European Journal of Agronomy最新文献

Silvopastoral systems integrate trees, pastures, and animals aiming for sustainable livestock production. The APSIM model has been used to simulate silvopastoral systems but the estimates of light transmission through the tree canopies, belowground water competition, and their effects on pasture production, have not been well adjusted. Thus, we improved the simulation of pasture growth within a silvopastoral system by integrating the APSIM-Tropical Pasture and APSIM-Slurps models and calibrated the coupled models to simulate light transmission through the trees along with water competition among species. Observed data was obtained from a silvopastoral system established in São Carlos, SP, Brazil, during two experimental periods that went from Dec-2014 until May-2016 and from Apr-2018 until May-2019. The calibration of the combination of the models was considered overall accurate. The APSIM-Slurp model simulated well (R² = 0.64, NSE = 0.60) the radiation interception by trees. The APSIM-Tropical Pasture model showed a good performance to simulate pasture (R² = 0.90 and NSE = 0.72), leaf (R² = 0.82 and NSE = 0.44), and stem (R² = 0.82 NSE = 0.75) biomass and an acceptable performance for pasture LAI (R² = 0.76 and NSE = 0.58). With that, a multifactorial simulation was performed to test the sensitivity of pasture production as a function of levels of nitrogen, tree leaf area index (LAI), tree root water extraction coefficient, post grazing residual biomass, and season of the year, using 39 years of weather data from the same location of the field experiment as input. The sensitivity analysis showed that the combination of the models was sensitive to variations of the factors with the greatest sensitivity occurring for the tree LAI. The assembling of APSIM-Tropical Pasture and APSIM-Slurp models well simulated pasture production in a silvopastoral system with different managements, indicating the potential of its use as a decision-making tool for silvopastoral systems´ design.

Aims

The current study aims to investigate the optimal substitution rate of inorganic fertilizer with organic fertilization practices to reduce N₂O emission without yield penalty in rainfed maize and to explore its denitrifier regulating mechanisms. Methods A field study started in 2016 was continued in 2020 and 2021 by using five organic nitrogen (N) fertilizer substitution treatments, including 0 % (T1), 50.0 % (T2), 37.5 % (T3), 25.0 % (T4), 12.5 % (T5), and no fertilizer control (T6). In addition to these organic fertilizer substitution rates, the maize's remaining N and phosphorus requirements were fulfilled by applying chemical fertilizer up to 200 kg N ha^–1 and 150 kg P₂O₅ ha^–1. Results The application of organic fertilizer in treatments T2, T3, and T4 reduces the total N₂O emission by 38.19 %, 24.48 %, and 22.22 %, respectively, compared with T1. Different substitution rates did not significantly affect biomass but significantly (P<0.05) affected grain yield. Treatments T1, T3, and T5 had the highest grain yield, with no significant difference. The total N and NO₃⁻−N contents were lower, but the soil moisture was higher in treatment T3 compared to T1. Based on the bioinformatics analysis, key OTUs of nifH N fixing bacteria, nirK, and nirS denitrifiers are subordinate to the generic levels of Azospirillum, Cronobacter, Devosia, and Sulfuricaulis, respectively. Conclusions In the current study, a substitution rate of 37.5 % organic fertilizer sustains maize yield by neutralizing soil pH, improving soil moisture, and nitrate-N, and abundance of nifH N fixing bacteria and nirK denitrifiers to reduce N₂O emission in rainfed maize fields in Northern China.

