European Journal of Agronomy最新文献

Early senescence in the conventional pattern of wheat production is widespread, leading to decline in potential for increased and stabilized yield. Straw returning could effectively improve soil quality and extend the growth period of wheat. However, the response mechanism of straw returning in delaying leaves senescence and increasing crop productivity by regulating the internal physiological and biochemical processes of leaves is not clear. A field experiment was conducted in northwest China in 2013 (the data used in the text from 2021–2023) with four straw returning patterns: no tillage with straw mulching (NTSM), no tillage with straw standing (NTSS), conventional tillage with straw returning (CTS), and conventional tillage with straw removing (CT, the control). The antioxidant enzyme activities, cellular osmotic contents, endogenous hormones contents, stay-green capability of wheat leaves, photosynthetic source, and crop yield were investigated and analyzed. We found that no tillage with straw mulching (NTSM and NTSS) could effectively enhance the antioxidant and cell osmoregulation capabilities of wheat leaves during the grain filling process. Compared to CT, the SOD activity of NTSM and NTSS increased by 24.9–28.2 % and 18.6–20.4 %, respectively. Soluble protein content increased by 37.3–43.2 % and 25.2–57.1 %, and proline content increased by 37.3–78.4 % and 26.5–31.4 %. However, malondialdehyde content decreased by 20.3–21.4 % and 19.2–23.2 %. It was evident that the NTSM treatment provided a solid physiological foundation for delaying leaves senescence. NTSM could also help to maintain the homeostasis of endogenous hormones in wheat leaves during the grain filling period. Compared to CT, the contents of zeatin, salicylic acid, and jasmonic acid of NTSM increased by 23.3–27.2 %, 18.5–22.0 %, and 15.2–15.3 %, while abscisic acid content of NTSM decreased by 9.4–10.2 %. Based on physiological changes within the leaves, NTSM and NTSS reduced stay-green of leaves before 45 d post-emergence by 20.6–20.9 % and 17.5–17.8 % compared to CT. After 45 d post-emergence, NTSM and NTSS increased stay-green of leaves by 21.9–23.0 % and 17.0–19.1 % over CT. Thus, the grain yield of NTSM was 15.0–22.2 % and 9.0–11.3 % higher than that with CT and CTS, primarily due to the synchronous increase in ear number and thousand grain weight. Therefore, no tillage with straw mulching promoted the mobilization of antioxidant and cellular osmoregulatory responses, optimized endogenous hormones signaling, and maintained higher photosynthesis source and stay-green during the grain filling stage, thus delaying leaves senescence and increasing crop productivity in arid irrigated areas.

The use of super-intensive orchards is a growing trend in fruit production. The present study aims to improve management of these cropping systems by focusing on how agronomic decisions impact orchard dynamics in the short to medium term and by providing a decision-support approach based on stable temporal patterns from previous seasons. A multitemporal study using remote sensing and LiDAR was conducted in a commercial almond orchard over four growing seasons (2019–2022) to determine the optimal timing of image acquisition for variable pre-harvest treatments. A model-based clustering (mclust) was applied to optimal Sentinel-2 NDVI maps and apparent soil electrical conductivity (ECa) data, interpolated to the pixel centroids of Sentinel-2 image grids, to delineate potential management zones (PMZs). The leafiness-LiDAR index (LLI), a leaf area index (LAI) estimator, was obtained as ground truth after summer pruning and before harvesting, showing a significant influence of fertigation and pruning on the LAI, with summer pruning particularly influencing orchard dynamics. The optimal time for NDVI mapping was found to be two months after summer pruning in productive years and two weeks after in unproductive years. The delineated PMZs were consistent across seasons and corresponded to significant LAI differences. This method could contribute to improving resource management and sustainability in super-intensive commercial orchards.

