Agronomy Journal最新文献

Soybean [Glycine max (L.) Merr.] provides plant-based protein for global food production and is extensively bred to create cultivars with greater productivity in distinct environments through multi-environment trials (MET). The application of MET assumes that trial locations provide representative environmental conditions that cultivars are likely to encounter when grown by farmers. A retrospective analysis of MET data spanning 63 locations between 1989 and 2019 was conducted to identify mega-environments (ME) for soybean seed yield in the primary production areas of North America. ME were identified using data from phenotypic values, geographic, soil, and meteorological records at the trial locations. Results indicate that yield variation was mostly explained by location and location by year interaction. The phenotypic variation due to genotype by location interaction effects was greater than genotype by year interaction effects. The static portion of the genotype by environment interaction variance represented 26.30% of its total variation. The observed locations sampled from the target population of environments can be divided into two or three ME, thereby suggesting that improvements in the response to selection can be achieved when selecting directly within clusters (i.e., regions and ME) versus selecting across all locations. In addition, we published the R package SoyURT that contains the datasets used in this work.

Splitting N fertilizer application between the corn (Zea mays) plants pre-emergence and vegetative growth stage has the potential to reduce N losses while increasing N use efficiency. However, there are few published reports that show that splitting fertilizer reduces greenhouse gas emissions. Therefore, this study compared the CO_2e (carbon dioxide equivalent) calculated from N₂O-N and CO₂-C emissions from a single pre-emergence application of 157 kg urea- N ha⁻¹ with a nonfertilized control and two applications of 78.5 kg urea-N ha⁻¹ that were applied at pre-emergence and at the corn plants V6 growth stage. Over three growing seasons (2021, 2022, and 2023), soil temperature, moisture, N₂O-N and CO₂-C emissions were measured six times per day from when the urea was applied to harvest. N₂O-N and CO₂-C emissions were separated into pre-split and post-split periods, and carbon dioxide equivalence was calculated. Splitting the N rate: (1) increased N₂O-N emissions in 2021 and 2022; (2) reduced CO₂-C emissions during the post-split period in 2022 and 2023; and (3) did not influence total CO_2e in 2021 and reduced CO_2e emissions in 2022 and 2023. Based on these results, splitting the N fertilizer rate between pre-emergence and the corn plants V6 growth stage should be considered as a potential climate smart practice.

Winter cover crops reduce erosion and nutrient runoff from agricultural systems. Although cereal cover crops can decrease field nitrate leaching by 50%–95%, the magnitude of this reduction varies within and between fields, making it challenging to monitor the impact of cover crops on nitrate leaching at large spatial extents. Satellite remote sensing using red-edge bands has been shown to effectively estimate crop nitrogen (N) content (kg ha⁻¹) in later growth-stage crops with a closed canopy. In this study, we evaluated 15 spectral indices derived from Sentinel-2 imagery to estimate N concentration (%) and content (kg ha⁻¹) of cereal cover crops, using 1627 destructive samples collected from 2018 to 2023 in Maryland. Observed N content ranged from 0.1 to 214.7 kg ha⁻¹, while N concentration ranged from 0.6% to 5.5%. The 15 indices considered were poor predictors of N concentration (adj. R² = 0.089, root mean squared error [RMSE] = 0.802%), but were more successful at measuring N content (biomass × N concentration). Delta red-edge (ΔRE) was the best predictor of N content (adj. R² = 0.748, RMSE = 13.10 kg ha⁻¹ from cross-validation with 80% train and 20% test splits iterated 100 times) using samples with imagery collected within ±4 days of destructive sampling (n = 1110). Our findings indicate that longer red-edge wavelengths (783 and 740 nm) are more suited for estimating N content in cereal cover crops compared to shorter red-edge wavelengths, which have been shown to be more sensitive to biomass. Leave-one-year-out cross-validation demonstrated that the relationship between ΔRE and N content was robust across all four cover crop sampling years included in the study (adj. R² = 0.700–0.769, RMSE = 10.70–15.40 kg ha⁻¹). Regression model performance improved with the addition of multiple predictors, including biomass (estimated from Normalized Difference Vegetation Index), weather variables (adj. R² = 0.765, RMSE = 12.37 kg ha⁻¹), management variables (species, season, adj. R² = 0.772, and RMSE = 12.13 kg ha⁻¹), and biophysical variables (height, fractional ground cover, adj. R² = 0.818, and RMSE = 10.29 kg ha⁻¹). These findings demonstrate the feasibility of quantifying N content in cereal cover crops using a red-edge-based spectral index across large geographic extents and indicate the inclusion of additional predictors, such as weather and management data, improves model accuracy. This work has implications for quantifying reductions in N leaching associated with cover crops, aiding in policymaking and evaluation of conservation programs that impact water bodies such as Chesapeake Bay.