Soil moisture content (SMC) acquisition is vital for crop stress diagnosis and precision irrigation. However, UAV remote sensing-based SMC monitoring usually suffers from low accuracy and spatio-temporal applicability. To address these issues, this study integrated crop physiological spectral response features into an ensemble learning model. A two-year field experiment (2022–2023) was conducted. First, fractional-order differentiation (FOD) and continuous wavelet transform (CWT) were used to enhance the responsiveness of summer maize canopy spectra to SMC and leaf physiological parameters (LPPs), including leaf area index (LAI), leaf chlorophyll content (LCC) and leaf water content (LWC). Afterwards, variable importance in projection (VIP) was adopted to characterize the spectral response properties of each parameter. Finally, a stacked ensemble learning model (SELM) based on Bayesian optimization (BO) was used to construct a SMC monitoring model, and the feasibility of fusing LPP spectral response features for SMC monitoring was evaluated. The results indicated that: (1) Changes in SMC had significant effect on LCC and LAI of summer maize, which in T4 (with field water-holding capacity of 80 %-95 %) were 14.07 % and 34.41 % higher than that in T1 (40 %-50 %), respectively. (2) Spectral transformation could significantly enhance the correlation between SMC and LPPs with canopy spectra (the average increase of R reached 0.23). (3) Consideration of the crop physiology spectral response could improve the SMC monitoring accuracy, the LCC-FOD-BO-SELM had excellent monitoring performance (R²= 0.78; RMSE = 0.019). The monitoring model fusing LPPs spectral response features had the highest accuracy (R²= 0.81; RMSE = 0.016). (4) BO could significantly reduce the overfitting problem of the monitoring model, with the maximum difference between the R² of the training and test set after BO being only 0.01), thus improving the model’s generalizability. This new approach to SMC monitoring using UAV hyperspectral data provide scientific support for precision agriculture and irrigation.

Integrated weed management (IWM) promotes the combination of non-chemical techniques to achieve sustainable weed control while reducing the reliance on herbicides. However, IWM strategies reducing both herbicide and tillage intensity remain unsatisfactory, leading to weed-induced yield loss. In this study, five different IWM strategies were implemented for three years (2020–2022), aiming at reducing herbicide application across four tillage intensities while limiting weed-induced yield loss. These strategies were annual moldboard ploughing without herbicides (PL0H), annual moldboard ploughing with reduced herbicide use (PLHred), occasional moldboard ploughing with reduced herbicide use (PLredHred), shallow tillage without herbicides (ST0H) and no-tillage with reduced herbicide use (NTHred). Over the three years, averaged soil tillage intensity rating (STIR) and herbicide treatment frequency index (HFTI) ranged from 6 to 87 and from 0 to 1.6, respectively, and showed an inverse relationship. Reducing herbicides led to more mechanical weeding and reducing soil tillage led to more herbicide use. The effects of IWM strategies and years since implementation, on total weed and crop biomass, estimated weed and crop volume, weed density, weed species richness and grain yield were analysed in winter wheat. No differences in weed biomass, volume, or species richness were observed between IWM strategies over the years. Weed density increased only in PL0H between 2020 and 2022. Wheat grain yield varied by years but not among IWM strategies over time. Estimated weed-related yield loss was moderate in 2020. The feasibility and performances of such systems must be assessed over a long-term period to ensure their sustainability.

Irrigation has played a pivotal role in Chinese wheat production and is becoming increasingly crucial in adapting to the changing climate. However, the benefits of water-saving wheat production in the long-term period and its response to climate change have received limited attention. In this study, a 6-year field experiment was conducted to investigate the grain yield and water use with three treatments such as Irrigation three times (I3), Irrigation two times (I2), and disposable pre-sowing irrigation (I0), and their sensitivity to weather conditions. The average yield over six years for the I2 treatment is 8.3 Mg ha⁻¹, similar to I3 treatment while using 10.2 % less irrigation water and improving 8.0 % water use efficiency. In contrast, the grain yield in I0 treatment is 28.4 % lower than I3 treatment while consuming 36.9 % less irrigation water. Furthermore, 90.2 % of the yield decrease in I0 treatment results from the lower ear number. Water stress from jointing to flowering accounts for 58.8 % of ear number decrease. Although interannual yield variation is similar among the three treatments, the source of the variation is very different. Kernel weight explains the yield variation by 92.3 % in I3 treatment and 66.1 % in I2 treatment, while ear number accounts for 60.7 % of the variation in I0 treatment. Minimum temperature for kernel weight in both I3 and I2 treatments and rainfall for ear number in I0 treatment is the most important weather factor, respectively. In summary, this study provides valuable insights into the delicate balance between water conservation and food security while adapting to varying weather conditions.

