European Journal of Agronomy最新文献

The ecological intensification (EI) can reduce the usage of N fertilizers in agriculture by increasing crops diversity, stimulating N fixation, and recycling plant nutrients in soil. In the present study, we aimed to evaluate the EI system compared with farmer practice (FP) and FP with silage (FPS), according to N rate, in the yield of maize and soybean, N agronomic efficiency (NAE), and N fertilizer replacement value (NFRV). A long-term experiment was established in 2011 in Ponta Grossa, Brazil, and evaluated during eight seasons. The treatments were arranged in a split-plot design, with three systems (FP, FPS, and EI) as main plot, and N rate applied to maize as subplot at 0, 70, 140, and 210 kg ha⁻¹, with four replicates. The systems consisted of rotational of maize and soybean in a no-till during summer, with different cover crops and fertilization in winter; in the FP, black oat and wheat were cultivated, white oat and ryegrass in FPS, pea and wheat in EI (without fertilizer). All treatments were duplicated to evaluate the crops in all seasons. The maize yield ranged from 9.3 to 12.6 Mg ha⁻¹ in the eight seasons, where the EI showed higher production than FP and FPS. In the average, the maize accumulated yield (eight seasons) was 5–7 Mg ha⁻¹ higher (0.6–0.9 Mg ha⁻¹ year⁻¹) in EI compared to FP and FPS. The soybean yield was not influenced by the systems or residual N application. The seasons influenced the maize yield, in which the seasons 2017/18 and 2018/19 resulted in the highest maize yield for all systems, and the season 2015/16 showed the lowest. The production of pea aboveground biomass had small variation between the seasons. The NFRV (N saved) for maximum yield was 16 kg ha⁻¹ yr⁻¹ (8 % of N applied) in the FPS, and 79 kg ha⁻¹ yr⁻¹ (38 % of N applied) in the EI, comparing to N required in FP. The NAE was lower in the EI than the FP and FPS and decreased with increase in N rate; e.g., in the season 2011/12, the NAE was 11 kg kg⁻¹ in the EI and 30 kg kg⁻¹ in the FP and FPS. The eco-intensification system resulted in higher maize yield and lower N required compared to FP, which can be strategic to increase crop yield, reduce N application, and moving towards regenerative agricultural systems.

The annual dynamics of carbon dioxide (CO₂) fluxes for irrigated and rainfed alfalfa (Medicago sativa L.) in the Southern Great Plains of the United States of America (USA) under different watering regimes are not yet fully understood. The main objective of this study was to examine the dynamics of eddy covariance (EC) measured CO₂ fluxes in relation to various biophysical factors and hay harvests for irrigated and rainfed alfalfa in central Oklahoma, USA. The study also aimed to investigate the relationship between CO₂ fluxes and satellite-derived enhanced vegetation index (EVI) at different spatiotemporal scales and to assess the temporal variability in CO₂ fluxes and EVI to variable growing conditions and hay harvests. The cumulative hay yields were 7.15 t ha⁻¹ (two harvests in 2019) in the rainfed field and ∼9 t ha⁻¹ (4–5 harvests in 2020 and 2021) in the irrigated field. Having sufficient rainfall during April and May was crucial to achieve economically feasible yields of alfalfa during the first harvest in May. The availability of water strongly regulated the potential for regrowth and carbon uptake of alfalfa following harvesting. The alfalfa fields were near carbon neutral or a small carbon source from January to mid-March and carbon sink after the initiation of vegetative growth in mid-March. The alfalfa fields were strong carbon sinks (cumulative annual net ecosystem CO₂ exchange, NEE, up to −578 g C m⁻² in irrigated field) on an annual scale. When accounting for the loss of carbon due to the removal of hay from the fields, the carbon balance of the alfalfa fields varied from small carbon sinks to small carbon sources, depending on the amount of hay harvested annually and the growing conditions. In general, the temporal patterns of CO₂ fluxes and EVI were similar in relation to growing conditions and hay harvests. However, some discrepancies and time lags were observed due to the coarse spatiotemporal resolution of the EVI products. Thus, it is essential to integrate two or more satellite products with different temporal and spatial resolutions to accurately monitor the frequent and varying sizes of hay harvests and vegetation regrowth after harvesting, and to simulate continuous time-series CO₂ fluxes.

