Agronomy Journal最新文献

Crop models are valuable tools for simulating and assessing genotype-by-environment interactions. In most studies, these models are parameterized based on crop data from a few sites and years, which often limits their applicability to a broader geographic context. Therefore, we utilize countrywide multi-environment variety trial data in this study to implement a genotype-specific model parameterization for winter rye (Secale cereale L.) in Germany. We use the Crop and Environment REsource Synthesis (CERES) model originally used for wheat available in the decision support system for agrotechnology transfer (DSSAT) framework and adapt and evaluate it for rye. Calibration and evaluation involved a comprehensive agronomic trial datasets for the rye cultivar Palazzo, encompassing 194 site-years of experiments covering various cereal production regions in Germany. The parameterization followed a structured approach, encompassing phenology, growth, and yield-specific coefficients. The parameterized CSM-CERES-Rye (where CSM is cropping system model) demonstrated reasonable accuracy in simulating critical crop parameters, including aboveground biomass, leaf area index, tiller, grain number, unit seed weight, and grain yield. The model is available for diverse model-based assessments of rye cultivation, including evaluating crop management, analyzing crop rotations, and assessing rye's suitability across varied environments, making it valuable for sustainable agriculture and decision-making.

Soybean [Glycine max (L.) Merr.] is highly efficient in the biological N₂ fixation (BNF) process through the association of bacteria of the genus Bradyrhizobium in the root nodules of the plants. However, there are still doubts about the need to complement soybean N demand through N fertilization in high-yield environments. In addition, the real impact of co-inoculation of soybean with Azospirillum brasilense and Bradyrhizobium spp. is not yet clear in such environments. A field experiment was conducted from 2012 to 2021 with six soybean cropping seasons in a crop rotation scheme with black oat (Avena strigosa Schreb), maize (Zea mays L.), and wheat (Triticum aestivum L.) under no-till (NT) in Southern Brazil. Soybean seeds were co-inoculated with A. brasilense (strains Ab-V5 and Ab-V6) shortly after inoculation with Bradyrhizobium japonicum, and different levels of N fertilization were used in top dressing at the start of pod formation (R₃). Soybean nutritional status and grain yield were not benefited by co-inoculation with A. brasilense. Since the increased inoculum rate of A. brasilense co-inoculated with rhizobia in soybean compromised both N nutrition and grain yield, this practice should not be encouraged. There was no need to complement soybean N demand through N fertilization during the reproductive stage. Soybean achieved grain yields of 5.0–5.7 Mg ha⁻¹ and, even so, there was no need to complement N demand through N fertilization. The results suggest that soybean N demand in a high-yielding environment under NT could be satisfied exclusively through the optimization of BNF.

The environment to which a crop is exposed during the growing season has a significant impact on seed composition. The objectives of this study were to (1) estimate the contribution of genotype (G), environment (E), and their interaction (G × E) to the agronomic features and seed quality of chickpea and (2) identify climatic variables that significantly affect chickpea nutritional composition. A total of 10 pre-commercial and commercial cultivars of Cicer arietinum L. were evaluated in 15 environments in Argentina. Average plant height ranged between 44.7 and 55.2 cm, 100-seed weight was 38 and 27 g, and grain yield was 750 and 1500 kg ha⁻¹ for kabuli and desi chickpea, respectively. The results obtained from the variance component analysis showed statistically significant (p < 0.05) contributions of the effects of G, E, and G × E interaction on the nutritional quality of chickpea seeds. Protein, carbohydrates, and mineral content were mostly affected by E, whereas oil content was G-dependent. Only tocopherols were affected by G × E interaction. The total phenotypic variance is mostly composed of environmental effects captured during the seed filling period. High mean daily air temperature had a negative effect on carbohydrates and increased protein and mineral content. The fatty acid profile and gamma tocopherol contents were affected by accumulated precipitation and evapotranspiration. Air humidity was negatively correlated with protein content and iodine value. Results from this research are useful for breeders to broaden the genetic background of chickpea genotypes and for farmers to identify climatic conditions that impact grain quality.

