European Journal of Agronomy最新文献

In winter wheat-summer maize double cropping, fertilization changes the nitrogen (N) balance in the soil N pool, grain N uptake, N loss, and nitrogen use efficiency (NUE), ultimately impacting crop productivity and environmental health. For agricultural systems to maintain yields at lower environmental costs, N budgets must be balanced. Still, there is a deficiency in the reporting of N budgets of farming systems that incorporate all N flows. We evaluated the effects of various N fertilizer application rates on soil N balance and crop productivity in the 2017–2021 winter wheat-summer maize growing seasons. These rates included non-N fertilization (N0), 75 kg·N·ha⁻¹ (N75), 150 kg·N·ha⁻¹ (N150), 225 kg·N·ha⁻¹ (N225), and conventional N fertilizer application rate, 300 kg·N·ha⁻¹ (N300). Our analysis was based on a long-term field fertilization experiment and in-situ observation (established in 2010). The results show that fertilization significantly increased crop yields (wheat: 29.2 %–97.6 %; maize:25.4 %–98.1 %; annal: 27.0 %–96.7 %), among which N225 showed the highest increase value, compared with N0. Moreover, the N225 maximized grain N uptake by 201.0 %, reducing N losses and increasing N sequestration compared to the conventional N application rate of N300. N balance changes from negative to positive as the N application rate increases (wheat: −63.78–85.24 kg·ha⁻¹; maize: −55.77–82.25 kg·ha⁻¹; annal: −119.58–167.46 kg·ha⁻¹·yr⁻¹). Combining years of N inputs and outputs, the N225 is more balanced. Therefore, our study shows that an appropriate reduction of N fertilizer (N225) can help maintain agricultural productivity and promote N sequestration in farmland by reducing environmental N loss. Maintaining the virtuous cycle of N in the farmland ecosystem is beneficial and essential for the efficient utilization, high yield, and sustainable development of farmland resources.

Soda saline-alkali stress severely hampers rice growth, nitrogen use efficiency, and yield formation. Biochar addition has been recognized as a potential solution to mitigate the adverse effects of saline-alkali stress on crops. Similarly, optimal agronomic measures are known to optimize crop growth conditions and enhance yield formation and resource utilization efficiency. Despite this knowledge, there is limited understanding of the combined effects of optimizing agronomic measures and biochar addition on ionic accumulation, nitrogen use efficiency, and rice yield in soda saline-alkali paddy fields. In this study, a 3-year field experiment was undertaken to evaluate the combined effects of optimal agronomic measures and biochar application on rice physiological properties, nitrogen use efficiency, and yield under five agronomic treatments: zero-fertilizer control (CK), farmers’ practice (FP), high-yield and high-efficiency management (OPT1), super-high-yield management (OPT2), high-yield and high-efficiency management combined with biochar (OPT1+B), and super-high-yield management combined with biochar (OPT2+B). The results demonstrate that treatments of optimal agronomic practices combined with biochar application (OPT2+B and OPT1+B) effectively reduced leaf Na⁺ concentration, Na⁺/K⁺ ratio, abscisic acid (ABA) concentration, and malondialdehyde (MDA) concentration, while enhancing leaf K⁺ concentration, improving leaf water status, and reducing relative electrical leakage over three years. Furthermore, these combined treatments positively influenced the enzyme activities of nitrate reductase (NR), glutamine synthetase (GS), glutamate synthase (GOGAT), and leaf nitrogen content (LN), as well as SPAD values. Additionally, the average nitrogen agronomic use efficiency (AEn) increased by 154.71 %, 109.81 %, 50.67 %, and 32.60 % in OPT1+B, OPT2+B, OPT1, and OPT2, respectively, compared to FP, while nitrogen physiological use efficiency (PEn) decreased by 64.03 %, 58.56 %, 29.46 %, and 21.81 %. The average grain yield (GY) increased by 311.42 %, 302.86 %, 196.57 %, 178.86 %, and 133.72 % in OPT2+B, OPT1+B, OPT2, and OPT1, respectively, compared to FP. AEn exhibited positive correlations with K⁺, leaf water status (LWS), NR, GS, GOGAT, LN, SPAD, and GY. These findings will offer new insights into the sustainable development and utilization of soda saline-alkali lands.