Improving resource use efficiency through optimized nitrogen (N) management is the key to increasing crop productivity, but it remains unclear whether integrated N management practices could better improve wheat yield and the use of N and water. Here we investigated the effects of individual and integrated N management practices on wheat production in different soil-climatic conditions, and constructed the relationship between the soil-climate factors and N application rate on wheat yield. Results showed that individual N management practice (optimized N application rate only) reduced wheat yield by 2.4 %, increased N use efficiency (NUE) by 45 %, and decreased water use efficiency (WUE) by 2.2 %; however, integrated N management practice (integrating optimized N rate with irrigation, tillage, manure, or straw return) increased yield and NUE on average by 5.4 % and 55 % respectively, without reducing WUE. In different soil-climate conditions, individual N management practice decreased wheat yield from 1.1 % to 5.0 %; however, integrated N management practice increased yield by 8.5 %, 6.4 %, and 5.8 % in soil organic carbon (SOC) ≥ 10 g kg⁻¹, mean annual precipitation ≤ 400 mm, and 10 °C < mean annual temperature ≤ 15 °C, respectively. Mixed-effects model showed that the integrated N management practice had a higher yield by better exploiting the yield-enhancing benefits of SOC. Facing the complex production environment of the future, applying integrated N management practice will be a key alternative for improving grain production and resource use efficiency.

Rice yield variability can be attributed to the range of sowing dates across extensive cropping areas and varying nitrogen application rates. Nonetheless, the precise quantitative relationship between sowing dates, nitrogen application rates, and rice yield development remains elusive. In this study, we evaluated differences in dry matter and yield components of three indica hybrid varieties for five sowing dates at four ecological sites in the middle and lower reaches of the Yangtze River, China in 2019 and 2020, with three nitrogen application rates. Results indicated that yield initially increased and then decreased with delayed sowing dates. Photosynthetically active radiation accumulation after anthesis (PARAA) was identified as the key positive climate resource affecting rice yield, decreasing by an average of 19.7 MJ m⁻² for every 15-day delay in sowing. Nitrogen agronomic efficiency decreased linearly with the delay in sowing dates. Early sowing of rice with high PARAA and effective accumulated temperature after anthesis (EATAA) showed yield increases due to nitrogen application, whereas late-sown rice with insufficient photothermal resources did not exhibit marked yield improvement and may experience yield reduction. Ensuring adequate dry matter accumulation after anthesis and high seed setting rate was the main way to increase yield, influenced by sowing dates and nitrogen application rates. Intriguingly, our study revealed that nitrogen application markedly enhanced yield sensitivity to PARAA and EATAA. This finding underscores the pivotal role of nitrogen in modulating crop responsiveness to climate resources, and contributes to a deeper understanding of agronomic practices for optimizing yield under varying climatic conditions.

Iron deficiency yellowing is a serious and widespread problem that seriously affects plant growth and development, ultimately damaging plant yield. Sulfur is one of the essential elements for plant growth and development, and plays an important role in crop stress resistance. Iron (Fe) and sulfur (S) play a core role in the mineral nutrients required for plant metabolism, as both elements are essential for the activity of several proteins involved in basic cellular processes. This research used peanuts as materials to explore the effect of exogenous sulfur on alleviating iron deficiency and yellowing in peanuts under iron deficiency and iron enrichment levels. A two-year field experiment was conducted on windblown sandy soil to determine peanut yield, photosynthetic rate, photosynthetic pigment content, and the activity of key enzymes such as protective enzymes in leaves and roots. The results showed that the application of exogenous sulfur can increase pod yield by an average of 12.6 %-21.6 %. The application of exogenous sulfur significantly increased the migration of iron from roots to the ground, and increased the accumulation of active iron in young leaves by 42.6–73.2 %. Exogenous sulfur application increased the content of GSH in leaves, reduced the damage of Fe-deficient to leaf tissue structure, and effectively increased or maintained the accumulation of photosynthetic compounds in leaves. In addition, exogenous sulfur application at Fe-sufficient levels promoted dry matter accumulation while increasing N and S nutrient content, thereby increasing the N: S ratio. Therefore, exogenous sulfur application significantly increased the content of Chl a and Chl b in leaves, as well as the net photosynthetic rate. The application of exogenous sulfur increased the activity of SOD, POD, and CAT enzymes in roots and leaves, decreased the content of H₂O₂ and MDA in leaves, and reduced the rate of O_.^2- generation, thereby enhancing the plant's resistance to oxidative stress. This confirms that the application of exogenous sulfur and sufficient iron is of great significance in reducing iron deficiency yellowing in peanuts and improving yield.