This study quantifies the effects of wide row spacing on lint yield, yield components, and fiber quality of upland cotton (Gossypium hirsutum L.). An experiment was conducted in Tifton, GA in 2021 and 2022, where cotton was planted in six replications of 91-, 122-, 152-, and 183-cm row spacings. Following defoliation, 1.8 m was hand harvested from each plot, and boll density plant⁻¹ and density ha⁻¹ were determined for sympodial and monopodial branches. Additional measurements included fruit and lint yield distribution assessments and intra-boll yield components including seed cotton weight boll⁻¹, seed index, seed boll⁻¹, lint weight seed⁻¹, seed surface area (SSA), fiber density, and single fiber weight. Lint yield was reduced 20% in the 183-cm row spacing compared to the 91-cm row spacing. The 91-cm rows had the lowest number of sympodial bolls plant⁻¹ with 152- and 183-cm rows demonstrating a 24%–28% increase in sympodial bolls plant⁻¹. Sympodial bolls ha⁻¹ were reduced 22% in the 183-cm row spacing compared to the 91-cm row spacing. There were no differences in bolls ha⁻¹ or lint yield ha⁻¹ with respect to monopodial growth. There were no differences in seed cotton weight boll⁻¹, seeds boll⁻¹, fiber density, single fiber weight, or turnout. Seed index and SSA were increased in the 183-cm row spacing. Lint weight seed⁻¹ was reduced in the 91-cm row spacing.

Researchers have extensively studied and documented the effects of potassium (K) fertility on alfalfa (Medicago sativa L.). Yet, additional research is needed to determine how interactions of K, cultivar, and harvest management influence the K needs of alfalfa. To explore these interactions, we conducted 5 years of field research at the University of Wyoming James C. Hageman Sustainable Agriculture Research and Extension Center in Lingle, WY. Treatments were (a) four K rates (0, 56, 112, and 168 kg K₂O ha⁻¹ year⁻¹) applied before planting in the fall of 2016 and after the final harvest in the fall of 2017–2020, (b) two cultivars (Hi-Gest 360 and AFX 457), and (c) two harvest times (early harvest, late bud to early [10%] bloom, and late harvest, 7 days after early harvest), arranged in a 4 × 2 × 2 factorial under random complete blocks with four replications. At 168 kg K₂O ha⁻¹ year⁻¹ and early harvest, a consistently significant (p < 0.05) higher yield response was observed. The same response was seen at 112 kg K₂O ha⁻¹ year⁻¹ and late harvest. This occurred at a site with moderate-to-high soil K levels throughout the study period. There was a linear (p < 0.001, R² = 0.66) and quadratic (p = 0.006, R² = 0.61) response of forage accumulation to K rate at early and late harvest, respectively. Similar trends were also seen for stem count, relative water content, root uptake of K, and tissue K. Time of harvest showed immense potential for optimizing K's effect for a consistent high-yield response. However, fertilizing alfalfa with 112 kg K₂O ha⁻¹ year⁻¹ gave the most profitable production under both harvest times. If K fertilizer prices drop over time, high profits could be attained with higher K fertilization rates. After 3 years of production, average forage accumulation increased under an early harvest system and decreased under a late harvest system. Growers in Wyoming and similar regions are encouraged to consider fertilizing alfalfa with moderate K rates (∼112 kg K₂O ha⁻¹ year⁻¹) on soils testing moderate-to-high in soil test K, implement a late harvest system for the first 3 years after planting, and transition to an early harvest system after the initial 3 years to maximize alfalfa profits.

An additive main effects and multiplicative interaction (AMMI) model is used to explore the genotype × environment interaction (GEI) in complete multi-environmental trials. This model orders genotypes (G) according to their performance across environments (E) on a vectorial plane generated by the first two axes of a principal component analysis (AMMI-biplot). Alternatively, interaction terms can be regarded as random effects, which can be predicted from linear mixed models using a factor analytic (FA) covariance structure for the GEI terms. Here, an FA-biplot was obtained by plotting the G and E scores derived from the FA mixed model with complete and incomplete data. The aim of this work was to compare AMMI-biplot with FA-biplot for balanced data and then show the impact of the imbalance on the FA-biplot. The G ordinations were assessed in four scenarios generated using datasets of 3 consecutive years obtained from comparative wheat trials conducted under a complete random block design in different environments across the Argentine network of cultivar assessment. For each scenario, G with the lowest performance in the third year were deleted, one by one, from all sites to generate a scenario with missing G. Although we used different statistical procedures to obtain AMMI-biplot and FA-biplot, they showed the same interaction pattern in the case of up to 50% of G dropped from all E in the last year of the multiyear trials. We conclude that the FA-biplot yields a robust G ordination even when with incomplete datasets.

During symbiosis, C that rhizobia respire to power N fixation can be stored as polyhydroxybutyrate (PHB), shown to support rhizobia survival under laboratory starvation. We collected soil in 2015 from four replicate plots per treatment in two long-term experiments at Waseca, MN. Treatments differed in the intervals between soybean [Glycine max (L.) Merr.] hosts. We measured PHB accumulation in eight nodules per plant from four soybean (cv. MN0095) trap plants per soil sample. Trap plants were arranged in a greenhouse, common-garden experiment, and PHB accumulation was measured using flow cytometry. Treatments sampled after long intervals without soybean (greater than 2 years) showed a greater relative abundance of high-PHB strains. Treatments sampled after the first year of soybean following 5 years of a non-host crop showed a decreased relative abundance of high-PHB strains, compared to treatments sampled after long intervals without soybean. The latter result is consistent with the hypothesis (not tested directly here) that some high-PHB strains were “sanctioned” by plants as less beneficial. Our results suggest that rhizobia strains with the tendency to allocate more C to N fixation at the expense of PHB accumulation may be less likely to persist where soybean is grown infrequently or where soil conditions make PHB particularly valuable. However, with typical 2-year rotations in Minnesota, differences in PHB storage are unlikely to have a major effect on the relative survival of strains.

