European Journal of Agronomy最新文献

Increasing soybean seed production and obtaining more vegetable oil and vegetable protein are important measures to ensure global food security. The Huang-Huai-Hai region is the main production area of high-quality and high-protein soybeans in China. No-tillage with straw mulching seeding technology is a new soybean planting mode in this area, which can significantly improve seed yield. However, the yield increasing mechanism is still unclear. A split-plot experimental design was adopted, which includes the main plot of seeding practice and the subplot of fertilizer treatment. The soil bulk density, soil water content, soil temperature, soybean root morphology, leaf area index, photosynthetic characteristics, canopy light transmittance, dry matter accumulation, and their relationship with seed yield were investigated. With the increase of experimental years, soil bulk density in topsoil decreased gradually (down 8.63 %) under straw mulching seeding treatment. Straw mulching stabilized the surface soil temperature, especially avoiding excessive surface soil temperature (down 1.11 ℃), which was beneficial to soil moisture retention (up 11.53 %). Straw mulching seeding practice created a suitable soil environment and promoted root growth (i.e. root length, root surface area, and root volume), especially when combined with fertilizer application. Both straw mulching and fertilizer application improved the leaf area index (up 16.55 %) of soybean. Furthermore, the leaf area index of soybean under fertilization was significantly increased in the R3-R5 stage, while it was increased during the period of R5-R7 under straw mulching treatment. In addition, the highest net photosynthetic duration was found under straw mulching (107 d), followed by straw crushing (104 d), and straw removing (102 d). The average soybean yield under straw mulching treatment was 4589.9 kg ha⁻¹, which was increased by 15.2 % and 10.3 % compared to straw removing and straw crushing, respectively. Compared with no fertilizer treatment, the average soybean yield with fertilization was 4580.2 kg ha⁻¹, which was increased by 17.2 %. In conclusion, under semi-arid conditions, straw mulching seeding practice with fertilization application created a suitable soil environment beneficial in promoting the growth of soybean underground and above ground, ultimately improving the seed yield.

In the context of global food security and climate change, intercropping has been proposed as an effective agricultural practice to improve crop productivity and land use efficiency. The objectives of this study were to evaluate the total yield potential, land equivalent ratio, and economic benefits of one annual medic (Medicago polymorpha L.) and two alfalfa (M. sativa L.) cultivars intercropped with camelina [Camelina sativa (L.) Crantz] at two different seeding rates (15 and 30 kg ha⁻¹) in comparison with that of monocrops. Results showed that medic and alfalfa intercropped with camelina had greater land equivalent ratio (LER) value (∼1.63) and productivity than the monocropping systems. The intercropped camelina on average yielded (1045 kg ha⁻¹ across the planting pattern) 17 % less than that of monocrop (1261 kg ha⁻¹). Particularly, the seed yield of intercropped camelina + alfalfa ‘XJDY’ on average was 1295 kg ha⁻¹ (across the planting pattern), exceeding the mean seed yield of 1261 kg ha⁻¹ when it was monocropped, and produced the mean economic benefit of $3123 ha⁻¹, which was about 1.3–1.9-fold greater than each of the species monocropped. Additionally, although the forage nutritional values of Medicago species or camelina seed oil quality varied among the different intercropping systems, they fell within the ranges reported in other studies. As alfalfa can be harvested multiple times during the growing season, we would like to highlight the potential for double-season (spring and fall) planting of camelina into the alfalfa ‘XJDY’ considering the shorter growing cycle of camelina (about three months from seeding to harvest). In summary, the alfalfa-camelina intercropping in this study showed great potential to improve land use efficiency, enhance productivity, and increase economic benefits, and thus can be used as a promising way to support the development of sustainable agriculture.

