Agronomy Journal最新文献

We applied the reaction norm concept to assess the stability and plasticity of rice (Oryza sativa L.) grain quality traits, specifically for whole milling, chalk, and length. For that, we used 15 days of planting trials from 2021 and 2022, which included 19 commercial varieties evaluated in randomized complete block design with three replications. The analysis was conducted in two phases: first, we obtained adjusted means for each line in each trial, followed by a joint analysis to calculate broad-sense heritability and genotype-by-environment (G × E) interaction. Next, we used Finlay–Wilkinson's regression, genotype-genotype × enviroment (GGE) biplot, and environmental covariates to dissect the G × E. All analyses were performed in R using SpATS, sommer, statgenGxE, metan, EnvRtpe, caret, and snpReady packages. Furthermore, to better understand the G × E effect, we employed linear and exponential regression models. Our results revealed a higher G × E interaction for whole milling and chalk, while grain length showed a lower interaction. Notably, the specific planting days were more critical for quality traits than planting windows. We identified key environmental covariates: potential evapotranspiration and relative humidity from pre-flowering to flowering for whole milling; vapor pressure deficit and relative humidity from flowering to post-flowering for chalk; and wind speed, potential evapotranspiration, and relative temperature anomaly during various growth stages for grain length. These covariates explained ∼76% of the total variation in these traits. Reaction norm curves provided insights into genotype-specific responses to environmental factors, and the narrow-sense heritability of reaction norm components (intercept and slope) revealed “new” heritable traits to be used for stability and adaptability selection.

Despite improvements to grain yield in maize (Zea mays L.) during the Green Revolution, maize remained predominantly tall unlike other major cereals such as wheat (Triticum aestivum L.) and rice (Oryza sativa L.). Although short-statured maize (SSM) has been a topic of interest for many decades, historical efforts to introduce it commercially have remained unsuccessful. Commercial interest in SSM has recently returned mainly due to their lodging resistance, potential for high-density planting, and better in-season accessibility for pest and disease management. In this article, we conduct a systematic review to examine the limitations of past SSM hybrids in regard to genetic backgrounds and traits associated with agronomic performance, and renewed interest in SSM. Our objectives are to (i) identify the limiting factors of early SSM to understand why maize lagged behind in adopting dwarf traits compared to other cereal crops, and (ii) analyze the drivers behind renewed interest in SSM to assess its agronomic significance and potential role in future crop improvement strategies. This study analyses 45 studies and 17 patents and patent applications published between 1965 and 2024. Linear regression models were used to analyze changes in yield, height, harvest index, and lodging over time in both short and tall hybrids. Based on our review results, traits used to reduce height in maize introduced undesirable defects in plant reproductive development and architecture in early SSM hybrids. Modern improvements in SSM hybrid performance are due to less severe dwarfing traits or through concentrating trait effects away from reproductive structures.

Weeding robots are expected to decrease herbicide use on conventional farms and reduce manual labor on organic farms. A multi-objective linear programming model was used to compare the economic, environmental, and social performance of robotic and non-robotic weed control in conventional and organic sugar beet (Beta vulgaris L.) production in Bavaria, Germany. On the conventional farm, the weeding robot generated a mean gross return of €58,612 year⁻¹ compared to €57,728 year⁻¹ when using herbicide spraying. However, the mean return on total costs for the weeding robot was negative (€−2750 year⁻¹) and substantially lower than the €8663 year⁻¹ achieved with herbicide spraying. In organic farming, this technology was more profitable than non-robotic mechanical weeding, generating a mean gross return of €73,098 year⁻¹ and a mean return on total costs of €10,373 year⁻¹. The corresponding figures for non-robotic mechanical weeding were € 59,176 and €7,577 year⁻¹. The carbon emission intensity of sugar beet was comparable between weed control strategies on the conventional farm and marginally lower for robotic weeding on the organic farm. On both farms, autonomous mechanical weeding used more skilled labor due to routine supervision, field-to-field transport, and human intervention requirements. Higher skilled labor time with robotics negatively affected farmers’ work–life balance. Investment cost, supervision and human intervention requirements, technology specialization, and logistics of field operations were identified as the main barriers to adoption of the tested weeding robot. These barriers should be prioritized when developing future autonomous farm equipment.

Providing accurate and precise nitrogen (N) fertilizer recommendations remains a significant challenge. To arrive at a recommendation, researchers traditionally conduct hundreds or thousands of field experiments measuring the grain yield response to varying N fertilizer rates. A statistical model is then fit to the data to calculate the agronomic optimum nitrogen rate (AONR)—the fertilizer N rate that maximizes crop yield. We evaluated the impact of excluding individual fertilizer rates on the AONR estimate and its precision using a mixed-effects quadratic-plateau model on a previously published dataset of 49 maize (Zea mays L.) fertility trials with eight N fertilizer rates. When excluding the control treatment (0 kg N ha⁻¹), the AONR deviated from the AONR calculated using all eight rates from 0 to 59 kg N ha⁻¹ for individual sites—a larger change than when any other N rate was excluded. Excluding the control treatment also caused the greatest loss in the AONR estimate precision, with an average standard error increase of +43% without the control compared to +23% for other N rates. Furthermore, our simulations confirmed these findings and showed the largest losses of accuracy and precision when the control treatment was excluded. These trends in bias and precision persisted with different simulated experimental designs. Our results demonstrate the importance of including the control treatment in N fertility trials designed to estimate the AONR. Therefore, we recommend including a control treatment for a more precise N fertilizer recommendation program, especially in on-farm research.

