. After the application of products to be studied, the jar was covered with multiple layers of cheese cloth to facilitate aeration. Soil samples were collected at designated times for the characterization of the soil microbiome (via amplicon-based sequencing and shotgun metagenomic sequencing), determination of culturable bacterial populations, and measurement of microbial biomass (via PLFA profiling). Selected soil samples were then processed at the end of the experiment for soil aggregation and water-holding assays.

We have employed this assay system to evaluate the impact on the soil microbiome of PhycoTerra® and PhycoTerra® Organic, two different formulations of microalgae-based soil amendment products. Other soil amendment products, such as molasses and seaweed extract, were included, along with a water-only control. Our results demonstrate that many of these amendments impacted the soil microbiome by increasing soil bacterial populations and microbial biomasses. Both formulations of PhycoTerra® stimulated the growth of diverse groups of soil microbes and, moreover, improved soil health by enhancing soil water-holding capacity.

The direct measurement of soil gases provides insight into the biogeochemical processes responsible for nitrogen cycling, respiration, and microbial signaling. Fast, spatially resolved measurements of concentrations and isotopic signatures of soil gases afford unique insights into characteristic responses of these systems to changes in environmental conditions, such as recovery from drought. We have developed and deployed a fast monitoring system consisting of new diffusive soil probes coupled to a Tunable Infrared Laser Direct Absorption Spectrometer (TILDAS). We monitored a range of soil gas species as messengers of biogeochemical nitrogen and carbon cycling in the soil. We present measurements of the response of soils to re-wetting in laboratory studies and at the Tropical Rainforest at Biosphere 2 in Arizona. In the laboratory we measured a wide range of trace gases before, during, and after a simulated rainfall upon northeastern US temperature forest soil in meso-scale columns. At Biosphere 2 the probe-TILDAS system followed trace gas and isotopic signatures during a two-month induced drought in the rainforest and the subsequent return of rain. Subsurface concentrations and above-ground fluxes of N₂O and its isotopes, CH₄ and its isotopes, and CO₂ were quantified in real time with temporal resolution of 30 min or 2 hours for the laboratory and field, respectively. In addition, NO, NO₂, and a wide range of volatile organic compounds (e.g. monoterpenes, sesquiterpenes, isoprene, acetonitrile, aromatics) were also measured in the laboratory study. We will discuss similarities and differences between these studies as well as plans for additional deployment of the subsurface gas sampling system.

Identifying suitable species for simple and complex pasture seeding mixture in specific growing conditions can help reduce negative impact of invasive species and anthropogenic disturbances. Establishment and performance of four native cool-season (C3) grasses, three introduced C3 grasses, and three C3 legumes in monoculture and complex seeding mixture (3-9 species) were evaluated at the University of Wyoming James C. Hageman Sustainable Agriculture Research and Extension Center near Lingle, Wyoming at three different planting times and two growing conditions. The species were sown in spring, fall, and late fall (dormant planting) under irrigated and dryland conditions in 2018. Growing condition, planting time, and species mixture significantly affected emergence, plant density, plant cover, dry matter (DM) yield, and forage nutritive value (P<0.001). In 2019, tall fescue DM was 5.2-8.9 Mg ha^-1 in irrigated and 1.2-5.8 Mg ha^-1 in dryland conditions. Spring planting resulted in higher performance of legume (cicer milkvetch, sainfoin, and birdsfoot trefoil) monoculture (4.5-7.3 Mg DM ha^-1) and mixture with native and non-native grasses (4.1-8.1 Mg DM ha^-1). Native grass when grown with legumes had higher relative yield (relative yield total, RYT>1) compared to yield of native grass mixture. Seed mixture of native and non-native grass in equal proportion did not contribute to higher relative yield (RYT<1) compared to native and non-native grasses grown separately. When relative feed value was regressed against the proportion of legume in the mixture, the quadratic relationship indicated that at least 25% of legume was needed in the mixture for high forage nutritive value (e.g., crude protein 200 g kg^-1)and productivity (e.g., 6.8 Mg DM ha^‑1). Overall, the study demonstrated that introduced C3 grasses, particularly tall fescue, have potential to be used in both irrigated and resource-poor dryland conditions either as monoculture or as mixture with legumes.

Low forage yields from old alfalfa stands are often a result of poor management practices. Appropriate agronomic amendments such as potassium (K) fertilization and harvest time can help revive the yielding potentials of old alfalfa stands. A study was conducted on a 13-yr old alfalfa stand at UW SAREC, Lingle in 2019 to determine K × harvest time interaction effect on yield response of old alfalfa stands. Six K rates (0, 56, 112, 168, 224, and 280 kg K₂O ha^-1) were applied to alfalfa. Alfalfa was harvested at two harvest times (early harvest, late bud to early [10%] bloom; late harvest, 7 days after early harvest). Treatments were organized in a randomized complete block design with four replications. The 224 kg K₂O ha^-1produced the highest total forage yield (8.0 Mg ha^-1) at early harvest, while 168 kg K₂O ha^-1produced the highest total forage yield (8.6 Mg ha^-1) at late harvest. Differences in plant growth at both harvest times might have influenced the amount of K required to improve yield. There was a quadratic relationship (P= 0.01, R²= 0.39) between forage yield and relative water content of alfalfa. This suggests that the moisture content in alfalfa in conjunction with K and harvest management plays an important role in alfalfa for yield potential. Overall, preliminary results of the study indicate that applying K to old stands of alfalfa based on when to harvest could be a viable option to improve forage production of declining old alfalfa stands.

As average global temperatures rise, cool-season C3 grasses, such as the most commonly grown Kentucky bluegrass (Poa pratensis L.), struggle to tolerate extreme summer heat and require more of increasingly scarce water. Triploid interspecific hybrid Bermudagrass (Cynodon dactylon [L.] Pers. × C. transvaalensis) is a warm-season C4 grass that may be increasingly more suited for northern ecosystems traditionally classified as transitional or cool-season climates. This C4 grass has been successfully grown for a decade in Provo, UT, USA, an area in a traditionally cool-season zone, and is being considered for more widespread use in this and similar regions. The objective of this study is to evaluate two hybrid Bermudagrass varieties compared to a blend of Kentucky bluegrass varieties for water loss and canopy health, temperature, and growth when mowed at moderate or short heights in all combinations of deficit, moderate, and high irrigation. Surprisingly, mowing height had few impacts in relation to irrigation rates. Although the results of the study show that the two types of Bermudagrass respond differently to deficit irrigation, both had significantly more root growth than Kentucky bluegrass, with roots that stretched deeper and had a greater biomass. While Bermudagrass also produced greater shoot biomass than Kentucky bluegrass, the latter had a higher shoot to root ratio. The results of this direct comparison with Kentucky bluegrass and Bermudagrass reinforce the notion that the latter has more extensive root systems to extract water from a greater volume of soil, which will decrease irrigation needs as this species is potentially used more frequently in cool-season climes where water resources are increasingly scarce.