Agronomy Journal最新文献

Winter camelina [Camelina sativa (L.) Crantz] is a potential third crop to diversify maize (Zea mays L.)–soybean [Glycine max (L.) Merr.] rotations in the upper Midwest. Although generally considered a low-input crop, empirical evidence suggests that it responds to added nitrogen (N) fertilization. However, optimum agronomic N rates have not been extensively studied in the region. A study was conducted from fall 2018 to fall 2020 at three locations to assess the effects of N fertilizer application time (all N-rate applied in spring and N-rate split applied in fall and spring) and rates on biomass and seed yield, and quality of winter camelina. Nitrogen application time did not affect yields. Both biomass and seed yields were greatly affected by N rates at all locations. Nitrogen had minimal effects on the oil and protein content of seeds, although greater N rates were associated with a slight decrease in oil content and a slight increase in protein content. The number of branches and silicles per plant varied significantly with N rates in all locations. The seed-to-silicle ratio showed significant differences in two out of three locations. Residual soil N increased with increasing N rates. A fertilization rate of 67 kg ha⁻¹ provided the highest camelina seed yield. While this study has determined the agronomic maximum rate for applied N, further economic analysis could provide comprehensive decision-making for farmers.

Since the dawn of agriculture, food security was improved by replacing hunting with domesticated animals and gathering was replaced with planting seeds in the soil. In many areas, agricultural practices resulted in ecological systems being replaced with domesticated plants and animals. This fundamental process created the food resources needed to build the Great Pyramid of Giza and the Hanging gardens of Babylon. However, these food production systems also contributed to the Irish potato famine, the North America Great Plains dust bowl, and the extinction of many animals. From these spectacular successes and failures, we learned that food and environmental security requires a skilled workforce and that new innovations are often needed to solve complex problems. For example, during the transition from the European Middle Age to the 16th century, European farmers learned that food and economic security was improved by switching from a two-field rotation (one seeded and one fallow or resting) to the Norfolk rotation that consisted of wheat (Triticum aestivum), turnips (Brassica rapa), barley (Hordeum vulgare), and clover (Trifolium). This rotation increased productivity, improved diets, and provided the food needed for the industrial revolution.

Over time, we also learned that sustainable food production requires careful attention to soil and environmental health. For example, a multiyear drought during the 1930's when combined with the plowing of the North America Great Plains led to the “Dust Bowl”. Based on these and other lessons, we hypothesize that to avoid future food and economic insecurity, we need a common vision that considers soil, ecosystem, human, and environmental health (D. E. Clay et al., 2012; Smart et al., 2015).

Building a unified vision is complicated by scientific disagreements, social differences, and a lack of consensus on the fundamental facts. For example, how much land is used to produce annual crops in the North America Great Plains? The answer to this question is complicated by different databases producing different answers (Lark et al., 2015, 2017; Reitsma et al., 2016; USDA, 2020; Center for Spatial Information Science and Systems, 2024) and by research papers that make unvalidated predictions. For example, Rashford et al. (2010) predicted that in the Prairie Pothole region of North America, approximately 12.1 million ha (30 million acres) of grasslands would be converted to cultivated crops by 2011 if the corn (Zea mays) selling price continued to increase. Subsequent analysis showed that even though prices increased, the predicted wide-scale land use changes never occurred (Joshi et al., 2019; Lark et al., 2015; Wright & Wimberly, 2013). The lack of change was attributed to farmers who valued multiple income streams and whose actions w

Later spring termination of fall-planted cover crops can result in more biomass production, which has the potential to improve environmental benefits. However, later cover crop termination can also have the potential to harm cash crops, such as through increases in corn seedling disease. With the objective of better understanding such potential competing interactions, we evaluated the impact of early and late termination timing of two cover crops—cereal rye (Secale cereale L.) and hairy vetch (Vicia villosa Roth.)—on growth, seedling disease, and grain yield of corn in 2021 and 2022 in Nebraska. The total biomass production varied among treatments. Hairy vetch biomass was lower than cereal rye biomass at both termination times in 2021, but not different from cereal rye terminated early in 2022. Radicle root rot severity, a symptom of corn seedling disease, was not affected by cover crops in 2021. In 2022, however, radicle root rot was most severe in cereal rye terminated late treatments. Hairy vetch did not affect radicle root rot severity, regardless of termination time. Late termination of cereal rye resulted in a reduction in corn yield of 15.3% in 2021 and 76.1% in 2022 compared to no cover crop. Corn (Zea mays L.) grain yield was not affected following hairy vetch at either termination timing. Our results suggest that corn planted following late-terminated cereal rye can increase the severity of root rot and decrease corn grain yield; however, planting green into hairy vetch can be a successful option to increase biomass without increasing corn seedling disease or decreasing corn yield.

Soil biological processes are important drivers of crop productivity in agroecosystems. Soil microarthropods play key roles in nutrient cycling and plant nutrient acquisition, though little is known about how these effects manifest in crop production under different organic fertilizer amendments. We explored the interactive effects of microarthropods and fertilizers on crop productivity in two greenhouse experiments: experiment one involved a single Collembola species, and experiment two involved diverse microarthropod communities. Oats were grown as a model crop in both experiments under one of three initial fauna abundance levels (none, low, and high). In both experiments, four organic fertilization treatments were compared: alfalfa green manure, Kreher's Poultry Litter Compost, Chilean nitrate, and a nonamended control. Oat growth and development were evaluated weekly. During each experiment, 48 pots were selected randomly for destructive harvest at two separate times to mimic forage and grain harvest stages. At each harvest, multiple soil metrics (microarthropods, microbial biomass, microbial enzymes, and soil carbon and nitrogen) and plant metrics (biomass, reproduction, and tissue carbon and nitrogen content) were evaluated. Our findings indicated that microarthropods, both single species and diverse communities, stimulated nitrogen cycling and enhanced crop nutrient status. As microarthropod abundance and diversity increased, microarthropods exerted more effects on soil microbial activity. The effects of the microarthropods enhance the breakdown of fertilizers, ultimately making fertilizer choice less important for soil processes and plant nutrient availability. Our findings suggest that microarthropods drove oat production yields primarily through their effects on soil biological processes.

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.