Agronomy Journal最新文献

Crop rotations on dairy farms in eastern Canada nowadays include fewer perennial crops and more nitrogen-demanding annual crops. This study compared, over a 7-year rotation cycle, the agronomic performance and the legacy effect of six crop rotations that varied in crop types (perennial or annual) and nutrient sources (mineral or organic). Crop yield and nutritive value were determined on a yearly basis and cumulated over the rotation cycle. The legacy effect was assessed by growing forage corn (Zea mays L.) in year 6 and soybean [Glycine max (L.) Merr.] in year 7 in all rotations. The legacy effect of perennial forage crops manifested with a 78% lower weed biomass at harvest of forage corn in year 6 and a 14% greater soybean yield in year 7. A greater soil-derived corn nitrogen uptake in year 6 after repeated slurry applications indicated a modest legacy effect of organic fertilization on soil N supply capacity. The presence of perennial forage crops or the use of organic fertilization did not affect cumulative dry matter or crude protein production over the 7-year rotation cycle. The addition of alfalfa (Medicago sativa L.) in mixture with perennial grasses improved forage yield (+26%) and nutritive value (greater digestible energy and crude protein concentration) in post-seeding years. In perennial-based rotations, adding alfalfa to grasses resulted in greater dry matter (+22%) and crude protein (+46%) productions over the 7-year cycle despite a fourfold reduction in N fertilizer input, attesting to the high N use efficiency of perennial legume-based cropping systems.

Accurate prediction of paddy rice (Oryza sativa L.) phenology is necessary for informing field management and improving yield. There exist different ways, including physics-based, data-driven, and hybrid approaches, to make rice phenology prediction. However, few studies have investigated the performance of the above three modeling approaches. This study compared the performance of a physics-based model (ORYZA), a data-driven model (using the distributed random forest [DRF] technique), and a hybrid model (an integration of the ORYZA model and DRF-based rice development rate parameter estimates) for rice panicle initiation and flowering date prediction. The feature importance analysis method was introduced to quantify the relative importance of input variables for rice phenology prediction. The results showed the following: (1) Rice genotypes and cultivation patterns resulted in poor performance of the ORYZA model for phenology prediction, whose root mean square error (RMSE) ranged from 6.01 to 8.12 days, and the coefficient of determination (R²) ranged from 0.06 to 0.24. (2) The hybrid model, whose RMSE ranged from 3.11 to 3.66 days, improved the ORYZA model but still underperformed the data-driven model, whose RMSE ranged from 2.44 to 2.57 days. The worse performance might be attributed to the poor prediction accuracy of the model parameter, development rate in the juvenile phase, where the mean absolute percentage error was 0.286. (3) Satellite-based vegetation indices, leaf area index, and evapotranspiration played an important role in determining the predictive capacity of the DRF technique for ORYZA model parameters and rice phenology. Overall, we suggested using data-driven models for accurate rice phenology prediction.

Soybean (Glycine max (L.) Merr.) breeding programs face challenges in evaluating large progeny populations, which is labor- and resource-intensive. This study addresses these challenges using high-throughput phenotyping and machine learning (ML) models to predict phenotypic traits in soybeans. We developed and validated ML models using vegetation indices and canopy images from aerial imagery. A total of 275 soybean genotypes were characterized across two environments and management practices. A total of 11 classical traits were measured, and five vegetation indices were calculated from aerial images at different growth stages. ML algorithms, including support vector machine for regression, random forest (RF), multilayer perceptron (MLP), and adaptive boosting, were employed. Additionally, convolutional neural networks with transfer learning were used to extract features from the images. Significant correlations were found between agronomic traits, vegetation indices, and canopy characteristics. The high heritability of the red–green–blue vegetation index and green leaf index (mean broad-sense heritability of 0.56) compared to other RGB-based indices indicates their potential usefulness in genetic evaluations. Advanced ML techniques, particularly transfer learning with ResNet 50, enhanced the prediction of phenotypic traits such as days to the R7 growth stage (DR7) and plant height at maturation (PHM). The integration of ResNet 50 with RF achieved a prediction accuracy of 0.64 for DR7, while ResNet 50 with MLP reached an accuracy of 0.68 for PHM. These findings highlight the potential of these techniques to improve decision-making in soybean breeding. Lastly, principal component analysis identified genotypes with desirable trait combinations, advancing soybean development.

