Agronomy Journal最新文献

Optimizing phosphorus (P) application in corn (Zea mays L.) silage production systems to align with crop P requirements while sustaining soil test P (STP) levels can help mitigate environmental risks and enhance farm profitability. The objectives of this study were to characterize P balances of corn silage fields in New York, their drivers, relationships between P balances and field STP and nitrogen (N) balances, as well as the impact of manure application practices on balances. Field-level balances (supply–uptake) for P and N were derived for 994 field observations across eight dairy farms and 5 years. On average, P balances were low (11 kg P ha⁻¹) with a wide range across farm averages (−11 to 30 kg P ha⁻¹). Across farms, P was applied at higher rates to fields with adequate STP than to lower STP fields, indicating potential opportunities for reallocation of P within farms. Phosphorus balances were positively related to N balances. Manure nutrient utilization indicated that N-based applications would lead to large positive P balances in all farms. Phosphorus-based manure applications could cover on average 51% of corn N requirements under current farm manure application practices. This could be increased up to 85% when maximizing the utilization of manure inorganic N. Management alternatives to prevent excessive P balances include improving diet formulation to reduce P excretion, reducing animal density, exporting manure, implementing manure treatment technologies that conserve N and/or remove P, combining appropriate rates of manure and fertilizer, and maximizing manure inorganic N utilization in field applications.

Consistent increases in soybean (Glycine max L.) grain yields over the past decades in Illinois have co-increased P demand, but—as for many US Midwest states—P fertilizer recommendations are outdated. We evaluated soybean grain yield, P uptake and removal with grain harvest, and P use efficiency in 4 site-years of field trials on Argiudolls-Endoaquolls and Fragiudalfs-Hapludalfs under annual P application treatments of source (monoammonium phosphate [MAP], diammonium phosphate [DAP], triple superphosphate [TSP]), rate (partial vs. full maintenance rate), and timing–placement combination (fall and spring broadcast, spring banding). Substituting ammonium phosphate fertilizers, the most commonly used P fertilizers in the US Midwest, with N-free TSP supported similar yields and resulted in similar P removal as hypothesized, while avoiding co-applied N that can be subject to losses primarily via leaching. Soybean yield and grain P removal were unresponsive to rate, timing, and placement even at the partial rate, although banding and spring application may have reduced N loss risk for MAP and DAP. Given the challenges in accurately estimating P removal rate by grain harvest due to the variability in yield and grain P concentrations across years, assessment of P use efficiency should focus on long-term balance between fertilization (input) and crop removal (output).

Nitrogen and genotype play vital roles in modulating plant disease resistance. Maize (Zea mays L.) rough dwarf disease (MRDD) is a global viral disease that has caused serious yield losses. However, it is not clear how nitrogen and genotype interact to affect MRDD. We conducted field experiments in 2011 and 2013 to investigate the MRDD incidence, yield, and yield loss rate of 59 maize hybrids under high nitrogen (HN) and low nitrogen (LN). Compared with HN, the MRDD incidence and yield loss rate of S-sensitive hybrids (nitrogen significantly influenced MRDD incidence in susceptible genotypes) could be significantly reduced by 50.6% and 35.5%, respectively, in LN without compromising maize yield. In contrast, R-insensitive types (resistant hybrids in which MRDD incidence was unresponsive to nitrogen treatment) could maintain high MRDD resistance and yield at HN. US hybrid 78599 and inbred line Dan340 were the main parental resources of the resistant genotypes, and inbred lines Huangzao4 and Ye478 were the main parental resources of the susceptible genotypes. The physiological mechanism leading to increased MRDD incidence was thought to be higher nitrogen concentrations in the stalks. This study provided theoretical support for using reasonable nitrogen management to control MRDD and breeding MRDD-resistant maize hybrids.