Reaching production potential, crop quality, and profitability are pivotal goals across cropping systems. The Ohio corn performance test (OCPT) has deployed research approaches in the last 50 years. Partnerships between Ohio State University, seed companies, and cooperating farmers have tested commercially available corn (Zea mays L.) hybrids across several locations in the state. This work aims to identify historical changes in agronomic characteristics, environmental factors, crop management, and their association with crop productivity and gross income over 50 years. Yield improvements were observed, from 9.34 (1972–1981) to 14.78 (2012–2021) Mg ha⁻¹. Adopting management practices such as crop rotation and soil conservation practices (e.g., minimum till, no-till, and stale seedbed) accompanied production improvements. Our results showed that seeding rate, seedling emergence, and final stands had strong correlations with yield (81%, 64%, and 82%). Regions with better weather conditions (i.e., more precipitation, higher average temperatures, lower wind speed) also had strong correlations with yield; the central region had the highest average yield. In this 50-year dataset, OCPT yields represented gross income values 40% higher compared to the average for the state of Ohio during the same period. This study indicates that yield improvements in the corn performance test have been achieved through synergistic changes in new hybrids, key management practices, and coupled with suitable growing environments. Our work reaffirms that selecting hybrids that are best adapted to specific growing environments is a primary factor in achieving high yields and profits at the farm level.

Few studies have investigated the effect on the genotypic value of wheat (Triticum aestivum L.) families with the adoption of the additive and epistatic (additive × additive) relationship matrix. The objective of this study is to select F_2:3 families of wheat by means of three statistical genetics models (without pedigree information, additive, and additive plus additive × additive epistatic) and to evaluate the selection rank between the traditional model and the model with best fit of families for recombination and for deriving progenies. The experiment was composed of a total of 880 F_2:3 families of tropical wheat, from 56 populations conducted by the genealogical method, which came from a full diallel involving the cultivars BRS 254, BRS 264, and BRS 394, CD 1303, Tbio Aton, Tbio Ponteiro, Tbio Duque, and Tbio Sossego. The pedigree matrix was calculated, obtaining approximately 20 generations of ancestry of the parents. The data were analyzed in three genetic-statistical models: Model 1—without information on family relationship; Model 2—computing the additive relationship matrix; and Model 3—including the additive and epistatic (additive × additive) relationship matrix. Using the additive and epistatic (additive × additive) pedigree matrix has a significant effect on most traits. The selection revealed families of populations with potential to be used in recombinations: BRS 254/CD 1303, Tbio Ponteiro/BRS 394, and BRS 394/Tbio Ponteiro, with genetic value to derive progenies: BRS 254/Tbio Aton, Tbio Aton/Tbio Duque, and BRS 394/Tbio Aton, and with both attributes: BRS 254/CD 1303, BRS 394/Tbio Ponteiro, and Tbio Sossego/BRS 264.

Soil organic carbon (SOC) as a key soil health indicator is integral to the soil's capacity to function as a vital living ecosystem that sustains plants, animals, and humans. Accurate SOC estimation is essential to decision-making for an increasing number of stakeholders, such as farmers, industry professionals, and policymakers, to determine the environmental benefit of agricultural practices, and more recently, allocate financial rewards through carbon market initiatives. Our study examined SOC variability in soils from four different regenerative management systems on a single farm using stratification and sample compositing, and analyzed by four different laboratories using dry combustion, the recommended analytical method, but one which varied according to laboratory standard operating procedures (SOP). Results showed significant variation in SOC levels for the same soil samples at different laboratories (1.6 ± 0.2 g kg⁻¹), variation comparable to that between the distinct management systems (1.5 ± 0.4 g kg⁻¹). Our findings show that analytical variability within and between laboratories must be considered, that use of the same laboratory, and to the extent possible the same SOP for successive SOC measurements at the same location is necessary, and that rigorous stratification alongside minimal sample consolidation should be conducted to generate analytical sample numbers that cater to logistics, economics, and scientific rigor.