Many agricultural problems can be illustrated and solved through long-term field experiments. Since 1967, an experiment has been conducted in Bałcyny, Poland, to compare continuous cropping with growing crops in crop rotation. The experiment also provides information on the history of chemical crop protection, which may have influenced soil and crop contamination with pesticide residues. In this study, fields of winter rye in continuous cropping (CC_rye) and a 5–6-field crop rotation (CR_rye) were used as an example to investigate the occurrence of pesticides in soil and grain in the context of previous use of chemical crop protection products. Two levels of crop protection were also examined: herbicide (since the 1972/73 growing season) and fungicide (since 1982/83) application (HF) and control treatment (CT; no herbicides since 1972/73, no fungicides since 1982/83). Insecticides were used only when absolutely necessary. Between 1967 and 2019, 58 different active substances were applied to the fields selected for this study. Soil and grain samples collected in 2019 after rye harvest were analyzed for the presence of 441 and 496 different substances, respectively. Only DDT and its metabolites were detected in soil, even though DDT was not applied to the fields after 1967. The concentration of ΣDDT (sum of all metabolites and isomers) ranged from 038 to 130 µg kg^–1 soil and exceeded the limit set in Poland (120 µg kg^–1) under CR_rye-HF treatment. In winter rye grain, no pesticide residues were found. The results show that long-term rational use of less persistent pesticides does not lead to contamination of the soil or the test cereal grain, but DDT continues to threaten the environment five decades after its probable last use. The study points to the value of comprehensive long-term recording for providing a retrospective assessment of pesticide exposure. Long-term field experiments facilitate this approach.

Intercropping, i.e., growing several species in the same field for a major part of their growing periods, often improves yield and weed control, but their performance greatly varies across situations. The aim of this study was to evaluate the effects of bi-species legume–cereal intercrops on weed dynamics and their impact on crop production, in the absence of nitrogen or water stress, via simulations with FlorSys. This individual-based 3D model simulates daily crop–weed seed and plant dynamics over the years, from cropping system and pedoclimate, focusing on competition for light. The study tested seven species proportions in two species mixtures (wheat–faba bean and barley–pea) and nine spatial sowing patterns in three species mixtures (triticale–faba bean, wheat–faba bean, wheat–pea), in both cases comparing the intercrops with the corresponding sole crops (controls). Intercrops and controls were inserted into rotations and simulated over 30 years and repeated with 10 climate scenarios from South-Western France, either with or without weeds. The simulations showed that: (1) the intercrops that best controlled weeds were barley–pea and triticale–faba bean, (2) the spatial pattern alternating one cereal row with one legume row as well as the 67 %-cereal–33 %-legume and 100 %-cereal–50 %-legume species proportions were those that maximised yields and minimised losses due to weeds, (3) the weed biomass in intercrop was greater than or equal to that of the sole cereal, and less than that of the sole legume, and (4) legumes benefitted more from intercropping than cereals because cereals are more competitive against weeds. Intercrop yield was best when combining species with contrasting shading responses (etiolated with stockier plants, leafy with stemmier plants) but early and good plant emergence was essential, particularly for weed suppression.

Estimating grain filling rate (GFR) and thousand-grain weight (TGW) plays an important role in evaluating yield and guiding the selection of varieties and cultivation strategies of winter wheat (Triticum aestivum L.). However, the current GFR and TGW monitoring methods mainly rely on destructive sampling, which can not achieve rapid estimation in a large area of farmland. This study aims to establish a method for estimating GFR and TGW of winter wheat using multispectral UAV images. Initially, grey correlation analysis method was used to evaluate the contributions of Leaf Area Index (LAI), Chlorophyll Content (SPAD), Aboveground Biomass (AGB) to GFR. A new comprehensive indicator, called LAI-SPAD-AGB index (LSA), was proposed to characterize GFR by establishing a linear regression model between LSA and GFR. Subsequently, UAV-based multispectral images were used to estimate LAI, SPAD, AGB, employing the methods such as Partial Least Squares Regression (PLSR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). Using the linear regression equation between LSA and GFR along with estimated LSA values, GFR was estimated and mapped. TGW was estimated based on GFR and grain-filling duration (GFD). Results showed the high GFR estimation accuracy (R²: 0.89, RMSE: 0.29 g/d, NRMSE: 10.0 %) and remarkable TGW estimation precision (R²: 0.92, RMSE: 4.20 g, NRMSE: 8.1 %). The parcel-scale distribution maps of estimated GFR and TGW were generated. The novel and non-destructive method of estimating GFR and TGW of winter wheat using UAV-based images can offer strong support for water and fertilizer management in the field.