The study of factors affecting soybean development is crucial for informed decision-making and risk analysis, particularly for a crop of such economic significance to Brazil. With the increase in the world population, the demand for soybean by-products is expected to rise, amid a backdrop of climate change that threatens agricultural production by altering the fundamental conditions of plant physiological development, as well as the reducing factors such as pests, diseases, and weeds. In this context, evaluating the new epidemiological conditions of phytopathogens in future scenarios is important for making the best possible decisions. Currently, one of the most significant diseases affecting soybean cultivation in Brazil is target spot. We aimed to assess the changes that will occur with target spot disease in soybean yield, focusing on its severity across three major regions of Brazil. The major challenge of accurately modeling climate change impacts on target spot epidemiology was addressed by modifying a generic epidemiological model for the target spot fungus and dynamically coupling it with the DSSAT/CROPGRO-Soybean model, enabling the simulation of plant-pathogen interactions under various climate scenarios. The results showed a significant increase in soybean yield across all scenarios and future periods in the three major regions. The disease severity also changed over time, increasing until 2039 and then declining until 2100 in all scenarios and regions. The SSP1-RCP2.6 scenario stood out as the most stable, with smaller declines, and relative increases from 1981 to 2019 of 7.9 % (2020–2039), 9.8 % (2040–2069), and 4.8 % (2070–2100) in the North region; 16.35 % (2020–2039), 13.1 % (2040–2069), and 14.45 % (2070–2100) in the Central region; and 3.6 % (2020–2039), 6.3 % (2040–2069), and 4.02 % (2070–2100) in the South region.

Rice production in the Yangtze River Basin accounts for 44.4 % of China’s total rice production. Exploring the response of crop yields to soil organic carbon (SOC) storage under various fertilisation treatments for maintaining high and sustainable crop yields is an urgent issue. A database containing information on crop yields, SOC content, environmental factors (climate and soil properties), and nutrient input from fertilisation was established from seven long-term experimental sites located in the middle and lower reaches of the Yangtze River Basin (operational since the 1980s/1990s) in two lowland rice-based cropping systems (i.e., rice–wheat rotation and rice–rice rotation systems). The study considered four treatments: no fertiliser application (CK); application of chemical nitrogen, phosphorus, and potassium fertilisers (NPK); application of manure (M); and a combination of NPK and M (NPKM). Results showed that the NPKM treatment produced the highest crop yields, followed by the NPK/M and CK treatments. The NPK and NPKM treatments generally had higher sustainable yield indices (SYI, 0.34–0.74) and lower coefficients of variation (CV, 11–32 %) than the M and CK treatments (SYI: 0.29–0.62 and CV: 15–44 %) in both cropping systems across all sites. Crop grain yields were significantly increased with increasing SOC storage (0–20 cm) and followed a logarithmic regression in both systems, suggesting that a further increase in SOC content could lead to higher yields. Structural equation modelling indicated that fertilisation, soil properties, and climate together explained 75–77 % of the variance in crop yield in the two systems. The primary contributing factors were fertilisation and its associated changes in soil nutrients. Chemical fertilisers mainly had direct effects on crop yields, while manure had both direct and indirect (through improvements in soil properties) effects on crop yields. In the rice–rice system, SOC alone had both direct and indirect (through the improved availability of soil nutrients) positive effects on crop yields. Our findings emphasise the potential benefits of sequestering SOC not only for enhancing crop production but also for improving the stability and sustainability of crop yield from paddy fields.

