European Journal of Agronomy最新文献

A proportion of potassium (K) exits grassland-based dairy farms in tradeable products. Potassium imports are typically needed to offset depletion of soil reserves. The objectives of this study were to (i) quantify K entering and exiting a grassland-based dairy farm including K lost to water, (ii) to relate the balance between K entering and existing the farm to soil K fertility status in order to (iii) design a better K fertilisation strategy for grassland under temperate climatic conditions. The quantities of K entering and exiting a grassland-based dairy farm (Solohead Research Farm; 52⁰51’N, 08⁰21’W) were determined each year between 2005 and 2022. Potassium losses to groundwater were measured during the winters of 2020/21, 2021/22 and 2022/23. Averaged over 18 years, K entering (kg ha⁻¹ ± standard error) was 82 ± 11 and exiting was 41 ± 4. The annual average farm K balance was 41 ± 12 kg ha⁻¹ and ranged between −36 and 136 kg ha⁻¹. Annual K loss to groundwater (mean ± SE kg ha⁻¹) ranged between 6.9 ± 6.13 and 59 ± 7.4. Annual average soil test K (STK; following extraction using Morgan's solution (Na acetate + acetic acid, pH 4.8)) concentrations in paddocks across the farm ranged from 85 to 253 mg L⁻¹. The yearly change in average STK concentrations correlated with annual farm K balance in the preceding year (R²=0.59; P<0.001). Annual farm-scale K budgets were useful in quantifying K flows in products and losses. Potassium leaching to groundwater represented the majority (55 %) of K exiting the farm; exceeding export of K in milk and other products. Maintaining overall farm STK status required annual fertiliser K inputs of 22.5 kg ha⁻¹ between 2016 and 2022. This study elucidates the challenges in managing soil K fertility on grassland based dairy farms.

The timing of the transition between endodormancy and ecodormancy remains uncertain. However, with advancements in phenology modelling, we can now fit models which allow for variable transitions between chilling and forcing models. Previous studies have primarily focused on single-cultivar parameterisation, and few have explored multi-cultivar comparative modelling. In this paper, we address this gap by evaluating three parameterisation approaches based on the recently developed PhenoFlex framework using a large flowering time dataset of twenty-six apple cultivars collected at the same location in England. The three parameterisation approaches were: cultivar-specific, group-specific with the groups derived using the K-means algorithm on mean bloom and variation of bloom dates, and a common model (for all twenty-six cultivars). The three PhenoFlex models fitted to each of three groups of cultivars based on their flowering time and the common model fitted to all cultivars achieved similar predictive performance, better than predictions using the average bloom date of each cultivar. The best approach to apply would depend on the amount of data present. The common model works best with large number of cultivars with small datasets (∼10 years), the mean flowering date grouped works best with medium numbers of datasets (∼20 years) and the cultivar-specific model should only be used when each cultivar has at least 30 years of data, however, it is more biased, so it is likely to predict bloom dates later than the observed bloom dates. Finally, the PhenoFlex model was shown to perform better than the StepChill model, where no overlapping is allowed between chilling and heat models. The result of this study indicates that the PhenoFlex model can be used to determine apple flowering time at the species level.

Soil organic matter (SOM) is crucial for karst ecosystems, affecting cropland health, climate change mitigation, and rocky desertification control. However, there are limited research on cropland SOM prediction in karst areas with complex topography and diverse microclimates. Here, we compared the performance of four machine learning algorithms—random forest (RF), support vector regression (SVR), multilayer perceptron regression (MLP), and gradient boosting regression trees (GBRT)—for predicting cropland SOM in a typical karst landform area in 2019. Our results indicated that the GBRT model achieved the highest prediction accuracy with an R² of 0.69, MAE of 2.19 g/kg, RMSE of 3.37 g/kg, and LCCC of 0.82. Using the GBRT model and spatial data on climate, topography, and remote sensing, we predicted SOM for each 30 m × 30 m grid cell. The analysis revealed higher SOM content in the northeastern and southwestern regions and lower content in the central area, ranging from 13.95 to 47.81 g/kg, with an average of 27.16 g/kg. Lime soil had the highest SOM content, while purple soil had the lowest. Paddy fields showed significantly higher SOM than dry land. Over the past 40 years, SOM content has slightly increased, while its spatial distribution has remained stable.