Although one-time application of a single polymer-coated urea (PCU) can minimize the N loss in paddy field and improve nitrogen use efficiency (NUE) in relative to rapid-release urea (RU), it shows a uncertainty in increasing rice yield. There is a proposal that one-time application of PCU mixed with RU can synergistically increase grain yield and NUE. However, few studies focus on the response of roots and rhizospheric bacteria to the nitrogen (N) management. We investigated the issue based on a two-year field experiment using an indica-japonica hybrid variety Yongyou 2640, with four N managements including N omission (0 N), split application of RU (Control), one-time application of 100 % PCU (T1) and one-time application of 70 % PCU + 30 % RU (T2). Results showed that, compared with those in the control, grain yield and total number of spikelets were significantly increased in the T2 treatment while they were comparable in the T1 treatment. Both T1 and T2 remarkably increased N recovery efficiency (RE_N). During booting, the highest α diversity in rhizospheric bacterial communities was observed in the T2 treatment, followed by T1 and control. Among the root morph-physiological traits, the redundancy analysis (RDA) highlighted the significant contribution of root oxidation activity (ROA) to bacterial communities. Additionally, the linear discriminant analysis effect size (LEfSe) identified nine specific genera taxa in the T2 treatment. The abundances of these genera, particularly the Nitrospira, highly correlated with ROA, root H⁺-ATPase activity, organic acid content, microbial biomass carbon and nitrogen (MBC and MBN) contents, MBC-to-MBN ratio, and the N accumulation during booting (NABT). These traits exhibited notable advantages in the T2 treatment, which contributed significantly to the grain yield, RE_N, and the total number of spikelets. Interestingly, the nitrate-N content was most significantly increased in T2, followed by T1 and control, rather than the ammonium-N content, which was also highly correlated with the abundance of Nitrospira. In conclusion, a combination of PCU with RU could coordinate root activity and bacterial communities, especially the ROA and Nitrospira, and facilitate the consumption or cycling of nitrate-N while mitigating the risk of its mobility, leading to a remarkably increase in NABT, and consequently, synergistically increase grain yield and NUE.

Due to climate change, flooding disasters accompanied by high temperatures (FL-HT) are becoming increasingly common during cotton growing seasons, severely restricting cotton production. However, the characteristics and impacts of cotton FL-HT have rarely been investigated at the regional scale. In this work, based on existing field FL-HT research, we employed daily air temperatures and an agrometeorological indicator called the standardized antecedent precipitation evapotranspiration index to establish a novel approach to characterize FL-HT events during different cotton growth stages in the middle and lower reaches of the Yangtze River (MLRYR) during 1961–2020. Additionally, the influence of accompanying high temperatures (AHT) on the yield-reducing effect of cotton flooding was examined. The results demonstrated that more than half of the cotton FL-HT events were submonthly events, which were efficiently captured by our approach. Over the past six decades, the temporal trends of the cotton FL-HT frequency, the AHT intensity, and the conversion rate (from flooding to FL-HT) were upward at 83.0 % of the sites within the MLRYR, and up to 36 sites exhibited significant (p<0.05) increasing trends. In the most recent decade (the 2010s), the historic highs of the indicators were detected, as demonstrated by the most frequent FL-HT events (780 counts), the most intensive AHT, and the highest conversion rate (20.9 %). During the flowering and boll-filling stage, all the indicators were much greater than those during the other stages; the conversion rate reached 42.2 %, and the FL-HT frequency accounted for 63.0 % of the total FL-HT frequency over the whole growth period. Spatially, 43 % of cotton FL-HT events occurred in the southeastern MLRYR, which was identified as the region most affected by cotton FL-HT. The gravity centers of the cotton FL-HT indicators in different decades exhibited a northward-moving trend. Finally, according to the correlations between flooding intensity and cotton climatic yield, the yield-reducing effect of flooding was much stronger in the years with greater AHT (6 districts had significant correlations) than in the years with less AHT (only 2 districts had significant correlations), verifying the strengthening effect of AHT in cotton in response to flooding. In summary, this work provides new insights into monitoring cotton flooding-HT disasters and reducing flooding risk under climate change.