Estimating potential crop yield during the growing season allows growers to adjust inputs, set reasonable harvest expectations, and guide marketing decisions. Therefore, the ability to estimate yield is a valuable yet difficult goal for growers. To evaluate the potential of several methods of wheat (Triticum aestivum L.) grain yield prediction, this experiment used published methods based on phenological characteristics: stand and tiller/spike counts, and newer methods that employ image- and reflectance-based approaches, such as fractional green canopy cover (FGCC) and normalized difference vegetation index (NDVI) readings. This experiment was conducted across six locations (Banner, Box Butte, Cheyenne, Intensively Managed at Cheyenne, Deuel, and Kimball counties) in western Nebraska during 2019–2020, 2020–2021, and 2021–2022 for a total of 11 site-years. Treatments consisted of seven winter wheat varieties that were evaluated in the Winter Wheat State Variety Trials. Stand count did not show a significant correlation with wheat yield (0.09) nor model fit for yield estimation. Spike count was significantly correlated with yield (0.54), but efforts to use it to estimate final yield were not significant. Due to the inconsistency of yield prediction with historical methods, analyses of novel methods of yield estimation were warranted. NDVI and FGCC readings correlate with wheat yield and model fit efforts were successful. NDVI at Feekes 10 correlated significantly at 0.39, while FGCC had correlations of 0.56, 0.50, and 0.68 at Feekes 2, 4, and 10 (respectively). This experiment suggests that NDVI and FGCC are methods that could be used to replace outdated and laborious approaches.

Groundnut productivity in rainfed regions of arid and semi-arid agro-ecosystems in southern India is highly vulnerable to intra-seasonal rainfall variability as well as the timing, duration, frequency, and intensity of dry spells. The Rayalaseema semi-arid region of India, a major groundnut (Arachis hypogaea L.)-growing region, has experienced increasing intra-seasonal rainfall variability, especially over the last few decades. To understand the impacts of dry spells on the growth and development of groundnut, field experiments on groundnut (cultivar K6) were conducted at the Agricultural Research Station, Ananthapuramu, Andhra Pradesh, during 2015–2020. The results indicated that the amount and distribution of rainfall during different phenophases were the most influential parameters on dry matter production, phenology, and pod yield in groundnut and can have either a positive or negative effect depending on whether the rainfall matches the crop's water requirements during specific growth stages. It was found that, in July second fortnight (II-FN) sown crop, the rainfall received was synchronized with pod initiation to maturity stage, resulting in higher dry matter production (456 g m⁻²) and pod yield (1123 kg ha⁻¹). On the other hand, pod initiation-pod maturity stage with August I-FN sown crop received less rainfall, leading to a soil moisture deficit and lower pod yields (815 kg ha⁻¹). The duration of pod initiation-pod maturity stage with August I-FN sown crop was significantly less (47 days) compared to July II-FN (54 days) and July I-FN (50 days) sown crops, resulting in reduced pod filling period that drastically affected pod yield. It can be concluded that the existing sowing time for groundnuts needs to be advanced from August I-FN to July II-FN. This will not only avoid terminal moisture stress during pod initiation-pod maturity stage but also help in enhancing groundnut productivity in rainfed areas of Rayalaseema region.

Fraise mowing has traditionally been used as an aggressive cultural practice for bermudagrass thatch management in golf and sports turf settings. Despite expanded applications, its effect on edaphic characteristics has yet to be thoroughly explored. The objective of this research was to evaluate physical properties of two soil types beneath established Tifway hybrid bermudagrass (Cynodon dactylon × C. transvaalensis Burtt. Davy) following fraise mowing. A study was conducted during the summers of 2016–2019 on a Cecil sandy loam (loam) and a sand-capped soccer field (sand) in Wake County, NC. Three fraise mowing depths of 0.6, 1.2, and 2.5 cm, and an untreated control were administered in mid-June of each year. Thatch content decreased by 18%–50% after fraise mowing. Divot resistance measured within the sand rootzone decreased by ≤24% with increasing fraise mowing depth and the removal of roots, rhizomes, and stolons. Saturated hydraulic conductivity in the sand decreased by 0%–57% with increasing fraise mowing depth compared to the control. Surface hardness increased ≤53% with 1.2- and 2.5-cm fraise mowing depths with differences more pronounced in the loam (≤28 g_max) compared to the sand (≤6 g_max). At saturation (0-cm pressure) and field capacity (100-cm pressure), water content decreased by 9%–18% and 5%–10%, respectively, with increasing fraise mowing depth in the sand. Results indicate fraise mowing did alter soil physical properties in both soils and increasing fraise mowing depths increased the effects.