Leaf area index (LAI), which is closely related to canopy physiological processes such as photosynthesis and water utilization, serves as an important biophysical parameter for monitoring rice (Oryza sativa L.) growth. However, due to significant variations in the water-soil mixed background during the rice growing season, the optimal LAI retrieval method for rice throughout the whole period remains unclear. Here, different LAI retrieval methods, categorized into vegetation index (VI)-, look-up table (LUT)- and machine learning (ML)-based groups, were evaluated for rice at multiple growth stages using Sentinel-2 images. Particularly, the performance of rice LAI derived from the optimal retrieval model was comprehensively analyzed to understand factors influencing LAI estimation during different growth stages. Results suggested that the Gaussian Process Regression (GPR) in the ML-based group achieved the best performance for rice LAI estimation in the whole growth period (R² = 0.75, RMSE = 0.60, RRMSE = 15.81 %), followed by the normalized difference VI (NDVI) in the VI-based group (R² = 0.74, RMSE = 0.61, RRMSE = 15.98 %) and the cost function K(x)=log(x)+1/x in the LUT-based group (R² = 0.70, RMSE = 0.69, RRMSE = 18.09 %). Notably, although the VI-based LAI retrieval method incorporated ground measurements to build empirical LAI-VI relationships, a single VI with parametric regression cannot well capture variations in rice LAI across growth stages compared to the ML-based method using simulation dataset from the physical model. The optimal ML-based method also exhibited better performance in rice LAI estimation than similar studies, with R² increasing by 0.14 and RMSE decreasing by 0.18. Furthermore, based on ground LAI measurements, the water-soil mixed background is a primary influencing factor for rice LAI estimation in the tillering, jointing, and booting stages (RMSE: 0.39–0.79), whereas the saturation effects should be considered in the full heading stage (RMSE = 0.68). Overall, this study indicates that the GPR-based retrieval strategy is the optimal method for generating rice LAI datasets over the whole growth period, providing valuable reference for precision agriculture application such as field irrigation, fertilization management, and yield estimation.

Stalk lodging in the monocot Zea mays is an important agricultural issue that requires the development of a genome-to-phenome framework, mechanistically linking intermediate and high-level phenotypes. As part of that effort, tools are needed to enable better mechanistic understanding of the microstructure in herbaceous plants. A method was therefore developed to create finite element models using CT scan data for Zea mays. This method represents a pipeline for processing the image stacks and developing the finite element models. 2-dimensional finite element models, 3-dimensional watertight models, and 3-dimensional voxel-based finite element models were developed. The finite element models contain both the cell and cell wall structures that can be tested in silico for phenotypes such as structural stiffness and predicted tissue strength. This approach was shown to be successful, and a number of example analyses were presented to demonstrate its usefulness and versatility. This pipeline is important for two reasons: (1) it helps inform which microstructure phenotypes should be investigated to breed for more lodging-resistant stalks, and (2) represents an essential step in the development of a mechanistic hierarchical framework for the genome-to-phenome modeling of herbaceous plant stalk lodging.

Wheat–maize rotation, as a typical planting mode in arid irrigation areas, has some disadvantages, such as a low resource utilization rate and high carbon emissions (CEs), which seriously limits green and sustainable agricultural development. It is unclear whether green manure can be properly incorporated into a wheat–maize rotation system to improve agricultural sustainability while achieving yield increases, higher efficiency, and emission reductions. A field experiment was carried out at an arid oasis region in northwestern China from 2019 to 2022. The five treatments were treated as follows: (i) conventional tillage and leisure (CT), (ii) no-tillage mulch with green manure retention (NTG), (iii) no-tillage and removal of above-ground green manure (NT), (iv) tillage in which green manure is mixed with soil (TG), and (v) tillage in which green manure is partially removed from the ground and roots are incorporated into the soil (T). In this experiment, the effects of different green manure return methods on maize yield, water use, and CE-related parameters were investigated to evaluate the sustainability of different green manure return methods. We found that compared with CT, NTG and TG increased the maize grain yield (GY) by 26.1 % and 26.7 %, maize energy yield (EY) by 19.8 % and 18.8 %, water use efficiency based on grain yield (WUE_GY) by 35.1 % and 30.8 %, and water use efficiency based on energy yield (WUE_EY) by 29.3 % and 22.5 %, respectively. Compared with CT, the CEs of NTG were reduced by 7.4 %, and the carbon emission efficiency (CEE) increased by 28.8 %. In addition, NTG increased soil carbon sequestration potential (NPP/CE) while increasing net primary productivity (NPP), net ecosystem productivity (NEP), and carbon sequestration (CS). The sustainability evaluation index (SI) of NTG was the highest among the different green manure return methods. Therefore, no-tillage mulch with green manure retention can be used as a recommended green manure return method for green, sustainable production in arid oasis irrigated areas.