Pastures represent the main land use type in Brazil and are used for livestock production and maintenance of land ownership. Most pasture areas exhibit signs of degradation related to inadequate management, which affects pasture biomass production. Terrestrial ecosystem models are effective tools for evaluating the economic and ecological returns associated with distinct management scenarios. In this paper, a new forage module implemented in the ECOSystem MOdel Simulator (ECOSMOS) is formulated, and ECOSMOS-Forage model calibration and evaluation results were obtained for estimating tropical forage growth under continuous and rotational stocking methods subject to grazing or cutting regimes. A set of equations to simulate the daily carbon allocation, growth, senescence, and morphological characteristics of Urochloa (syn. Brachiaria) brizantha cvs. Marandu and Piatã were proposed. Biophysical and physiological parameters, including the solar radiation balance, soil water content, evapotranspiration and carbon assimilation, were calibrated and evaluated with one micrometeorological experiment. Additionally, two sets of parameters were calibrated for Marandu grass growth under continuous and rotational stocking. Based on the Marandu parameterization, a third set of parameters, thereby adjusting a few parameters, was derived for the Piatã grass under rotational stocking. The ECOSMOS-Forage model could successfully capture the energy, carbon and water fluxes and biomass dynamics during growth cycles. The net radiation, evapotranspiration and gross primary production were simulated with agreement index (d) values of 0.97, 0.89, and 0.95, respectively, during the calibration phase and 0.98, 0.90, and 0.93, respectively, during the evaluation phase. Regarding model application to simulate forage growth under rotational stocking, the model could estimate aboveground, leaf and stem dry matter accumulation levels with d values ranging from 0.91 to 0.98. Although forage growth is relatively difficult to simulate, compared with annual crops, the results suggested that the model could provide high potential for simulating tropical perennial forage grasses.

Fertilization depth adjustment is a well-known strategy for increasing crop yields. However, the precise mechanism associated with this strategy remains unclear, particularly regarding increased nutrient absorption and utilization, and maize seed production. Thus, we examined the effects of different nitrogen fertilization depths [0 cm (L0), 5 cm (L5), 15 cm (L15), and 25 cm (L25)] on maize crop growth, nutrient uptake and distribution, fertilizer use efficiency, grain yield, and economic benefits in a field study conducted for two years (2021–2022) in Hexi Oasis Irrigation Area, northwest China. The optimal nitrogen fertilization depth was crucial for enhancing growth, dry matter production, and the grain yield. In particular, compared with L15 and L5, L25 significantly (P < 0.05) increased the average plant height by 5.00 % and 10.36 %, respectively, and dry matter accumulation by 2.65 % and 3.39 %. Furthermore, compared with L5 and L15, the total nutrient uptake was 19.17 % (P < 0.05) and 7.11 % higher under L25, respectively, and the average grain nutrient uptake was 23.33 % higher (P < 0.05). Moreover, L25 significantly increased the N, P, and K fertilizer utilization efficiency compared with L5 and L15, and the highest dry matter to grain translocation occurred under L25. Structural equation modeling confirmed that deep nitrogen fertilization promoted growth, dry matter translocation to grain, and the uptake and distribution of nutrients in maize plants to significantly improve the fertilizer use efficiency and yield. These findings are important for guiding fertilization management practices to increase maize seed production in regions with similar climate conditions.