Maintaining soil organic carbon (SOC) is critical for global food security as it is essential for soil functions that sustain crop yields. There has been an increase in predictive soil mapping, which when combined with extensive crop yield datasets, enables a better understanding of crop yield and SOC relationships. This study focused on updating maps of SOC content in Saskatchewan using recently digitized historical SOC datasets and predictive soil mapping, and using the maps to examine the relationship between SOC and crop yield. A database of 5014 SOC values was used to map SOC contents using a Random Forest model and a range of environmental covariates. The final SOC model had a R² of 0.48, root mean square error of 0.98%, concordance correlation coefficient of 0.67, and a bias of 0.12%. The relationship between mapped SOC values and crop yield data, with 100,000–200,000 records depending on crop type, was then assessed using a linear mixed effects model after normalizing the data by rural municipality to remove broad-scale climate effects. Overall, an increase in SOC by 1% led to an increase on average of 263 kg ha⁻¹ for wheat (Triticum aestivum L.), 293 kg ha⁻¹ for barley (Hordeum vulgare L.), 133 kg ha⁻¹ for canola (Brassica napus L.), and 135 kg ha⁻¹ for field peas (Pisum sativum L.). These results show that increasing SOC was associated with greater yields for four major crops in Saskatchewan, with the largest gains occurring when the initial SOC contents are lower.

Harvest losses caused by the low height of the lowest pods (LPH) are a significant issue in soybean cultivation. Minimizing these losses requires identifying management, physiological, and agronomic factors that interactively modulate LPH. Four studies were conducted to examine the relationships among soybean LPH, node and internode features, and light quality under different management practices. These practices included population density (19, 31, and 43 plants m⁻²) and row width (equidistant, 25, 51, and 76 cm), relative maturity (maturity group [MG] 0.8, MG 2.1, and MG 2.8), mulch color (gray bare soil, red mulch, and white mulch), and timing of stand reduction (V1, R3, R4, and R5 growth stages). An increase in population density from 19 to 43 plants m⁻² led to an average increase in LPH of 28%, from 11.9 to 15.3 cm. LPH was not influenced by row width. Later maturing cultivars demonstrated the highest potential for enhancing LPH, with late AG2802 having a higher LPH (18.8 cm) than early AG0803 (12.4 cm). Data indicated that the elongation of internodes 10, 11, and 12, along with changes in the red to far-red light ratio beneath the canopy, plays a pivotal role in determining the location of the lowest pods. Moreover, LPH is established around the R3 growth stage. Nevertheless, further investigations are warranted to gain a better understanding of how these parameters, individually and collectively, influence LPH in soybean.

Accurate predictions of herbage biomass are important for efficient grazing management. Small-scale farms face challenges using remote sensing technologies due to insufficient resources. This limitation hinders their ability to develop machine learning-based prediction models. An alternative is to adopt less expensive measurement methods and readily available data such as weather data. This study aimed to examine how different temporal aggregations of weather data combined with compressed sward height (CSH) affect the prediction performance. We considered weather features based on different numbers of weather variables, statistical functions, weather events, and periods. Between 2019 and 2021, data were collected from 11 organic dairy farms in Germany. Herbage biomass exhibited high variability (coefficient of variation [CV] = 0.65). Weather data were obtained from on-farm and nearby public stations. Prediction models were learned on a training set (n = 291) and evaluated on a test set (n = 125). Random forest models performed better than models based on artificial neural networks and support vector regression. Representing weather data by a single feature for leaf wetness reduced the root mean square error (RMSE) by 12.1% (from 536 to 471 kg DM ha⁻¹, where DM is dry matter) and increased the R² by 0.109 (from 0.518 to 0.627). Adding features based on multiple variables, functions, events, and periods resulted in a further reduction in RMSE by 15.9% (R² = 0.737). Overall, different aggregations of weather data enhanced the accuracy of CSH-based models. These aggregations do not cause additional effort for data collection and, therefore, should be integrated into CSH-based models for small-scale farms.

Potassium (K) is an essential macronutrient that determines crop productivity and promotes crop growth under conditions of abiotic stress. In this review paper, peer-reviewed publications (experimental data) dealing with the role of K administration in the control of cereals growth under drought stress are reviewed and analyzed. Most published research on the impact of K administration on cereals growth under drought focuses primarily on bread wheat (Triticum aestivum L.) and maize (Zea mays L.), along with barley (Hordeum vulgare L.), rice (Oryza sativa L.), pearl millet (Pennisetum glaucum L.), and sorghum (Sorghum bicolor L.). The beneficial effect of K on cereals growth under drought has been related to maintenance of water balance in the crops, increased activity of the antioxidant enzymes, increased synthesis of osmolytes, contribution to a low sodium (Na)/K ratio, extension of the grain filling period, and increase of the grain growth rate and weight. Moreover, K was suggested as a possible way for enhancing nitrogen use efficiency (e.g., bread wheat and sorghum). Nevertheless, the beneficial effect of foliar applied K in dried soil during grain filling differed among bread wheat cultivars, highlighting that the effect of K supply on grain weight may differ depending on application method, that is, administration through the soil or the foliage. Overall, K supply could be exploited as a tool for increasing cereal tolerance to drought, for example, by considering late administration of K through top dressing applications or foliar sprayings. Yet additional information is needed on the rate and the K form, along with the most suitable growth stage for application. Such information could help agronomists develop strategies for high-quality cereal production in stressful environments.