Accurately estimating the yield of citrus fruit on individual trees is essential for precise orchard management and the income of producers. However, estimating the yield of citrus fruit from images of trees remains challenging among different processes of tree pruning and image acquisition. This study adopted a deep learning based detection model to count fruit in tree images and machine learning models to estimate the yield of individual trees from the fruit count. Trees under four levels of pruning intensity (no pruning, 0–5 %, 5–10 %, and 10–15 % of new sprouts pruned) and imaged from three different views (two, four, and six images per tree) to determine the optimal conditions for yield estimation. The variables considered for yield estimation included fruit count, pruning intensity and image views. Dataset containing 1200 tree images were used to train and test four machine learning models: random forest, support vector machine, extreme gradient boosting (XGBoost), and generalized linear model. The XGBoost model achieved the lowest errors in both training and testing. The optimal yield estimation occurs when there are two, four, and six image views and trees that have been pruned >10 %, 5–10 %, and ≤5 %, respectively. The findings can enhance the accuracy of image based citrus fruit yield estimation for individual trees and reveal the influences of pruning and image views.

Context

Accurate monitoring of leaf area index (LAI) is conducive to timely and targeted management measures. Unmanned aerial vehicle (UAV) remote sensing provides an important way for non-destructive monitoring of crop leaf area index.

Objective

In this study, visible light (RGB) and multispectral remote sensing data from the UAV and ground-measured LAI data from the Plant Canopy Analyzer LAI-2200 C were used to conduct inversion of maize LAI on a fine scale.

Methods

To address the problem of spatial scale mismatch between the spatial resolution of UAV images and the ground-measured LAI, the scale difference between UAV image data and ground-measured data was reduced by removing the outermost ring data measured by the LAI-2200 C instrument, calculating the spatial resolution of the UAV images after resampling based on the height of the plant, and the resampling method based on the circle. Finally, through the above method to resample the UAV images, we extract the vegetation index and canopy height features as the input variables of the random forest model to build the maize LAI inversion model in vegetative stages and reproductive stages respectively, which is referred to as the Vis_H+RF method.

Results and conclusions

The Vis_H+RF method of Tongliao experimental station has an R² of 0.96 in the vegetative stages and a R² of 0.61 in the reproductive stages, both of which perform well and have certain migration capabilities.

Significance

The LAI inversion model constructed based on the method in this study is basically consistent with the actual situation and can provide data support for maize growth monitoring.

Melon grafting represent an increasingly important strategy against biotic and abiotic stresses. Cucurbita hybrid rootstocks have been extensively developed for this purpose, but it is necessary to find new alternatives. In this work, several experimental rootstocks of cultivated and wild resources from the Cucurbita and Cucumis genera have been evaluated using Piel de Sapo melon as scion. Cucurbita rootstocks tended to increase fruit size and seed cavity and offered more rounded shapes at the early stages of ripening, but this effect was later minimised. Cucumis rootstocks tended to show less vigour than those of Cucurbita. C. metuliferus hybrid rootstock caused plant collapse at specific environmental conditions, probably due to differences in the growth of rootstock and scion. One of the Cucurbita maxima x C. moschata and the Cucurbita pepo experimental hybrids were also less vigorous, delaying flowering and fruit set, and compromising yield. The effects of Cucurbita and Cucumis rootstocks on sugar and acid accumulation of Piel de Sapo were almost negligible, but they altered the volatile profile. This last impact depended on the specific scion x rootstock interaction and the environmental conditions. Cucumis rootstocks tended to minimise it, especially the hybrid Cucumis melo subsp. melo x C. melo subsp. agrestis Pat 81, which offered a volatile profile highly resembling the non-grafted control. Among Cucurbita alternatives, the commercial hybrid Cucurbita maxima x C moschata had the lowest impact, while other experimental hybrids C. moschata x C. moschata and C. maxima x C. moschata increased levels of alcohols. Among Cucumis rootstocks, the hybrids of wild species Cucumis ficifolius x C. anguria and C. ficifolius x C. myriocarpus, and C. metuliferus had a higher impact on the volatile profile.