Accurate estimation of wheat maturity date (MD) is helpful to make reasonable harvest planning and guarantee crop yield and quality. In this study, wheat phenology extracted from satellite images was assimilated into WheatGrow model to develop wheat maturity date estimation model. Theoretical uncertainty was introduced into assimilation system as the error covariance matrix of remote sensing observations, which improved the performance of maturity date estimation model. Compared with the simulated maturity date of crop growth model and assimilation system combined with the constant uncertainty (Assimilation1), the accuracy of assimilation system combined with the theoretical uncertainty (Assimilation2) was higher (r = 0.81, RMSE = 4.5 d). Assimilation2 has better performance and robustness in different years and different subregions. The mean relative errors between the estimated values of Assimilation2 and the observations were generally small and concentrated in the range of −5 % to 5 %. The estimated maturity date showed latitude variation in spatial distribution in the Huang-Huai-Hai Plain (HHHP). In addition, the trend of wheat maturity date from 2001 to 2020 in the central region of HHHP was significant (p < 0.05), and the mean change rate of maturity date reached 3–6 d/10a. However, the overall change trend of maturity date in the HHHP was not significant. Temperature was main driver affecting the spatiotemporal variation of wheat maturity date. The regional wheat maturity date estimation model can provide technical support for wheat maturity date estimation at regional scale.

Grazing is a major driving force of biodiversity, functions and stability of alpine meadows. In selenium-deficient alpine meadows, moderate selenium supplementation can promote plant growth and increase selenium content within the food web. However, the combined effect of grazing and selenium (Se) addition on the stability of alpine meadow communities within selenium-deficient soil is still unclear. Therefore, we conducted a three-year experiment in an alpine meadow of Qinghai-Tibetan Plateau (QTP), with two stocking rates (0 and 6 sheep months ha⁻¹) and six levels of Se addition (0, 5, 10, 20, 40 and 80 g ha⁻¹) to explore how grazing and Se addition affect community stability and its relationship with species richness, species asynchrony and functional group stability. Results showed that the community stability, species richness and biomass response ratio of the community increased gradually with the increase in the selenium application under grazing and enclosure treatments, reaching the maximum values at 20 g ha⁻¹. However, when the selenium addition exceeded 20 g ha⁻¹, the above mentioned indexes were decreased gradually, especially under grazing. Structural equation model showed that grazing and selenium addition indirectly affected the temporal stability of sedges and forbs, thus influencing the temporal stability of the community. Results of this study indicated that the alpine meadow can maintain high species diversity and community temporal stability under moderate grazing combined with selenium addition, providing a scientific basis for selenium-enriched grazing management in alpine meadows.

Cover crops (CCs) are recognised as valuable for weed management, while fallow soil between cash crop seasons likely increases weed presence. Weeds may offer similar ecosystem services as CCs, although they pose a risk of seedbank buildup. This study evaluated the impact of two winter CC systems (3-year triticale cultivation, TRIT; and a 3-year succession of rye, clover, and mustard, RCM) compared to weedy fallow (WF) on weed seedbank size and composition in a 3-year ‘maize (Zea mays L.)–maize–soybean (Glycine max (L.) Merr)’ crop succession. After 3 years, seed density of spring/summer weeds reduced in all treatments, potentially stemming from herbicide use during cash crop seasons and tillage operations. Triticale had the lowest seedbank density (9,487 seeds m⁻²) and higher diversity (Shannon Index 6.9) compared to WF (28,543 seeds m^-² and 4.1, respectively). Furthermore, stochastic analysis revealed a lower risk of enlarging weed seedbanks in TRIT compared to WF (for seed densities above 900 seeds m⁻²). Moreover, management practices (CCs, cash crop sowing, termination/harvest) synchronised with weed seed production and germination likely contributed to the decreasing seed density of species including Portulaca oleracea and Chenopodium album, which were reduced by 90 and 80 %, respectively, by the study’s end. Over three years, autumn/winter and indifferent weed seed densities increased 4.2 times more in WF and RCM (22,638 seeds m⁻²) than in TRIT. This may be due to the varying growth rates among CC species in RCM, whereas TRIT consistently established rapidly, potentially outcompeting weeds until termination. Fallow periods between cash crops may increase weed species linked to that season and future crop–weed interference in varied crop rotations. Introducing CCs can mitigate this effect, although the choice of CC species may influence the extent of the impact.