This article presents an analysis of the response of the annual yield of rainfed maize crops in the Argentine Pampas Region to five drought indices (standardized precipitation index [SPI], standardized soil moisture index [SSMI], standardized evapotranspiration deficit index [SEDI], standardized precipitation-evapotranspiration index [SPEI], and standardized precipitation actual evapotranspiration index [SPET]) at different time scales (from 1 to 12 months). The idea of this work was to find the drought indices that best correspond to the interannual variability of maize yield and to use them in monitoring agricultural droughts. For this purpose, we correlated maize yields with different indices across all their time-scales. We selected the indices with the best overall response to yields and then performed a principal component analysis. The findings revealed that the SEDI and the SPEI displayed the highest correlations with maize yields, followed by SPI, while SPET exhibited the lowest correlations. Principal component analysis demonstrated a heightened predictive capacity of drought indices between February and March, particularly at 7–8-month scales, alongside the widely used 3-month temporal scale for monitoring agricultural droughts. The stronger correlations exhibited by SEDI and SPEI, which incorporate atmospheric evaporative demand into their calculations, suggest that water availability is not the sole meteorological factor influencing drought impacts. Atmospheric demand considers temperature and air humidity, factors that intensify plant stress conditions. These findings supported the importance of considering flexible drought indices adapted to different time-scales for accurate monitor of agricultural droughts, which can enhance planning and risk mitigation in crop production in the Pampas Region and beyond.

The last 20 years have been a period of significant advancement in the tools available for remote sensing of soybean [Glycine max (L.) Merr.] in terms of price, ease of use, quality of information provided, and range of available research applications. This review article posits that now is an appropriate time to reflect on the previous two decades of research effort devoted to remote sensing of soybean to gain an appreciation for how far the field has come, while also acknowledging how much work remains to be performed. Structured by field management activities, this review is based on selected works culled from a broad search. These works contributed meaningful knowledge specific to soybean or elucidated key points not presented in those more intentionally focused on soybean. While there were many successes in the varied applications of remote sensing in soybean research, taking this 20-year perspective also exposed areas of unmet expectations. Advances in knowledge are hampered by systemic challenges with inconsistent results and confounding factors imposed by research settings. There is potential to address these challenges by tempering expectations for what is possible and addressing reporting standards and data needs, specifically related to machine learning. The future is bright, but a concerted community effort is needed to continue to advance the state of knowledge into the next 20 years.

Industrial hemp (Cannabis sativa L.) is an ancient crop used throughout history for fiber, oilseed, and therapeutic compounds. Hemp varieties were cultivated across diverse environments in the United States, but knowledge of those agronomic practices along with genetic resources was lost during a period in which cultivation of cannabis was prohibited. Therefore, regional performance evaluations of hemp varieties for crop performance coupled with scientific communication of outcomes to the public are crucial for hemp's development as an agricultural commodity. Objectives for this research were to evaluate relative yields of industrial hemp varieties grown across the United States and link their suitability for commercial production across locations. A national collaboration established variety trials containing seven industrial hemp varieties planted across 14 locations (36°–48° N latitude and 72°–110° W longitude) over a 3-year period. Crop dry straw yield and seed yield increased from the averages of 1600 and 700 kg ha⁻¹ in Year 1 to 2400 and 1150 kg ha⁻¹ in Year 2, and 3050 and 815 kg ha⁻¹ in Year 3, respectively. The varieties Anka and X-59 performed best in Vermont and Virginia, where seed yields consistently exceeded 1100 kg ha⁻¹; however, no single variety performed above average across all sites. Overall, this assessment identified two industrial hemp varieties suitable for commercial production in specific sites and highlighted the importance for hemp breeders to investigate variety × location × year interactions when developing improved varieties to best capture site-specific productivity.

Four major leaf shapes exist in tetraploid cotton (Gossypium hirsutum L.): normal, sub-okra/sea-island, okra, and super-okra. The majority of upland cotton (G. hirsutum) varieties in the United States possess a normal leaf shape. However, the other three leaf shapes were reported to show a few production advantages such as accelerated flowering rates, early maturity, reduced lint trash and boll rot, and increased pest resistance. In this study, we evaluated the leaf-shape isolines LA-213-okra, LA-213-normal, LA-213-super-okra, and LA-213-sub-okra for physiological traits related to yield and performance and identified the isolines associated with superior physiological mechanisms. During flowering stage, narrow leaf shape isolines, LA-213-okra and LA-213-super okra, showed 6%–19%, 0%–15%, and 3%–73% greater chlorophyll index, quantum efficiency of photosystem II, and photosynthetic rate, respectively, than the LA-213-normal and LA-213-sub-okra isolines. Further, the water use of LA-213-super-okra was 12%–22% lower than that of the other three leaf shape isolines. With superior physiological performance, the okra and super-okra leaf shapes offer useful trait variation for cotton breeding and variety development.