Silk extension determines the timing of silking, subsequent pollination and kernel setting, which has not been accounted in functional-structural plant (FSP) modelling for maize. In this study, a two-year field experiment with two local maize hybrids i.e. AnNong 591 (referred to as AN591) and ZhongDan 909 (referred to as ZD909) was carried out in Hefei, Anhui Province, China in both 2019 and 2020. The silks at basal, middle and top sections of a maize ear were destructively sampled daily in capturing silk extension. Silk extension prior to its senescence is described with a segmented model i.e. an exponential function driven by cell division (Phase I), and two linear functions corresponding to rapid silk extension (Phase II) and slow extension (Phase III) driven by cell expansion. Data in 2019 were used to fit silk extension on basal, middle and top ear positions, and the associated parameter values for the exponential and linear functions were extracted while the highest R² was achieved. The model was then compared with the Logistic model by evaluating the normalized root mean square error (nRMSE) between simulations and observations in 2019 and 2020. It was shown that the ability of a segmented model in 2019 fitting was sound though a bit poorer than a Logistic model, however, the validation for the former model performed better than the latter when independent data in 2020 were applied. This indicated that the simple segmented model is competent in predicting silk extension. In addition, silk extension at basal, middle and top sections of a maize ear for both maize hybrids was visually demonstrated. In conclusion, modelling silk extension was successfully realised by the segmented model composed of exponential and linear functions, which complements FSP modelling of maize.

Partial substitution of chemical fertilizer with organic fertilizer is emerging as a promising measure to achieve sustainable agriculture with high crop yields and low environmental risks. How much synthetic nitrogen fertilizer can be replaced by each unit of organic fertilizer? How to balance the economic benefits against the environmental risks? There is still a lack of long-term field observations to address these challenges. An 8-year field fertilization experiment (initiated in 2014) was conducted using a split-plot design with five nitrogen rates (N: N0, N75, N150, N225, N300) as main plots in combination with two manure rates (M: M0 and M1) as subplots. The results indicated that the grain yield and economic benefits slowly increased or even decreased after the N rate exceeded 150 kg ha⁻¹. The N rate required for M1 to reach the highest yield of M0 was 105 kg ha⁻¹, which was 123 kg ha⁻¹ less than the 228 kg ha⁻¹ required for that of the M0. At this point, each ton of manure can replace 4.1 kg of synthetic N. Manure application considerably increased the net economic benefit by 10.2 %. The nitrate residue in the 0−200-cm soil layer sharply increased with the N rate, particularly when the N rate exceeded 150 kg ha⁻¹. An N rate exceeding 150 kg ha⁻¹ was more likely to cause nitrate leaching to the deeper soil layer (below 200 cm) during the summer fallow season. N₂O emissions and NH₃ volatilization gradually increased with the nitrogen and manure rates, and both exhibited nonlinear distribution curves with the N rate. Pursuing higher grain yields reduced economic benefits and caused more N pollution. The Structural equation modeling (SEM) results showed that manure application significantly increased the water storage rate during the summer fallow period by 4.1 %, which would promote wheat N uptake and ultimately reduce reactive nitrogen losses and improve economic benefits. Taken together, M1N150 was the optimal fertilization scheme to synergistically achieve high yield, high economic benefits and low environmental risks in Guanzhong Plain of China.

Green manure being return to fields has been widely used in arid areas, as an effective technical measure to improve soil fertility and increase crop yield. Although a large number of studies have shown that green manure return to field can promote crop yield increase, the physiological mechanism of different green manure return methods to promote crop yield increase has not been well explained. This study aims to further understand the relationship between soil water and nitrogen environment, crop roots and photosynthesis, and further clarify the physiological mechanism of green manure return methods to improve maize yield. A field experiment was carried out at an arid oasis region in northwestern China from 2020 to 2022. The five treatments were treated as follows: (i) conventional tillage and leisure (CT), (ii) no-tillage and green manure cover the surface (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). Results showed that the NTG and TG treatments significantly increased maize grain yield (GY). The GY of NTG and TG significantly increased by 13.0–34.3 % and 11.6–32.1 % compared with CT. In addition, NTG and TG significantly increased soil water storage (SWS) and soil total nitrogen (STN), increased maize root biomass (RB), increased maize leaf area index (LAI), leaf chlorophyll content (SPAD), and net photosynthetic rate (Pn), and decreased leaf senescence index (FSLA) compared with CT. The structural equation model showed that increasing SWS and STN could increase LAI by promoting root growth of maize, thus increasing Pn and finally increasing GY. Therefore, NTG can be a suitable green manure return method to increase maize yield in arid oasis irrigated areas.