Crop diversification has been shown to enhance a number of ecosystem services, including the regulation of insect pest, weed and pathogen pressure, thereby reducing pesticide needs and use. However, the quantitative relationship between crop diversification at the cropping system scale and pesticide use has been rarely addressed and is mostly supported by evidence from landscape scale or a few long-term experiments. Nevertheless, crop diversification can reduce pesticide use both as a result of the use of crops with lower inherent pesticide reliance (the effect of crop species), and as a result of the pest regulation effect due to the number of different crops in the cropping system (the effect of crop diversity). These two effects combine into a net effect at the cropping system scale which can be difficult to differentiate through experimental design or a modeling approach. We employed the DEPHY network database describing 1285 cropping systems and 67 cash crops to disentangle and quantify the two complementary effects on pesticide use at the cropping system level. Our results show that crop species and crop diversity explain 37.1 % and 1.3 % of the cropping systems total pesticide use variance respectively, while 38.7 % explained by other factors (Residuals). Excluding the crop species effect reveals that adding one crop in the cropping system decreases total pesticide use by 0.09 units of treatment frequency index, on average. Further studies are needed to shed light on the effects of crop species characteristics, as well as take into account other factors such as climate conditions.

Today’s agricultural production is heavily dependent on synthetic nitrogen (N) fertilizer. Its energy-intensive production and use are associated with a number of environmental burdens, such as global warming and marine eutrophication. Furthermore, fertilizer prices are subject to high volatility and have been rising steadily for years. One strategy to reduce the dependence on synthetic N fertilizer is to include legumes in the crop rotation, but it is important that this practice is economically viable to be adopted by farmers. Through gross margin analysis and life cycle assessment (LCA), we quantified the economic and environmental impacts of introducing grain legumes into rainfed bread wheat rotations in northern Spain. The analysis covered the full two-year sequences of barley-wheat, rapeseed-wheat and vetch-wheat. We further investigated the effect of four different bread wheat genotypes on the environmental and economic performance. In this case study, replacing synthetic N fertilizer with legume-fixed N in a two-year cropping rotation decreased most of the analysed environmental impacts. Modelled greenhouse gas emissions were 24 % lower for vetch-wheat compared to barley-wheat and 11 % lower compared to rapeseed-wheat. Despite higher wheat yield, the vetch-wheat rotation had an 18 % lower gross margin than the rapeseed rotation and a 1 % higher gross margin than the barley rotation. The sensitivity analysis showed that only when fertilizer and wheat grain prices were more than doubled, that the legume rotation became more profitable than the other rotations. Consequently, farmers would require a financial incentive to include legumes in crop rotations and reduce environmental impacts.

Xylella fastidiosa (Xf) is a plant pathogen which attacks vines, citrus fruits, olive, almond and several many other species, causing considerable economic losses. In the last few years Xf has become a global threat after being a local problem for decades: while it was once known only in circumscribed regions of the Americas, in 2013 it began to devastate olive groves in Southern Italy, and its presence has also been reported in France, Spain, and Portugal. Even though Xf has been under study since the late 1980s, the rapid worldwide spread raises the question of whether research infrastructure is adequately prepared to address new challenges, such as the need for novel methods to control and prevent Xf infection, gaining a deeper understanding of its biology and life cycle to identify vulnerable intervention points, coordinating international responses to combat its spread, and mitigating impacts on crops and the agricultural environment. Using a bibliometric approach, this study tried to answer this question by tracing an overview of the evolution of Xf literature from 1989 to present. The analysis was conducted on the Scopus database focusing separately on three periods (1989–1999; 2000–2010; 2011–2021). After a pioneering phase in 1989–1999, the intermediate period resulted the one with the highest scientific production, in which most currently ongoing research lines were started. The last period showed a revitalization of research after some years of decline, mostly due to the recent outbreak on olive in Europe, but it was characterized by a slower increment of topics with a growth of interconnection level among themes, indicating an ongoing process of consolidation of the established research lines. The authorship and thematic characterization demonstrated that Xf research was shaped by a "geographical factor", which has represented a crucial element over time and continues to have an impact on how collaborations and topics are organized.

