Agronomy Journal最新文献

Brazil can expand cultivated areas without deforestation by restoring degraded pastures and optimizing grain production through off-season crops. This study evaluated eight off-season treatments, including fallow, monocrops, and intercropping combinations, and their effects on maize (Zea mays L.) intercropped with guinea grass (Megathyrsus maximus (Jacq.) B.K. Simon & S.W.L. Jacobs ‘Massai’) in a tropical low-altitude region. The millet (Pennisetum glaucum (L.) R. Br.) + guinea grass treatment produced high aboveground biomass, efficient macronutrient accumulation, reduced soil temperature, and increased maize grain yield and guinea grass productivity compared to other treatments. Overall, the use of off-season crops improves degraded pasture renovation, enhances subsequent summer intercropped maize productivity, and represents a promising and sustainable agricultural practice.

Detecting plant nitrogen (N) deficiency is important for enhancing crop yield and nutrient use efficiency. Leaf color, a key indicator of relative pigment expression and crop N status, can be monitored using red, green, and blue (RGB) under-canopy images of maize (Zea mays L.). This study evaluated RGB indices collected from under-canopy images against applied N rates ranging from 0 to 271 kg ha⁻¹ in maize trials conducted in Iowa, Minnesota, and New York (2019–2020). Digital RGB camera images were processed for leaf identification, filtered, and analyzed for RGB band averages. Results showed strong correlations between RGB indices and the applied N rates, especially in indices involving the B band, like (R − B)/(R + B), with R² values up to 0.75 and p < 0.001. Power analysis showed high probabilities of detecting significant effect sizes using rapid multi-image capturing approaches that overcome image-to-image variability. In conclusion, under-canopy imaging can be an inexpensive approach for measuring in-season maize N status, and among the indices tested, (R − B)/(R + B) was the most successful at identifying N stress.

Milk thistle (Silybum marianum L.) is highly valued for its medicinal properties. It is renowned for its capacity to flourish in dry environments, making it an attractive option for farming in areas with scarce water resources. This study aimed to assess how drought stress, foliar potassium sulfate application, and their interaction affect different milk thistle genotypes. Ten different genotypes (nine Iranian and one Hungarian) were assessed under three levels of soil water availability including control, moderate, and severe water stress, with depletion rates of 40%, 60%, and 80% of available water, respectively. Also, two foliar treatments were applied (non-spray and K₂SO₄ spray). Foliar K₂SO₄ application was applied twice, 7 days apart, during the flower bud development stage, using a 2% concentration in both 2020 and 2021. Drought stress adversely affected physiological parameters such as relative leaf water content and photosynthetic efficiency but enhanced antioxidant enzyme activities and osmotic adjustment mechanisms. K₂SO₄ foliar application exhibited dual effects, increasing yield while reducing key bioactive compounds including phenol and flavonoids content of seeds. Genotype-specific responses highlighted varying degrees of tolerance to drought stress and potassium application. Sari exhibited sensitivity to drought, while Isfahan and Hungary genotypes showed tolerance to moderate water stress with potassium foliar spray. Principal component analysis revealed the relationship of traits and genotypes by traits in each moisture condition. The study underscores the complexity of drought response mechanisms and the need for tailored management strategies and genotype selection to ensure resilience and optimize yield in milk thistle cultivation.

Optimizing planting density is crucial for enhancing the yield potential of conventional japonica rice (Oryza sativa L.) by regulating yield components and population characteristics. In a 2-year field study, four conventional japonica rice varieties were evaluated under three planting densities: 16 × 30 cm (20.83 × 10⁴ hills hm⁻²), 12 × 30 cm (27.78 × 10⁴ hills hm⁻²), 10 × 30 cm (33.33 × 10⁴ hills hm⁻²). Super-high-yielding varieties exhibited greater sink capacity and stronger source activity compared with high-yielding varieties. However, differences in population traits such as the top three leaf pattern and panicle architecture were observed due to genetic variation. Increasing planting density improved yield in super-high-yielding varieties primarily by increasing panicle number at maturity. At the same time, higher density stimulated the growth of ineffective tillers, which reduced the productive tiller percentage. This led to a more rapid decline in leaf area during the reproductive stage, lower flag leaf chlorophyll (SPAD) values, restricted overall plant growth, and reductions in plant height as well as the length, width, and angle of the top three leaves. Panicle development was similarly constrained, resulting in shorter panicle length, fewer branch pedicels, and reduced seed-setting rate, 1000-grain weight, and spikelets per panicle. Despite these negative effects, the overall increase in total spikelets compensated for the declines in individual components, leading to net yield enhancement. In conclusion, a moderate increase in planting density for super-high-yielding conventional japonica rice varieties enhances sink capacity and is beneficial for yield improvement.