High yield, good eating quality, and high nitrogen (N) use efficiency present challenges in cultivating medium hybrid indica rice. We hypothesized that balanced source-sink relationships are key traits for achieving high yield, good eating quality, and high N use efficiency in medium hybrid indica rice under suitable N management regimes. Three field experiments were conducted with two medium hybrid indica rice cultivars. Five distinct N application levels, designated as N0, N75, N150, N225, and N300, were used to investigate varying source-sink characteristics and their impact on yield, eating quality, and N use efficiency. The results indicated that the ratio of dry matter at the heading stage to the number of spikelets per unit area (DM/Spik. R) exhibited a significant decrease with increasing N application rates in both cultivars over three years, except for the cultivar HLY898 in 2021. Conversely, an increase in N application rates generally increased the ratios of accumulated N rate and leaf area at the heading stage to the number of spikelets per unit area (AN/Spik. R and LA/Spik. R) across the cultivars and experimental years. Only AN/Spik. R showed significant relationships with yield, eating score, and N partial factor productivity (PFPN). In addition, significant differences were observed among the three study years for DM/Spik. R and LA/Spik. R, but no significant difference was found for AN/Spik. R across the three study years. Moreover, both N concentration and nitrogen nutrition index (NNI) at heading had significant and linear relationships with AN/Spik. R. Further analysis revealed that the highest yield and maximum eating scores, constituting 92 % of the total, were achieved when AN/Spik. R reached 3.65 mg per spikelet. Correspondingly, the NNI was recommended as 0.93 at heading when 90 % of the highest yield and maximum eating scores were harvested. Additionally, 79 % of the highest yield and maximum PFPN were obtained when AN/Spik. R and NNI were approximate to 2.58 mg per spikelet and 0.78 at heading, respectively. These results suggest that moderate AN/Spik. R corresponding to mild N deficiency at heading could be a key source-sink indicator for the integrated regulation of high yield, good eating quality, and high N use efficiency in medium hybrid indica rice.

Rapid and accurate crop classification is essential for estimating crop information and improving cropland management. The application of deep learning models for crop classification using time-series data has become the most promising method. However, most approaches rely on single models for data processing result in lower classification accuracy and poor stability. Therefore, this study proposes a dual-path approach with attention mechanisms (DPACR) to promote the performance of this model architecture in crop classification using time series data. Specifically, the model comprises two branches, the Recurrent neural network (RNN) branch with bidirectional gated recurrent units (GRU) with a self-attention mechanism, and the convolutional neural network (CNN) branch based on SE-ResNet. Crop classification is accomplished by a main classifier, supported by auxiliary classifiers from the two branches. Using the Google Earth Engine and the Sentinel-2 satellite data, DPACR was tested in the Hetao irrigation district in Inner Mongolia, China. The comparison experiment demonstrated that the DPACR achieved the highest overall accuracy (OA = 0.959) and Kappa coefficient (Kappa = 0.941) compared to other five models (MLP, SE-ResNet, Bi-At-GRU, SVM, and RF). DPACR excelled in classifying six crops, maintaining high accuracy across multiple classes. Compared to attention mechanisms, auxiliary classifiers can significantly improve classification performance. This study highlights the effective combination of cloud computing and deep learning for large-scale crop classification, providing a practical method for agricultural monitoring and management.

