Agronomy Journal最新文献

Tea (Camellia sinensis) has a long history in China, and the tea industry plays a crucial role in the national economy. Tea diseases can lead to the reduction of tea yield and reduce the quality of tea. Accurate and rapid identification of these diseases can help prevent and manage them effectively, significantly reducing production losses. However, manual recognition of tea diseases is costly, slow and subject to subjective factors. This paper proposes a deep learning-based tea disease recognition method in natural environment: referred to as YOLOv8-tea disease. The tea disease dataset in natural environment was made by ourselves. YOLOv8s is the baseline model. The VoVGSCSP module and efficient multi-scale attention module were introduced into YOLOv8s to improve the training speed and recognition accuracy of the model. To reduce the number of model parameters, Cross Stage Partial GhostNet Layer was used in the backbone network instead of C2f. Wise-IoU loss is used as a loss function to solve the problem of inaccurate detection caused by low image quality and improve the generalization ability of the model. Finally, in the dataset of tea diseases, the proposed method achieved an [email protected] (where mAP is mean average precision) of 96.34%. The number of model parameters was reduced to 8.81 M, and the number of floating point operations was reduced to 20.3 G. Compared to the original YOLOv8s model, [email protected] increased by 5.08%, the number of parameters decreased by 26.14%, and the detection speed was the fastest, with the frame per second reaching 153.3.

Soybean [Glycine max (L.) Merr.] is a major plant protein source worldwide, and its cultivation in central and northern Europe is still emerging. To understand the influence of the environment in the northern latitudes and its interactions with different soybean genotypes, a 3-year multi-location trial was carried out in Northern Germany. The objectives were to (i) quantify the grain yield and stability of six soybean genotypes across eight environments using the additive main effect and multiplicative interaction and best linear unbiased prediction models to identify superior genotypes as well as optimal environmental conditions for growing soybeans in northern latitudes, and (ii) assess the genotype-environment interaction on soybean grain yield, crude protein, and protein yield to explore the factors contributing to yield variability. The mean soybean grain yield was 2060 kg ha⁻¹, and it varied among locations and across years. A large portion of the total variance in all parameters was explained by environment (67.6%–82.8%), followed by genotype-environment interaction (7.7%–14.6%), while a small portion was attributed to genotypes (1.3%–10.5%). The growing conditions at site Müncheberg produced a stable soybean yield but were less productive than sites Dahlem and Dedelow. Regular precipitation in July and August corresponded with increased grain yield. The stability models ranked the feed-grade cultivar Merlin as superior in terms of stability and performance. In contrast, the food-grade cultivar Comandor may be risky for grain production in rainfed conditions. The study highlighted soybean's agronomic potential in northern latitudes and the influence of the prevailing environment on yield and stability.

It has been questioned whether the sufficient phosphorus (P)management approach could maximize potential grain yield in today's agricultural systems. The objective of this research was to establish six long-term experiments across Minnesota to test phosphorus (P) management strategies on soils with a defined long-term P history. Four soil test phosphorus (STP) interpretation classes were established as whole plots (low, medium, high, and very high). Split-plots within each STP class consisted of one split-plot that did not receive P (−P), and the second split-plot received a broadcast application of P fertilizer (+P) at the rate of 73 (low), 44 (medium), 15 (high), and 15 (very high) kg P ha⁻¹. Grain yield, grain P concentration, and grain P removal were determined during corn (Zea mays L.) (2015 and 2016) and soybean [Glycine max (L) Merr.] (2017) growing seasons. Grain yield was increased by P fertilizer at 7 of 18 site-years. Grain yields were similar between fertilized STP plots at the very low and low for corn and very low for soybean compared to nonfertilized or fertilized high and very high STP plots. No yield increase was noted for fertilized high or very high plots. Grain P removal was increased by applied P at 14 of 18 site-years at the low and medium STP classes with no increase for the high and very high P testing soils. Results from this research indicate no greater yield potential for soils built to high or very high STP classes versus adequately fertilizing low- or medium-testing soils.

The average annual corn (Zea mays L.) grain yield in Ohio has increased since the 1970s, yet the respective roles of corn hybrids and optimal nitrogen (N) fertilization in contributing to this historical trend remain unclear. This study evaluated trends in the agronomically optimal nitrogen rate (AONR) and corn grain yield at agronomically optimal nitrogen rate (YAONR) when corn followed corn (CC) or soybean [Glycine max (L.) Merr.] (SC) in the crop rotation within two eras of corn hybrid development. The two eras were associated with different technological development phases, including 1976–1995 (pre-transgenic era) and 1996–2021 (transgenic era). A total of 431 rainfed corn fertilizer N rate response trials were conducted in 31 Ohio counties over 45 years. From 1976 to 2021, AONR did not significantly increase, while YAONR increased by 96.1 kg ha⁻¹ year⁻¹, a 52% increase over 45 years. The YAONR significantly increased by 95 kg ha⁻¹ year⁻¹ for CC and 68 kg ha⁻¹ year⁻¹ for SC. Unfertilized (0 kg N ha⁻¹) corn yield gains were similar to annual yield gain at AONR, and the agronomic nitrogen-use efficiency (ANUE) greatly improved over 45 years, with an additional 4.6 kg of grain per kg of N for corn grown in 2021 compared to 1976. Overall, our study demonstrated that historical yield gains were largely due to improved corn hybrid ANUE and management rather than changes in N fertilizer requirements.