Supplemental irrigation (SI) at critical growth stages is a practical way to mitigate water shortage of winter wheat in dryland. However, the yield performance after SI is unstable in different precipitation years. To obtain a better understanding about the applicability of SI in dryland, the utilization efficiency of precipitation and irrigation water (IUE) was divided into 5 sequential ratios: water storage ratio, water consumption ratio, water transpired ratio, transpiration efficiency and harvest index. In this study, those ratios were investigated under four SI methods: no irrigation (W₀), SI once at jointing (W_j), SI once at booting (W_b) and SI twice at jointing and booting (W_j+b) throughout four years. Our results showed that water storage ratio was increased under all SI treatments, but water transpired ratio was only significantly increased under W_j and W_j+b, due to their greater development of plant population compared to W₀. Thus, wheat yield was greatly improved by 7–12 % under W_j and W_j+b than that under W₀ in dry and normal years. However, IUE was significantly decreased under W_j+b. Compared with W₀, W_j+b had higher evaporation during the fallow period and lower water consumption ratio. Furthermore, 100–300 cm subsoil water utilization was decreased under W_j+b from jointing to harvest time due to the low root length density (RD_L) in subsoil. Under W_j, evaporation, subsoil water utilization and RD_L were not negatively affected and water use efficiency was increased compared to W₀. Thus, SI once at jointing stage is a more suitable practice in dryland wheat farmland when considering the dual goal of effectively using water while increasing yields under worse precipitation year.

Crop diseases are increasingly causing devastating yield losses each year, posing a significant threat to global food security. Currently, fungicide treatment is one of the most effective measures to mitigate these disease-induced yield losses. Accurately predicting disease occurrence, onset timing, and development is critical for optimizing fungicide application schedules to achieve high efficacy. In this study, we compiled weekly disease observations, crop management, and weather data from 207 locations across China, India, and Japan. We compared six machine learning (ML) models for their performance in simulating disease severity. By mimicking disease development curves and trends at early stages, we developed a novel change detection (CD) model, named rolling linear regression (RLR), and combined it with the best-performing ML model to predict the occurrence, severity, and onset dates of four major rice diseases: leaf blast (Magnaporthe oryzae), panicle-neck blast (Magnaporthe oryzae), sheath blight (Rhizoctonia solani), and false smut (Ustilaginoidea virens). Our findings showed that the RandomForestRegressor demonstrated the highest performance in simulating disease severity, with mean absolute errors (MAE) below 0.5 % and root mean square errors (RMSE) below 2.5 %. The RLR model showed a distinct advantage over the other four widely used CD models based on five evaluation metrics. Additionally, the paired RandomForestRegressor+RLR model achieved the highest F1-score, ranging from 0.7 to 0.8, in predicting disease occurrence, outperforming 29 other ML+CD model pairs. Furthermore, the RandomForestRegressor+RLR model predicted onset dates with fewer than 6 error days and accuracies ranging from 74 % to 87 %. Combining ML with CD models not only shows robust generalization across diverse environmental conditions but also proves highly effective for large-scale disease risk forecasting in rice farming regions. The adaptability of ML techniques, when sufficient training data are available, particularly enhances decision support systems aimed at optimizing rice disease management practices for growers across various regions. Our hybrid model thus presents a compelling advancement in the precision agriculture domain, with significant implications for improving disease management strategies for crops beyond rice through timely intervention. This approach can contribute to safeguarding global food security by reducing crop losses due to disease damage.