The wheat production in the food basket of South Asia has plateaued with threats of environmental sustainability and posing a serious challenge to future food security. For sustainable wheat production in conventional rice-wheat (CTRW) systems under changing climatic scenario, atwo-year on-farm study was conducted. We evaluated system optimization practices (SOP) of legume inclusion with CTR-zero-tillage (ZT) wheat-mungbean (CTR-ZTWMb) and direct seeded rice-ZT wheat-mungbean (DSR-ZTWMb) and triple ZT (raised bed) based futuristic systems of maize-wheat-mungbean (ZTMWMb) and soybean-wheat-mungbean (ZTSWMb). The global warming potential (GWP) of wheat production was significantly reduced by 811 kg CO₂ eq/ha (783−861) in the SOP compared to CTRW. Moreover, the water usein wheat reduced by 85.9 and 85.2 ha-mm/ha in CTR-ZTWMb and DSR-ZTWMb with higher reduction in ZTMWMb and ZTSWMb by128.7 and 118.0 ha-mm/ha, respectively over CTRW. Similarly, the total weed density was reduced at 60 (39 and 52 %) and 90 (38 and 49 %) days after sowing with CTR-ZTWMb and DSR-ZTWMb over CTRW. However, the weed density reduction was lesser with ZTSWMb and ZTMWMb at 60 (3.0 and 23.6 %), and 90 (9.8 and 31.0 %) days after sowingcompared to the CTRW.The partial factor productivity (PFP) of NPK applied was 8.5–19.0 % higher under SOP over the CTRW. The use of non-renewable energy in wheat cultivation was reduced by 24.4–28.9 % with SOP over CTRW. The enhancement in wheat grain yield (7.4–11.8 %) and net returns (98–169 US$/ha) was also recorded with CTR-ZTWMb and DSR-ZTWMb and this gain in futuristic systems (ZTMWMb and ZTSWMb) was much higher in grain yield (17.2–21.0 %) as well as in net returns (283 and 362 US$/ha) over CTRW. The adoption of these SOPs on 1 million ha could produce 0.37–1.05 million t additional wheat over CTRW. The on-farm study evidenced thatwheat production with system optimization practices of legume inclusion and zero tillage are better alternatives to achieve higher productivity and profitability with a lesserenvironmental footprint in Indo-Gangetic Plains and similar agroecological regions.

Assessing the response of crop yield to year-to-year climate variability at the field scale is often done using process-based models and regression techniques. Although powerful, these tools rely on strong assumptions and can lead to substantial prediction errors. In this study, we investigate the use of a flexible machine learning algorithm combining Functional Principal Component Analysis and Random Forest, to relate field scale wheat yield to local daily climate variables. Instead of computing seasonal, monthly or any other arbitrary time-frame climate averages, climate time series are decomposed by Functional Principal Component Analysis into a few data-driven basis functions, called Principal Curves, in order to summarize the dynamic of key climate variables by a limited number of interpretable components. Scores associated to these components are then used as inputs of a Random Forest algorithm for yield prediction and for analysing important factors responsible for yield variability. To evaluate our approach, we use a French national database including wheat yield data as well as climate and management practice data for 298 farm fields from 2011 to 2016 in four main producing regions. Depending on the regions, our approach can explain from 62 % to 81 % of the yield variability when both agronomic and climate variables are included, down to 56–81 % when ignoring agronomic variables and 51–74 % when ignoring climate variables. Based on a year-by-year cross-validation, RMSE ranges from 0.5 t ha⁻¹ to 2.1 t ha⁻¹ in non-extreme years (2012–2015). However, prediction error can reach 3.6 t ha⁻¹ in case of exceptional weather conditions, such as those experienced in 2016 in Northern France. We find that this new approach performs in most cases better than the same machine learning algorithm using the usual time averages of climate variables, without the need to choose an arbitrary time-frame. We then show how important patterns in weather time series can be identified and how their effects on yield can be interpreted using the proposed modelling framework.