Composts are increasingly used to enhance soil health and agricultural sustainability, but their impacts on nitrogen (N) availability are unclear. In a 3-year field experiment with subsurface drip-irrigated tomatoes (Solanum lycopersicum), we investigated the effects of food waste and green waste co-compost (FW) and green waste compost (GW) on yield, nitrogen-use efficiency (NUE), nitrate leaching potential, and fertilizer N retention. Various combinations of compost rates (0, 9, or 18 t ha⁻¹) and fertilizer N levels (0%, 70%, 85%, and 100% of the recommended rate) were examined, and ¹⁵N-labeled fertilizer was used. In Years 2 and 3, FW and GW either maintained or increased crop yield compared to no compost when 0% or 70% N was applied; however, lower NUE was observed in the compost treatments compared to no compost when 70% or 85% N was supplied. Postharvest, FW showed greater fertilizer N retention in topsoil than GW and controls, while no difference in nitrate leaching potential was found among treatments, except for Year 2 during which FW exhibited the lowest potential. These results suggest that compost can immobilize fertilizer N, reducing its uptake by crops but enhancing soil retention, potentially acting as an N source or a primer of soil N mineralization. Future research is needed to assess compost's impact on N gaseous emissions and distinguish its dual roles as an N source and a soil N mineralization primer. This is essential for developing nutrient management guidelines to minimize N losses.

Grazed pastures supporting ruminant livestock have not been well characterized for soil health condition. However, growing interest in holistic management of compromised watersheds suggests that grazing lands deserve more attention for their capacity to provide ecosystem services. Relatively little is known about how grazing management affects soil aggregation and other surface-soil properties on private lands in the eastern United States. This study investigated the effects of land use (conventional-till cropland, no-till cropland, grassland, and woodland) and pasture management characteristics on soil aggregation, bulk density, sieved soil density, total soil N, and soil-test biological activity on 31 private farms distributed across the western half of Virginia. Soil stability index followed the order (p < 0.05): conventional-till cropland (0.60 mm mm⁻¹) < no-till cropland (0.78 mm mm⁻¹) < woodland (0.85 mm mm⁻¹) = grassland (0.89 mm mm⁻¹). Surface soil characteristics improved with pasture age due to organic matter recycling from residual forage mass and animal excreta. Increases in total soil N and soil-test biological activity helped create water-stable aggregation and reduce soil bulk density. Soil stability index was optimized with moderate stocking rate of 0.5–1.1 Mg live weight ha⁻¹. Stocking method did not affect soil aggregation or bulk density. Soil stability index declined with increasing N fertilization rate. Soil aggregation characteristics were generally not affected by organic amendment, quantity of hay fed on farm, or occasional hay harvest from pastures, likely because aggregation was high across management variables. Well-managed grazed pastures in Virginia are creating desirable conservation agricultural land uses to protect watershed quality.

Ranking the contribution of genotype, environment, and management (G × E × M) on maize's economic optimum nitrogen fertilizer rate (EONR) variability could improve understanding and predictability of EONR. We performed a simulation experiment using the Agricultural Production Systems sIMulator model with the objectives to (1) rank the effects of 24 individual G × E × M factors on the magnitude and interannual variability of the EONR across the US Midwest and (2) investigate the impact of G × M factors on the EONR variability under present and future climate scenarios. Results indicate that genetics (27%), management (31%), and environmental conditions (41%) each influence the EONR variability. Within these broad categories, the top three individual factors impacting the EONR were interannual weather variability, crop radiation use efficiency, and the soil inorganic N carryover from the previous year. The G × E × M factors influenced the yield response to N fertilizer in different ways. Soil-related factors (e.g., organic matter and residual nitrate) influenced grain yields at the low N rates, while management factors (e.g., planting date and density) influenced yield at all N rates. Combining increases in plant density and changes in genetics synergistically increased the EONR by 15% from baseline. Future climate scenarios without adaptation decreased the EONR and yield loss, but crop adaptation was buffered against the negative climate change impacts. We concluded that 59% of the annual EONR variability is manageable (due to genetics and management) and that G × M factors could buffer climate change's negative effects on crop production. Present results can inform experimental research on N fertilizer and N rate decisions.