High plant density (PD) can differentially impact maize yields depending on cultivar characteristics due to varying responses of canopy structures to high PD, making canopy optimization essential to improve yield. This is especially important for sweet maize, which has received limited attention in dense planting in China. A 2-year (2021–2022) field experiment evaluated the performance of two sweet maize cultivars, MT6855 and YZ7, under three PDs: 4.5, 6.0, and 7.5 plants m⁻² (PD1, PD2, and PD3). Results showed that increasing PD significantly boosted fresh ear yield in MT6855 while having minimal effect on YZ7. Notably, fresh ear yield of MT6855 under PD2 increased by 14.8% compared to PD1. As PD increased, both cultivars exhibited greater plant height, ear height, internode length, and leaf spacing, along with reduced internode diameter. Higher densities also decreased leaf width, leaf area, leaf angle, and net photosynthetic rate but significantly increased leaf area index, leaf orientation value, and canopy photosynthetic capacity. MT6855 consistently outperformed YZ7, with shorter leaf length, wider leaf width, lower leaf angle, higher leaf orientation, improved photosynthetic parameters, and higher SPAD (Soil Plant Analysis Development) values. Fresh ear yield was significantly positively correlated with canopy photosynthetic capacity, leaf width, and leaf orientation value and negatively correlated with leaf angle. These findings suggest that the compact cultivar MT6855 with 6.0 plants m⁻², optimizes canopy structures and enhances photosynthetic capacity, resulting in higher yields. This research offers practical insights for improving sweet maize yield through strategic cultivar selection and PD, supporting food security and sustainable agriculture in China.

The fertilizer response of yield has been one of the major indicators of agricultural productivity in both developed and developing countries. Filling the evidence gap remains vital regarding fertilizer response in South Asia, given the emergence of intensifying weather shocks. Nationally representative evidence at field levels reflecting farmers’ actual production environments is particularly scarce. We fill this knowledge gap by using three rounds of nationally representative panel data of farm households with plot-level rice (Oryza sativa) production information and assessing how the shapes of response functions are affected by shocks in temperatures, droughts, and rainfall, using common yield response functions including both quadratic function and stochastic linear response plateau (LRP). Notably, in the stochastic LRP model, we find that one standard deviation (1SD) increases in the percentiles of growing degree days (GDD) and high nighttime temperature (HNT) relative to their historical distributions reduce sub-plateau yield response by 50% or more and yield plateau by up to 0.4 t/ha in Boro and Aman irrigated system. In the Aman rainfed system, 1SD increases in GDD and HNT percentiles reduce sub-plateau linear responses by roughly 30%. Similarly, 1SD increases in drought severity and decreases in rainfall shift down the overall linear response function by 0.1–0.2 t/ha and yield plateau by about 0.1 t/ha. Furthermore, results for stochastic LRP are also consistent for both maximum likelihood estimation of Maddala–Nelson Switching Regression, as well as Bayesian regression models in which researchers’ prior beliefs are updated by posterior information obtained from the data based on the Bayes’ rules.

Anaerobic digestion and digestate solid–liquid separation are manure treatment strategies used on commercial dairy farms. These treatment strategies typically result in increased total ammoniacal nitrogen (TAN) concentration and pH, and reduced dry matter content, which tend to increase ammonia (NH₃) emissions following field application. We hypothesized that shallow-disk injection of liquid-separated, anaerobically digested dairy manures, compared to surface application without incorporation on no-till farmland, would reduce NH₃-N emissions and conserve manure-N for crop production. Six corn (Zea mays L.) silage studies were established on commercial dairy farms across Pennsylvania in 2021–2023 with side-by-side field-scale treatment comparison strips replicated five times per farm. We quantified the impact of liquid-separated, anaerobically digested manure application methods on (i) NH₃ emissions for 24 h after application, (ii) in-season soil nitrate-N, (iii) cornstalk nitrate at harvest, (iv) corn silage production, and (v) returns on investment. Surface-applied liquid digestate lost twice as much NH₃ as injected digestate during the first 6 h after application and 58% more cumulative NH₃ loss over 24 h after application. Pre-sidedress soil nitrate-N test and corn stalk nitrate at harvest indicated there was more than sufficient N for corn with both treatments. Across farms, corn silage yield was 3.8% greater (p < 0.05) with injection than surface broadcast and returns on investment were greater in five of the six comparisons. These findings indicate that injecting liquid-separated anaerobically digested manure can reduce NH₃ loss, slightly increase corn silage yields and returns on investment, and offer environmental benefits by reducing harmful NH₃ emissions.