Future climate change is expected to have serious effects on crop production in northern Shanxi province, China. However, systematic research on the effects of future climate change on the productivity and economic returns of cropping systems is limited. Based on field observations, this study validated the Agricultural Production Systems sIMulator (APSIM) and conducted long-term scenario simulations in northern Shanxi to assess the productivity/economic return of several common cropping systems in six clusters divided by climate conditions. Results indicated the following: (1) The APSIM validation during 2022–2023 presented generally acceptable results, with normalized root mean square error of 6.4%–28.8% and Willmott agreement index of 0.800–0.978 in simulating yield and biomass. (2) Future projections offered higher yields, economic returns, higher rate of actual yield accounting for potential value (RAY), and higher rate of actual economic return accounting for potential value (RAE). (3) The RAY was higher in food crops (87.7%–99.9%) than in forages (61.3%–87.1%), while the rotations with maize (Zea mays L.) produced lower actual yield and RAY. (4) The highest actual economic returns were observed in continuous maize (34.15–45.23 × 10³ RMB 2-year⁻¹), whereas crop–forage rotations were predicted to show lower RAE. (5) High precipitation condition primarily resulted in high actual yield/economic return, while low temperature and radiation were important factors enhancing potential values. Generally, crop–forage rotations were mostly recommended in clusters with at least moderate precipitation, whereas maize and potato (Solanum tuberosum L.)-based systems were expected to thrive under high- and low-precipitation conditions, respectively.

Sugarcane (Saccharum spp. L.) is a globally important crop, and foliar fertilization can enhance plant growth, yield, and stress tolerance. However, the use of multi-nutrient complexes remains underexplored. Here, we investigated the effect of two multi-nutrient foliar fertilizer applications at vegetative stage (N, K, S, B, Cu, Mn, Mo and Zn) and at maturation stage (P, K, Mg, S and B) in 13 field experiments in the early, mid-late, and late sugarcane harvest seasons. Four treatments were compared: (i) no application of multi-nutrient foliar fertilizer (control), (ii) application at the vegetative stage of sugarcane (V), (iii) application at the maturation stage (M), and (iv) application at both the vegetative and maturation stages (VMs). Application resulted in significant gains in all biometric parameters, sugar yield, and theoretically recoverable sugars; the largest increases occurred in VM. Bagasse, straw, and energy production followed the pattern of biometric parameters and increased by averages of 7.9%, 9.7%, and 9.7% compared to the control. In early harvest sugarcane, phosphoenolpyruvate carboxylase, ribulose-1,5-bisphosphate carboxylase-oxygenase, superoxide dismutase, catalase, ascorbate peroxidase, and peroxidase activities and proline content were enhanced in M and VM, whereas hydrogen peroxide (H₂O₂) and malondialdehyde contents decreased in these treatments. In summary, multi-nutrient foliar fertilization at the VMs enhanced sugarcane growth, yield, energy production, and quality across all harvest seasons while improving photosynthetic and antioxidant enzyme activities. The results demonstrate that tailoring multi-nutrient foliar fertilizer application to the specific physiological and nutritional demands of each phenological stage can promote sugarcane development by reducing the production of stress-related molecules and enhancing carbon assimilation.

Meloidogyne arenaria (peanut root-knot nematode [PRKN]) is the most important plant–parasitic nematode on peanut (Arachis hypogea) in the United States. Some PRKN-resistant cultivars are available, but comparisons among these cultivars in the field are needed. The objective of this study was to evaluate cultivars designated as resistant for their influence on PRKN and agronomic performance in fields with PRKN. The resistant cultivars TifNV-HighO/L, TifNV-HG, Georgia-14N, Georgia-22MPR, and ACI N104 were tested without nematicides. These were compared to a susceptible cultivar, Georgia-06G, with or without in-furrow fluopyram nematicide. Field trials were conducted in northwestern Florida (West Florida Research and Education Center [WFREC]) in 2023 and 2024 as well as north-central Florida (North Florida Research and Education Center [NFREC]) in 2024. PRKN indicators were low and unaffected by cultivars at harvest at WFREC in 2023 and midseason at NFREC. For all other seasons and sites, TifNV-HighO/L, TifNV-HG, Georgia-14N, and Georgia-22MPR substantially reduced PRKN abundances from roots at midseason and in soil at harvest as well as root and pod galling at harvest relative to Georgia-06G. ACI N104 typically supported greater PRKN abundances than other resistant cultivars, but generally reduced galling relative to the susceptible cultivar. In-furrow fluopyram with Georgia-06G was not effective at reducing PRKN abundances or improving yield. TifNV-HG was consistently a high-yielding cultivar, Georgia-06G was generally a low-yielding cultivar, and the other cultivars varied by site-year. In summary, cultivars highly resistant to PRKN are available, but even when PRKN is present, yield benefits of resistant relative to susceptible cultivars can vary based on environmental conditions.