Applying concepts from natural ecosystem ecology, particularly through diversified cropping systems that integrate woody crops with other crops, can enhance agrobiodiversity and significantly contribute to multifunctional agriculture. The ecological and, more recently, the agronomic literature have indicated that functional trait aspects of diversity offer a more comprehensive explanation for ecosystem functions compared to species richness alone. In this study, we apply the trait-based approach during experimental agroforest succession in subtropical Southern Brazil to investigate the main factors of crop functional structure, differentiating the effects of functional diversity (FD) from functional identity (CWM - community weighted mean), on Nature's Contributions to People (NCP). The experiment was designed varying crop FD, based on leaf nitrogen concentration, while maintaining crop species richness constant across all treatments. To assess FD and CWM, we measured six crop traits; maximum plant height, leaf area, specific leaf area, leaf nitrogen content, leaf dry-matter content, and stem-specific density. Additionally, we evaluated four material NCP (green manure, forage, germplasm, and food) based on data collected during the initial four years of the experiment (2017–2020). To analyze the data, we employed principal component analysis to reduce the number of variables and subsequently fitted linear mixed-effect models. Our findings provide support for the hypothesis that crop functional structure, particularly in relation to leaf traits (leaf area and leaf nitrogen content) and vegetative height, significantly influences material outputs and predicts above-ground biomass production that can be utilized as green manure and animal feed in agroforestry and silvopastoral systems. Both FD and CWM were important drivers of NCPs, showing that both niche complementarity and selection effects contribute to explain agroecosystem functioning. By utilizing functional traits, we reached valuable insights for generating agronomical recommendations and enhancing cropping system diversification through effective trait-based management.

The application of controlled-release urea (CRU) has become an important practice to increase maize yield and nitrogen (N) use efficiency (NUE) with one-time fertilization management. The appropriate ratios and application effects of combinations of regular urea and CRU with different N-release periods (U–CRU) may vary because of regional climate and crop specificity. A three-year (2019–2021) field experiment was conducted in the northeastern region of China to investigate the effects of U–CRU on maize yield, NUE, changes in soil inorganic N content, and apparent N loss. With N fertilizer applied at 210 kg ha^–1, two types of CRU with release periods of 60 days (CRUa) and 90 days (CRUb) were mixed with regular urea in four different combinations: CRU1 (Urea 40 % + CRUa 60 %), CRU2 (Urea 40 % + CRUa 40 % + CRUb 20 %), CRU3 (Urea 40 % + CRUa 20 % + CRUb 40 %), CRU4 (Urea 40 % + CRUb 60 %). The controls were Urea (U) only and no N fertilizer application (N0). Compared with U, all U–CRU treatments significantly increased soil inorganic N contents (NO₃^––N, 1.4–7.0 mg kg^–1; NH₄⁺–N, 0.3–1.3 mg kg^–1) in the 0–20 cm soil layer from twelfth-leaf (V12) to physiological maturity (PM) stages of maize. Furthermore, U–CRU treatments increased N uptake from silking (R1) to PM stages and therefore contributed to grain N accumulation which increased 17.1–30.9 % and improved N recovery efficiency (REN), which increased by 8.1–13.1 %. Associated with increases in quantity of ears and 100-grain weight, maize yield increased by 12.2–18.9 % in CRU3, with the increases the highest among treatments. Additionally, compared with U, all U–CRU treatments significantly reduced the inorganic N content in the deep soil layer (40–100 cm) and the lowest apparent N loss was in CRU3, decreasing by 93.4 kg ha^–1 compared with that in U. Regression relations between the mixture ratios of CRUs and maize yield, apparent soil N loss, and REN were used to determine that the appropriate ratio of U:CRUa:CRUb to attain high maize yield and NUE was 40 %:18.6–24.6 %:35.4–41.4 %. Therefore, determining the optimum mixture ratios of CRUs can increase maize yield and NUE while reducing the risk of N loss in continuous maize cropping systems. The results provide a scientific basis for N fertilizer management in spring maize production in northeastern China.