Common bean breeding faces challenges such as genetic and statistical unbalance across trials. This study aimed to evaluate the impact of using grain yield data (kg ha⁻¹) on selection efficiency by connecting sequential trials of common bean progenies under different experimental designs. Initially, 400 F_4:6 progenies were evaluated in 20 trials using a randomized complete block design (RCBD) during the 2019 dry season in southeast Brazil. Subsequently, 95 selected progenies were tested in three seasons (rainy/2019, winter/2020, and rainy/2020) using an incomplete block design (triple 10 × 10 lattice). Five models were fitted, each considering different (co)variance structures for residuals and progenies within generations. The model assuming a first-order analytic factor structure for progeny within generations and heterogeneous diagonal variance for residuals provided the best fit. This model produced a 68% higher average genetic gain compared to other models, along with a significant increase in average heritability. Changes in progeny classification based on predicted genotypic values were observed across seasons. The use of mixed models to fit (co)variance matrices proved superior to traditional compound symmetry models, especially in scenarios with genetic and statistical unbalance. This approach enhances the selection process by providing more accurate estimates of genetic parameters, ultimately contributing to the development of superior bean lines.

Declining soil nutrients and increasing total metals impair optimum land productivity. Understanding the spatial and temporal variabilities of soil properties helps researchers and farmers to determine the soil's ecological status and provide science-based recommendations for fertilization, crop suitability, and land use. Ningxia has less industrial waste gas and water pollution, good ecological environment, and is an important green vegetable planting base in China. This study was conducted to investigate the spatial and temporal variabilities of soil nutrients and metal elements in greenhouse conditions. All soil samples were alkaline. The concentration of soil nutrients and metal elements was concentrated in the soil surface (0–30 cm) and increased with cultivation years. A significant difference was observed between the soil planted for more than 11 years and that planted for 0 year (open field). A total of 76.8% of the soil samples’ organic matter concentration had relatively low (10–20 g/kg) and medium grades (20–30 g/kg). And 100% and 84.5% of soil samples’ total nitrogen and available nitrogen concentrations were <40 and <200 mg/kg, respectively, at 0–30 cm. And 68.3% and 95.1% of soil samples’ available potassium (>120 mg/kg) and phosphorus (>20 mg/kg) contents were relatively high and high, respectively. Nearly all soil samples have low metal element concentrations. The soil properties in the Yellow River irrigation region were higher than those in the other areas of principal component 1 (PC1). Comprehensive soil quality in Ningxia showed alkaline conditions and high potassium and phosphorus, along with suitable amounts of available nitrogen and zinc, making it ideal for high-quality greenhouse vegetable production.

Nitrogen (N) fertilizer recommendations for rainfed cotton (Gossypium hirsutum L.) in Florida have remained static at 67 kg N ha⁻¹ since 1981. A study conducted at the West Florida Research and Education Center in Jay, FL, in 2022 and 2023, evaluated the response of cotton (cv. DP-2038) to six N rates (0, 50, 101, 151, 202, and 252 kg ha⁻¹), using a randomized complete block design with four replications. Total rainfall during growing season was 923 mm in 2022 and 710 mm in 2023. Results show petiole nitrate N concentration of 5050–9700 mg kg⁻¹ at bloom and 363–1138 mg kg⁻¹ at 4 weeks after bloom is sufficient for rainfed cotton in Florida. Aboveground biomass achieved with 252 kg N ha⁻¹ was 25%–92% higher than with other N rates. The greatest seed cotton (3761 kg ha⁻¹) and lint yield (1817 kg ha⁻¹) were recorded with 151 kg N ha⁻¹, but this was statistically similar (p > 0.05) to 252, 202, and 101 kg N ha⁻¹. Lint turnout decreased with increasing N rate, with maximum in the unfertilized control and minimum with 252 kg N ha⁻¹. Fertilizer N use efficiency (NUE) and internal NUE decreased by 76% and 53%, respectively, with an increase in N rate from 50 to 252 kg ha⁻¹. The best fit linear plateau model indicated an agronomic optimum rate of 127 kg N ha⁻¹. These results show the need to revise N recommendations for rainfed cotton in Florida to maximize yield and economic returns while promoting sustainable agricultural practices.