Nitrogen (N) fertilizer recommendations in crops are affected by various factors including environmental and management practices. Effective N management should sustain maize (Zea mays L.) yield while minimizing nitrate (NO₃-N) leaching. This systematic review synthesized 132 peer-reviewed studies to quantify how recommended N rates (RNRs) vary across environmental and management factors, and to evaluate associations between RNRs, yield, and NO₃-N leaching. RNRs were grouped into quartiles derived from the empirical distribution of the study-level RNR values in the review dataset. RNRs were generally higher (>200 kg ha⁻¹) in subtropical and dry climates, in coarse-textured soils, and under irrigation. Yield responses to RNRs tended to be stronger in temperate climates, in coarse-textured soils, with conventional N sources, under split applications, and with irrigation. Studies conducted in semi-humid climates and in fine-textured soils showed weaker and inconsistent associations. NO₃-N leaching increased with RNRs under irrigation-based studies. Apparent increase in NO₃-N leaching in medium-textured soils likely results from residual N accumulation and post-harvest flushing where rainfall is high. These findings should be interpreted cautiously due to uneven study coverage and heterogeneous methods. Several key gaps remain in the evidence base. First, few studies quantify environment-by-management interactions. Second, NO₃-N leaching is rarely measured, and irrigation amount scheduling is often unreported. Third, paired irrigated-rainfed comparisons are uncommon. Finally, criteria for deriving RNR are inconsistent, and geographic coverage is limited. Research priorities include integrated factorial designs across climates and textures, water-accounted paired designs, concurrent residual-N profiling, long-term trials, transparent RNR definitions, and curated open datasets to enable synthesis.

Soil inundation increases anaerobiosis, slows organic matter decomposition, and affects greenhouse gas production from soils. The objective of this study was to evaluate the effects of natural short-duration recurring inundation on CO₂, CH₄, and N₂O emissions, as well as on labile soil permanganate oxidizable carbon and autoclaved-citrate extractable (ACE) protein contents. We assessed two locations in Ohio separately: (1) in the Northwestern location, hayfields prone to high inundation (N-HIH), low inundation (N-LIH), and non-inundated (N-NIH), and (2) in the Southern location, pasture fields prone to inundation (S-IP) or non-inundated (S-NIP) and non-inundated hayfield (S-NIH). Inundation was not controlled or simulated; rather, we evaluated it as a long-term natural effect. We collected greenhouse gas (GHG) samples in spring, early and late summer, and fall of 2021 and 2022. At Northwestern location, N-LIH and N-HIH emitted less CO₂, likely because of lower organic matter oxidation under more intense inundation. This pattern was less evident in the Southern location, where S-NIP and S-IP generally showed similar CO₂ emissions. Both locations acted as CH₄ sinks, and emissions remained largely unaffected by inundation at either location, suggesting that inundation duration and/or intensity were insufficient to maintain anaerobic conditions. Inundation, however, clearly increased N₂O emissions in both locations, especially during the drying period in early and late summer, after the wetter spring seasons, characterizing a progressive response to inundation. We frequently observed the highest N₂O emissions alongside elevated soil ACE protein levels. Natural recurring short-term inundation increased GHG emissions from hayfields and pastures, mainly by increasing N₂O emissions during post-inundation periods.

Wheat (Triticum aestivum L.) remains a major staple crop, which is vulnerable to abiotic and biotic stresses that can be compounded by climate change. This review assesses the projected effects of climate change on wheat production globally with an emphasis on the Canadian Prairies. The review aims to (i) discuss the projected impact of climate change on the Canadian prairie cropping calendar, (ii) assess the potential impacts of climate change on pest dynamics and abiotic stresses, and (iii) discuss beneficial management practices that can be employed to tackle climate change in wheat production systems. Climate change will potentially shift the current Canadian prairie calendar earlier in the year, potentially increasing wheat yields. The impact of climate change on pests is tied to the cropping calendar and the pest involved. Abiotic stresses, except carbon dioxide, will be aggravated. Beneficial management practices, that is, biostimulants, ultra-early seeding, seeding winter wheat, and cultivar mixtures, are potential strategies to stabilize wheat yield and reduce the yield gap under a changing climate. Biostimulants, although effective, have not been extensively tested for their impact on abiotic stresses in the Prairies. Studies on ultra-early warrant further research to address their effectiveness in Northern prairie regions and the challenges encountered by producers. Although growing winter wheat is an option to escape wetter spring conditions, issues with fall establishment and winter survival must be addressed. Despite the extensive global research on varietal mixtures, a knowledge gap exists regarding their benefits in the Canadian prairie context.