Global climate change has profound impacts on agricultural production, and it includes increasing temperature, global dimming, altered precipitation patterns, and elevated CO₂ concentration. However, the comprehensive assessment of the impact of different individual climatic factors and their interactions on crop production is relatively limited. Here we assessed the impacts of climate change and different climatic factors on winter wheat yields, interannual yield variability, and resource use efficiency in China from 1980 to 2020, with four wheat crop models (DSSAT-CERES-Wheat, DSSAT-Nwheat, WheatGrow, and APSIM-Wheat). The results showed that climate change was estimated to decrease wheat yields and increase interannual yield variability in the main winter wheat production region of China, especially in the Middle-lower Reaches of the Yangzi River Subregion, where yield reduction and the coefficient of variation increase could be 4.3 % and 30.2 %, respectively. Without considering the CO₂ effect, the primary reason for yield decrease and interannual yield variability increase was the interactions of temperature and solar radiation across the main winter wheat production region of China, and wheat yields were estimated to decrease by 9.2 % in the Southwest Subregion while the interannual yield variability increased by 49.5 % in the Middle-lower Reaches of the Yangzi River Subregion. The elevated CO₂ concentration was mostly beneficial, manifested as increasing the yield and decreasing interannual yield variability, but it could not fully offset negative impacts of climate change. Moreover, radiation use efficiency increased while heat use efficiency and precipitation use efficiency decreased during the study period. It is imperative to consider the diverse climatic factors and their respective regional impacts when adapting to climate change in China.

Water scarcity is a problem that affects agricultural production around the world. Increasing forage production in the semiarid region is necessary to maintain animal production, and consequently food security. Different agronomic management can improve growth and productivity responses and the water-economic indexes of the crop. Therefore, this study aimed to evaluate how the management of density and orientation of forage cactus plantations modify the morphophysiological indices, phenophases, cutting moment, productivity, soil water balance, and water-economic indicators in a semiarid environment. The study was carried out during two harvests (2020–2021 and 2021–2022) in the Brazilian semiarid region. Three experiments were conducted with the ‘Orelha de Elefante Mexicana’ clone under a randomized block design and four replications. Two experiments were composed of five planting densities (100,000, 50,000, 33,000, 25,000, and 20,000 plants ha⁻¹) modified by the spacing between plants (0.10, 0.20, 0.30, 0.40, and 0.50 m with a fixed distance of 1.00 m between rows), the first with East-West (EW) orientation and the second with North-South (NS) orientation. The third experiment presented four planting densities (50,000, 40,000, 33,000, and 28,571 plants ha⁻¹) modified by the distance between rows (1.00, 1.25, 1.50, and 1.75 m with a fixed distance of 0.20 m between plants). Biometric and biomass data were used to determine morphophysiological indices, delimitation of phenophases, ideal cutting moment, and fresh matter (FM) and dry matter (DM) productivity. The soil water balance was carried out using soil moisture readings and the physical-water properties, and the crop's water-economic indices were calculated. In general, morphophysiological indices, phenophases, and cutting moments were affected by densities (p<0.05). DM productivity was 16 % higher in the EW orientation (27.7 Mg ha⁻¹) compared to the NS orientation. The highest planting density (100,000 plants ha⁻¹) in the 1.00 × 0.10 m arrangement presented the highest averages of FM and DM of the cycles, being 401.5 and 32.4 Mg ha⁻¹ orientation EW and 420.8 and 29.5 Mg ha⁻¹ orientation NS. Density of 50,000 plants ha⁻¹ in the 1.00 × 0.20 m arrangement (265.5 and 23.5 Mg ha⁻¹ of FM and DM, respectively). This same behavior occurred for water and economic indices. Therefore, higher densities in forage cactus cultivation (100,000 plants ha⁻¹ in the East-West planting orientation and 50,000 plants ha⁻¹) must be adopted to enhance forage production in semiarid regions.

In the face of rapid population growth, scarce water resources, and changing climate conditions, smallholders confront significant challenges in maintaining the productivity of their agroecosystems. Traditional irrigation methods are often expensive and inefficient, limiting the potential for increasing crop yields. To address these issues, this study designed a subsurface irrigation system with ceramic emitters (SICE). SICE adjusted the discharge of emitters through the difference between the working head and soil water potential to provide a continuous water supply and maintain stable soil moisture. A four-year field study and economic analysis under two irrigation systems were conducted for wolfberry cultivation in Northwest China. Results showed that SICE created soil water content at 60 %-90 % field capacity, increased the photosynthetic rate of wolfberry leaves by 67.17 % and reduced malondialdehyde content by 13.61 % compared with surface drip irrigation (SDI). In comparison, SICE was better than SDI with the average increase in yield by 29.46 %, WUE by 9.97 % and IWUE by 31.71 % in four years. Furthermore, applying the SICE system reduced the total cost by 11.13 % while increasing the total return by 20.90 % compared with SDI. Therefore, the SICE system is an effective irrigation method that provides a suitable soil moisture environment for wolfberry cultivation of smallholders in northwest China, resulting in improved yield and reduced costs.