European Journal of Agronomy最新文献

Satellite data’s reliability, uniformity, and global scanning capabilities have revolutionized agricultural monitoring and crop management. However, the presence of clouds in satellite images can obscure useful information, rendering them difficult to infer. Aiming at the problem of cloud cover, this study presents a SpatioSpectral Generative Adversarial Network (SSGAN) approach for effectively eliminating cloud cover from multispectral satellite images. It utilizes the Synthetic Aperture Radar (SAR) images as complementary information with the optical images from the Sentinel-2 satellite. The proposed model exploits feature extraction by sub-grouping the 13 channels of Sentinel-2 images based on their electromagnetic wavelength. Experimentally, we demonstrated that the proposed SSGAN model surpasses conventional and state-of-the-art (SOTA) methods and can reconstruct regions obscured by clouds. The subgrouping optimized the utilization of sensor information and improved the performance metrics for reconstructed images. Compared to the state-of-the-art (SOTA) approach, the SSGAN model demonstrates higher performance, achieving a mPSNR of 32.771, mSSIM of 0.880, and correlation coefficient (CC) of 0.889. The SSGAN model was further evaluated under varying conditions, including scenarios without the inclusion of SAR data, where it achieved a mPSNR of 26.825, mSSIM of 0.726, and CC of 0.615. Adding SAR images into the model significantly enhanced its performance, resulting in a mPSNR of 29.932, mSSIM of 0.857, and CC of 0.735. These results indicate that higher mPSNR, mSSIM, and CC values correspond to better image reconstruction quality. Our method enhances the usability of satellite data for crop mapping, crop health monitoring, and crop yield prediction.

This study sought to corroborate microwave remote sensing and simulation models to efficiently delineate groundnut cultivation area and to estimate the yield by integration. Near real-time information on crop acreage and yield estimation is essential for making policy decisions. S1A SAR data were downloaded for entire crop growth period of groundnut during Kharif monsoon seasons (June – October) of 2019 and 2020 and were processed using MAPSCAPE RIICE software to extract groundnut cultivated area in the study districts of Tamil Nadu. Spectral dB curve groundnut generated using multi-date Sentinel 1 A SAR data showed a minimum at sowing, reached a peak at the pod development stage and decreased after that towards maturity. Groundnut area map was generated with a classification accuracy of 85.2 and 84.8 per cent with a kappa coefficient of 0.70, and total groundnut area of 104343 and 116199 ha was mapped during Kharif monsoon season 2019 and 2020, respectively. The mean agreement of 75.01 and 84.94 per cent was observed between DSSAT model simulated LAI and observed LAI at thirty monitoring locations in the study area during Kharif monsoon season 2019 and 2020, respectively, whereas agreement for yield was 82.11 and 83.70 per cent with RMSE of less than 20 per cent. Spatial distribution of groundnut LAI and yield was estimated by assimilating dB from satellite image and from DSSAT model, respectively. The estimated mean spatial LAI was 2.81 and 3.52, whereas mean spatial pod yield was 2124 and 2195 Kg ha⁻¹ during Kharif monsoon season 2019 and 2020, respectively with RMSE of less than 20 per cent and R² for integrating satellite products and simulation model for spatial estimates during both the year was >0.70, it shows the fitness of products towards increased accuracy of estimation.