One of the most important agroclimatic variables for stone fruit production is winter chill accumulation. To estimate chill accumulation in locations where climatic data is not recorded, spatial interpolation is necessary. In this study, we compare different interpolation methods for mean and Safe Winter Chill (SWC) in a Mediterranean stone fruit production area (Region of Murcia, SE Spain) using data from 49 climatic stations. To choose the most accurate interpolation method, as its choice may substantially influence the prediction accuracy, the predictive capability of several interpolation methods with different parameterizations (for a total number of 32 instances) was compared through out-of-bag bootstrap cross-validation, concluding that the best ones were Radial Basis Functions applied on the altitude-dependent regression residuals for mean winter chill and the altitude+latitude linear regression for SWC. The incorporation of altitude in the interpolation increased greatly the accuracy of the estimation. In fact, most of the chill accumulation spatial dependency was explained through altitude. The accuracy of the interpolation was not homogeneous across the study area. Chill accumulation in warmer coastal localities was overestimated by all the methods, possibly due to the proximity to the sea, highlighting the importance of microclimatic variables at higher-resolution spatial interpolations. Differences between methods were more notable in higher locations, where distance-only based methods underestimated chill accumulation and methods that consider altitude slightly overestimated it. This study demonstrates the importance of comparing the performance of multiple spatial interpolation methods before applying any for chill accumulation data.

Tea plant (Camellia sinensis) is a major global crop consumed as a drink after water. Quantifying carbon flux, specifically the net ecosystem exchange (NEE), in tea plantations is essential for determining carbon sequestration and ecosystem carbon balance. The Eddy covariance (EC) system is widely used for continuous monitoring of carbon flux but high costs associated with installation and maintenance limit its widespread adoption. In addition, EC flux data is often discarded due to malfunction of instruments caused by adverse weather conditions. Therefore, additional approaches for estimating NEE are necessary to overcome these challenges and ensure accurate NEE measurement. For this purpose, three standalone tree-based machine learning (ML) models were used for NEE estimation using EC flux data collected from tea ecosystem located in subtropical region (Danyang county of Zhenjiang city) of China. To address the accuracy limitations inherent in standalone ML models, the ensemble mechanism based on voting regressor method was proposed. In addition, k-fold cross-validation based on early stopping process was also used to enhance the performance of standalone ML models. Based on visual plots (scatter diagram, heatMap, Taylor diagram) and performing indices (root-mean-square error (RMSE), determination coefficient (R²), mean absolute error (MAE), mean absolute percentage error (MAPE), Nash–Sutcliffe efficiency (NSE), correlation coefficient (r), Kling Gupta Efficiency (KGE) and index of agreement (d)), the findings indicated that non-linear ensemble-generalized regression neural network (NLE-GRNN) significantly improved standalone ML model's results. In current study, the highest NSE, r and d in case of standalone ML model (DT) achieved 0.49, 0.73 and 0.75 respectively while our proposed NLE-GRNN model improved 48 % in NSE value (NSE = 0.97), 25 % in r value (r = 0.98) and 24 % in d value (d = 0.99). Likewise, NLE-GRNN significantly reduce errors (MAE, MAPE and RMSE) and provides NEE estimate closet to the observed value. The impact of climatic variables on NEE using shapley additive explanations (SHAP) analysis revealed that Rg (solar radiation) and Tair (air temperature) were the prime factors controlling NEE variation in the tea ecosystem. Considering the high accuracy and stability of the studied ML models, it is recommended to apply developed ensemble ML model (NLE-GRNN) for significant improvement of NEE estimate in the tea biomes or other ecosystems.