Soil clod size is an important factor affecting seedling growth; besides, cultivation of strong seedlings is important to improve yield and benefit of direct-seeding rapeseed. To simulate the open field conditions, two rapeseed cultivars were grown in different clod sized soil to study morpho-physiochemical responses at five leaf stage, beside applying metabolite profiling. Moreover, foliar application via alpha-linolenic acid (ALA) was applied to improve the adaptability of rapeseed to large clod sized soil. Our results showed that soil water content was decreased in large clod sized soil (K2) versus small clod sized soil (K1), which decreased seedling weight, shoot length and root vitality, while increasing root length. Furthermore, osmolyte contents and peroxidase (POD) and catalase (CAT) activity were decreased with increasing malondialdehyde content in studied rapeseed cultivars under K2 conditions. Moreover, phospholipase A2 (PLA2) of phospholipid metabolism and its related enzymes were decreased, while its content and gene expression were increased. Besides, ALA metabolism was up-regulated, while lipoxygenase (LOX) activity, content and its gene expression were elevated under K2 conditions. Metabolic profiling showed that lipid metabolism was significantly changed than other identified components, which is synthetic precursors of various secondary metabolites. Compared with K1, the metabolic pathways of ALA and pyrimidine were up-regulated, while the metabolic pathways of glycerophospholipids and purines were down-regulated in both studied cultivars under K2 conditions. Moreover, ALA application enhanced seedling growth by improving root length, shoot and root dry weight, root vitality, ALA and proline contents, while reducing the malondialdehyde (MDA) content of two rapeseed cultivars under K2 conditions. Taken together, our study provided a theoretical basis and technical support for the soil tillage and cultivation of direct-seeding rapeseed.

Variable rate poultry litter (PL) application can potentially increase cotton yield and reduce environmental degradation risks associated with excess applications. The objective of this study was to determine whether applying PL in an inherently variable cotton field as variable-rate based on soil organic matter (SOM, VRo) or elevation (VRe) maps leads to greater yield and production efficiency than applying by the traditional uniform rate (UR). Poultry litter was applied by varying the rate within ±20 % of the target rates (7.9–11.2 Mg ha⁻¹) where the highest elevation or lowest SOM regions received the highest rate, and the lowest elevation or highest SOM regions received the lowest rate. A treatment fertilized with conventional synthetic fertilizers served as the standard control (Std). Cotton fertilized with PL, regardless of the application method, provided greater K, S, and P nutrition and increased lint yield by as much as 30 % relative to the Std treatment. Applying the PL by the VRe method increased the production efficiency (yield per unit applied PL) by nearly 13 % over the UR. The VRo treatment resulted in a yield reduction of up to 11.8 % but the production efficiency was 14.2 % greater than the UR treatment. Variable rate application based on SOM was not as effective as that based on elevation. The results overall show that PL was superior to synthetic fertilizers in this soil and this superiority could further be enhanced by applying the PL as variable rate based on elevation maps.

Effective and flexible statistical analyses are key to getting the most out of long-term experiments (LTEs). Here, we aim to introduce Bayesian analysis to the wider LTE community and show how the modelling process differs from traditional statistical analyses. Bayesian methods have become increasingly popular due to more flexibility in model development with better access to statistical software and sampling algorithms. Using Bayes' Theorem, model coefficients are estimated by incorporating any prior knowledge we may have on model terms. Including prior knowledge in this way requires a different estimating procedure for a fitted model. Bayesian model coefficients are usually sampled from thousands of samples from one or more runs of a Markov Chain. We present the use of Bayesian analyses through three examples. Example 1 illustrates a single regression with and without factors using the Broadbalk Long-Term Experiment, showing how the estimated model changes with more uncertainty in our prior knowledge of model coefficients. Example 2 demonstrates the use of multiple regression, predicting grain yield from factor variables and seasonal weather variables. Example 3 shows an estimation of soil carbon changes under crop rotation and fertilization treatments with a hierarchical time series model using a Swedish soil fertility experiment.