In 2017, cotton (Gossypium hirsutum L.) leafroll dwarf virus (CLRDV) was first reported in the United States. One CLRDV inoculum source includes the previous year's cotton stalks; hence, destroying cotton stalks could be effective for CLRDV management. However, tillage-intensive stalk destruction methods (SDMs) can degrade southeastern soils, but a cover crop may provide short-term benefits and reduce CLRDV incidence. Therefore, we examined three SDMs (Tillage, Pull, Mow) across two cover crop levels (no cover and rye [Secale cereale L.]/clover [Trifolium incarnatum L.] mixture) and two cotton varieties to determine how cotton growth, soil penetration resistance (PR), and two CLRDV incidence sample times (pre-harvest and post-harvest) were affected across six environments during the 2021 and 2022 growing seasons. None of the SDMs affected any factors examined in this experiment, except soil PR and cotton yield. The Pull and Mow SDMs both increased soil PR compared to the Tillage SDM. An 8% yield increase (Pull > Mow) was observed, but the Tillage SDM yield did not differ from Pull or Mow SDMs. The rye/clover mixture also increased soil PR. Although cotton stands were 15% greater with no cover crop, subsequent cotton yield and fiber quality were minimally affected by cover crops. The rye/clover mixture increased post-harvest CLRDV incidence, and cotton yields were equal between cover crops. Pre-harvest CLRDV incidence probability was 0.23, but post-harvest CLRDV incidence probability was 0.71. Continuing to identify and evaluate cultural practices that reduce CLRDV incidence is imperative to prevent negative impacts.

Dollar spot (caused by Clarireedia spp.) is the most commonly occurring turfgrass disease on golf courses in North America, and current disease control programs rely on frequent fungicide applications. The escalating occurrence of fungicide resistance in Clarireedia spp. populations, coupled with the reduction of the annual kilograms active ingredient applied per hectare for some fungicides, emphasizes the need for alternative management strategies. The use of oxygenated or ozonated water treatments has been effective as a component of an overall plant disease management strategy. In field and growth chamber-controlled environment trials, the impacts of oxygenated and ozonated nanobubble water treatments were evaluated for turf quality and dollar spot control in seashore paspalum (Paspalum vaginatum Swartz). Despite generating relatively high levels of dissolved oxygen (40 mg L⁻¹) or ozone (ca. 8 mg L⁻¹) in water treatments through nanobubble aeration in all trials, these treatments did not cause damage to seashore paspalum tissues, but were unsuccessful in controlling dollar spot. Additionally, tests comparing two different application methods (soil drench versus foliar spray) for all treatments suggested that the application method had no effect on treatment efficacy. Overall, oxygenated and ozonated nanobubble water treatments did not adversely affect seashore paspalum turf quality and were ineffective in suppressing dollar spot in field and growth chamber trials.

Canola (Brassica napus) acreage increased in Western Canada in recent years, leading to rotations with fewer break years between canola plantings. Field trials suggest that frequent plantings of canola reduce canola yields. However, there is considerable disagreement about the magnitude and persistence of these effects. We analyze the effect of rotational practices on canola yields in Saskatchewan using over 20 years of observational data, representing 61% of canola hectares in the province. We examine how the impact of rotations varies across time, soil zone, soil moisture conditions and the distribution of yields. We regress canola yields in Saskatchewan on the share of land that was planted with particular crops in previous years, using a battery of covariates and fixed effects to address potential bias in the model. After including these fixed effects, we cannot reject the hypothesis that there is no sample selection bias. We use an unconditional quantile estimator to investigate how rotations affect different deciles of the yield distribution. Our analysis confirms that crop rotations significantly influence canola yields, albeit more modest than field trials suggest. We find a 7.5% yield reduction when canola follows canola, compared to cereals, with this penalty persisting for 4 years but diminishing in magnitude with each additional year. The adverse effects of consecutive canola plantings are more pronounced in wetter regions and at lower yield deciles. Conversely, canola yields are higher when planted after pulse crops (as opposed to after cereal crops).