Management zone (MZ) or variability zone delineation is a critical component of precision agriculture (PA), enabling site-specific management to optimize crop production and resource efficiency in response to within-field variability. This study evaluated whether digital soil maps (DSM or continuous soil property prediction maps) can serve as a superior information layer compared to the Soil Survey Geographic Database (SSURGO) for soil MZ delineation. High-resolution DSM data of four farmer fields in northeast Oklahoma, including soil features such as macro- and micronutrients, soil texture, and chemical properties at multiple depths, were used in two clustering techniques, k-means and fuzzy c-means (FCM), to delineate DSM-based MZs. Performances of DSM- and SSURGO-based MZs were evaluated using the variance reduction (VR) index based on yield monitor data from four fields between 2014 and 2020. In a baseline comparison (i.e., same number of MZs), k-means and FCM achieved a relative VR increase of 78% on average across all fields compared to SSURGO (with an absolute VR difference of 4%). When the number of MZs increased, VR was further improved by DSM-based clustering, particularly with four to five MZs (VR increased by 236% with five MZs, with an absolute VR difference of 13%). Our results showed that DSM-based clustering outperformed SSURGO-based zoning in reducing the within-zone yield variability. The leverage of DSM and clustering techniques enabled finer-scale on-farm yield variability detection and therefore enhances MZ precision. The insights from this study can inform future site-specific management strategies, ultimately supporting sustainable resource allocation, optimizing inputs, and minimizing environmental impacts.

Drought is a major constraint for oat (Avena sativa L.) production, particularly during critical growth stages. Understanding genotypic responses to drought stress and identifying sensitive periods are essential for improving resilience. We evaluated the effects of drought severity and duration on two oat genotypes, Ajay and Hayden, under greenhouse conditions. Treatments were severe drought: 40% of water-holding capacity (WHC); moderate drought: 60% of WHC; and well-watered: ≥85% of WHC. Treatments were imposed during the heading, flowering, and grain filling stages until harvesting. Overall grain yield decreased by 23% and 41.5% under moderate and severe drought conditions, respectively, compared to the well-watered condition. Hayden had higher grain yields, relative water content (RWC), and water use efficiency (WUE) than Ajay across all drought levels. Ajay showed higher root-to-shoot ratio, tiller number, and panicle number; however, these traits did not improve yield or leaf hydration under drought stress. Yield correlated strongly with yield components, such as panicle number and seed weight, compared to physiological traits, including soil plant analysis, development chlorophyll index, and RWC. Genotypes with high WUE and stable yields when exposed to drought during early reproductive stages should be prioritized in future research, which should directly measure performance under stress rather than rely on pre-maturity physiological indicators.

Pulse crops are becoming more popular to replace summer fallow in the conventional crop–fallow systems for increased crop yields, but limited information exists on the performance of pulse crops and succeeding crop yields and N dynamics in the US northern Great Plains. The objective of the study was to determine plant density, straw and grain yields, grain protein concentration, N uptake, harvest index (HI), N harvest index (NHI), N-use efficiency (NUE), and N removal index (NRI) of three pulse crops (chickpea [Cicer arietinum L.], lentil [Lens culinaris Medik], and pea [Pisum sativum L.]) and one control (spring wheat) as well as succeeding spring wheat in the rotation from 2021 to 2024. Plant density was 70%–203% greater for lentil than chickpea and pea but was 58% lower than spring wheat. Straw and grain yields and N uptake were 10%–68% greater for pea than chickpea and lentil, but yields were 25%–63% lower for pea than spring wheat. Grain protein concentration was 14%–20% greater for pea and lentil than chickpea and 27%–51% greater for pulse crops than spring wheat. The HI and NHI were 5%–25% greater for chickpea and lentil than pea and spring wheat. Spring wheat straw and grain yields, NUE, and NRI following pulse crops were 11%–21% greater than following continuous spring wheat. Because of greater grain yield and protein concentration, pea is recommended as the most effective pulse crop to replace summer fallow and increase crop yields and quality in crop–fallow systems in the northern Great Plains.