Pre-harvest sprouting (PHS) is a global issue affecting a multitude of crops, including wheat (Triticum aestivum L.). The combination of conducive conditions and a lack of genetic seed dormancy results in the sprouting of intact grain at or prior to harvesting. The initiation of germination synthesizes gibberellic acid resulting in the activation of the alpha-amylase synthesis via a calcium-dependent signal transduction pathway. Alpha-amylase synthesized via this pathway degrades the endosperm, decreasing bread-making quality. A commonly used indicator for bread-making quality is the Hagberg Falling Number. Environmental, phenotypic, genetic, and management factors influence the susceptibility of wheat to PHS. Rainfall, temperature, and relative humidity are commonly associated with PHS. The combination of these conditions results in the greatest severity of PHS. Morphological features such as awns and epicuticular waxes may increase the quantity of rainfall retained against the grain, increasing the risk of PHS. Similarly, management factors such as fertilization and fungicide application also may increase the risk of PHS occurring. Further research is necessary to understand the mechanisms and impact of management factors on PHS. Additionally, further investigations are needed to explore how environmental and genotypic interactions affect PHS susceptibility.

As competition for limited water resources in the western United States and other arid and semiarid regions intensifies, there is a need to provide alfalfa (Medicago sativa) growers with knowledge of how water-optimizing practices impact alfalfa nutrient use. The objective of this research was to evaluate how three water management strategies, and their interactions, influence alfalfa K and P concentration, uptake, uptake efficiency, and internal use efficiency. Alfalfa cultivars, deficit irrigation, and irrigation technologies were tested at two sites in Utah during 2020–2021. A single drought-tolerant (DT) cultivar (‘Ladak II’) was compared to a conventional alfalfa cultivar specific to each location. Four irrigation rates (100%, uniform reductions of 25% and 50%, and a targeted reduction of ∼50%) were nested within five pivot irrigation technologies. Few alfalfa K and P nutrient dynamics responded to the interactions of cultivar and rate or technology. Nutrient responses to the irrigation technologies were strongly associated with the technology effect on alfalfa yield such that uptake efficiency was sometimes greater with low-elevation sprinkler technologies. The K and P responses to deficit irrigation were most pronounced at the uniform or targeted 50% irrigation rate. Alfalfa cultivar had the least impact on alfalfa K and P dynamics, and the DT cultivar never improved uptake or efficiencies. These results indicate that few adjustments in K and P management may be needed with the three water optimization approaches evaluated in this study. The most notable is that K and P fertilizer input can likely be reduced with severe deficit irrigation.

Interest in cotton seed size and seeding density exists due to increased seeding cost and overall decreased seed size of cotton varieties. An experiment was conducted in 2019 and 2020 in Jackson, TN, Starkville, MS, and Brooksville, MS, to determine the impact of seed size, seeding density, and variety on cotton plant development and yield. Early-season seedling vigor was impacted by seeding density and seed size. Larger seeds and higher seeding densities produced the greatest seedling vigor. Fresh weight biomass was also impacted by seed size, as larger seed produced greater fresh and dry cotton plant biomass when pooled over seeding density and variety. The greatest seed cotton yields were obtained from planting larger seed, higher seeding densities, and from ‘DP 1646 B2XF’. Cotton variety and seeding density influenced financial returns and fiber quality. ‘NexGen 3406 B2XF’ planted at 148,200 seeds ha⁻¹ resulted in the lowest micronaire. Net returns were not influenced by seed size or seeding density; therefore, depending on seed costs, increasing seeding densities may not be beneficial.