Cover crops (CCs) are increasingly recognized for their multifunctionality in provisioning, regulating, and supporting ecosystem services. CCs are characterized by different functional groups, which deliver distinct benefits, such as N fixation, nutrient scavenging, or pest suppression. Research on CCs has expanded rapidly over recent decades, yet this growth has also been accompanied by significant semantic inconsistencies in the terminology used to describe CCs, including terms such as “green manure,” “catch crops,” “trap crops,” “service plants,” “service crops,” “living mulch,” and “companion plants.” This variability is more than linguistic. It hinders literature searches, biases meta-analyses, impedes standardization, complicates policy development, and obstructs effective cross-disciplinary collaboration and knowledge transfer. Furthermore, terminological ambiguity creates inefficiencies in research and challenges for educational and algorithmic tools. To address these issues, we argue for harmonization in CC terminology, proposing that the phrase “cover crops” be systematically included in titles, abstracts, or keywords of all CC publications while allowing complementary terms to highlight specific functions of CCs. Greater consistency in language will enhance the clarity, comparability, and impact of CC research, supporting both scientific advancement and practical implementation of CCs in agroecological systems.

Growing fall cover crops (CCs) before soybeans (Glycine max L.) is an encouraged practice in the Mid-Atlantic United States. Previous studies indicated that CC termination time can influence soil moisture during the soybean growing season. However, there is a lack of studies providing comprehensive data on soil moisture dynamics and plant water stress metrics. This study was conducted over 3 site-years in Pennsylvania on silt loam and silt clay loam soils to evaluate four treatments: early planting brown (soybean planted early into pre-killed CC, i.e., terminated approximately 2 weeks before soybean planting), early planting green (soybean planted early into living CC, i.e., terminated immediately after soybean planting), late planting brown (soybean planted late into pre-killed CC), and late planting green (soybean planted late into living CC). In 1 site-year, late planting green conserved soil water content later in the growing season, compared to planting brown. In all site-years, soybean grain δ¹³C, an indicator of plant water stress, was higher in the early planting brown (−27.5‰) than in the late planting green (−28.0‰) treatment. δ¹³C was negatively correlated with CC biomass at termination (r = −0.40) and yield (r = −0.50). When soybeans were planted early, soybean yield was 7%–70% higher with planting green than planting brown. Late planting green treatment yielded comparably or higher than early planting brown. These findings suggest that delaying CC termination to increase CC biomass can mitigate soybean water stress and translate to yield gains in the rainfed no-till systems of central and southeast Pennsylvania.

For irrigated cotton (Gossypium hirsutum L.) in Florida, the current nitrogen (N) fertilizer recommendation is 67 kg N ha⁻¹ and has not changed in the last 40 years despite changes in cultural practices and development of new varieties. A study was conducted at three locations to re-evaluate cotton [Delta Pine 2038 B3XF (DP 2038)] response to six N rates (0, 50, 101, 151, 202, and 252 kg ha⁻¹), using a randomized complete block design with four replications on sandy soils. The objectives of this study were to quantify N rate effects on (1) growth, (2) in-season petiole nitrate-N (PNN), and (3) yield and N use efficiency, with the goal of N rate optimization. Results indicate that leaf area index was maximized at 101–151 kg N ha⁻¹. Application of 101 kg N ha⁻¹ maintained PNN sufficiency throughout bloom. PNN between 7800 and 8692 mg kg⁻¹ at bloom, and 1733 and 4500 mg kg⁻¹ at 4 weeks after bloom can be considered sufficient for optimum yield. Statistically, no significant increase in biomass and lint yield was found beyond the application of 101 kg N ha⁻¹. A negative correlation was found between N applied and fertilizer N use efficiency (r = −0.85), and internal N use efficiency (r = −0.61). The best-fit linear plateau model showed 113 kg N ha⁻¹ as the agronomic and economic optimum N rate for irrigated cotton in Florida. Yield goal-based analysis indicates that 50 kg N ha⁻¹ (45 lbs N acre⁻¹) is required to produce 2.5 bales of cotton ha⁻¹ (∼1 bale acre⁻¹; 1 bale = 218 kg lint), enabling site-specific, yield-targeted N application.