Plant disease negatively impacts food production and quality. It is crucial to detect and recognise plant diseases correctly. Traditional approaches do not offer a rapid and comprehensive management system for detecting plant diseases. Deep learning techniques (DL) have achieved encouraging results in discriminating patterns and anomalies in visual samples. This ability provides an effective method to diagnose any plant disease symptoms automatically. However, one of the limitations of recent studies is that in-field disease detection is underexplored, so developing a model that performs well for in-field samples is necessary. The objective of this study is to develop and investigate DL techniques for in-field disease detection of barley (Hordeum vulgare L.), one of the main crops in Australia, given visual samples captured at barley trials using a consumer-grade RGB camera. Consequently, A dataset was captured from test-bed trials across multiple paddocks infected with three diseases: net form net blotch (NFNB), spot form net blotch (SFNB), and scald, in various weather conditions. The collected data, 312 images (6000 × 4000 pixels), are divided into patches of 448 × 448 pixels, which are manually annotated into four classes: no-disease, scald, NFNB and SFNB. Finally, the data was augmented using random rotation and flip to increase the dataset size. The generated barley disease dataset is then applied to several well-known pre-trained DL networks such as DenseNet, ResNet, InceptionV3, Xception, and MobileNet as the network backbone. Given limited data, these methods can be trained to detect anomalies in visual samples. The results show that MobileNet, Xception, and InceptionV3 performed well in barley disease detection. On the other hand, ResNet showed poor classification ability. Moreover, Augmenting the data improves the performance of DL networks, particularly for underperforming backbones like ResNet, and mitigates the limited data access for these data-intensive networks. The augmentation step improved MobileNet performance by approximately 6 %. MobileNet achieved the highest accuracy of 98.63 % (the average of the three diseases) in binary classification and an accuracy of 93.50 % in multi-class classification. Even though classifying SFNB and NFNB is challenging in the early stages, MobileNet achieved the minimum misclassification rate among the two diseases. The results show the efficiency of this model in diagnosing barley diseases using complex data collected from the field environment. In addition, the model is lighter and comprises fewer trainable parameters. Consequently, MobileNet is suitable for small training datasets, reducing data acquisition costs.

Due to climate disruption and delayed onset of rains, maize is often sown late, outside the optimal window, increasing the impact of pests such as fall armyworm and reducing yields. Identifying optimal sowing dates and the best genotypes is crucial to maximise yields while limiting fall armyworm (FAW) damage. Thus, this study was undertaken to evaluate yield and FAW damage variation in relation to sowing-date weather conditions to determine the optimal sowing window. The experiment had a split-plot design with four genotypes ('PVAH-1 L', 'PVAH-3 L', 'PVAH-6 L' and 'SAM 4 VITA') and four sowing dates (30 November to 14 January and delayed by 15, 30 and 45 days) during the three consecutive cropping seasons. Abundant rainfall and a high number of wet days increase yields while reducing FAW damage. The genotype shows that 'PVAH-1 L' has ear resistance (3.24) and leaf partial resistance (4.05) to FAW, with a high yield (6.54 t.ha⁻¹). In contrast, 'PVAH-3 L' (4.27 and 5.05), 'PVAH-6 L' (4.24 and 4.37) and 'SAM 4 VITA' (4.25 and 4.00) show partial resistance to FAW in both ears and leaves, but have relatively lower yields, except for 'PVAH-6 L' (6.29 t.ha⁻¹). Maize sown on 15 December had a high yield (8.76 t.ha⁻¹), similar to those sown on 30 November. However, sowing on 30 December and 14 January reduced yields by 2 t.ha⁻¹ and 7 t.ha⁻¹ respectively, while increasing FAW infestation and damage. Therefore, in the Lubumbashi region, due to the delayed onset of rains and climatic disturbances, the sowing period can be extended to 30 December, with an optimal window extending from 30 November to 15 December. To maximise yields and limit FAW damage, it is recommended that 'PVAH-1 L' be sown on 15 December, 'SAM 4 VITA' on 30 November or 15 December and 'PVAH-6 L' on 30 November or 15 December.

Climate change (CC), a long-term change in the average weather patterns that determine the Earth's climate, has a large and significant impact on agricultural systems, especially in Mediterranean climate regions (MCRs), which are characterized by mild and wet winters and warm and dry summers with increased drought and high temperature events, water deficits, and changes in precipitation patterns. Global greenhouse gas (GHG) emissions from anthropogenic activities have increased by an average of almost 1.5 % per year since 1990 reaching a level of 53.8 Gt CO₂-eq in 2022. CC can also significantly influence the timing and methods of pesticide application through changes in pest and disease occurrence and alterations in crop characteristics in these regions. Global pesticide consumption in agriculture worldwide in 2021 was 3.54 Mt of active ingredients, an increase of 11 % in a decade and a doubling since 1990. In this study, the main variables affecting agriculture and pesticide use under CC in MCRs were assessed. It is important to note that the challenges related to the impact of CC on agricultural practices and the necessary adaptation measures to be implemented are influenced not only by climatic factors but also by other variables, such as soil type and agroclimatic zone. The data used for this review were obtained from the Web of Science (WoS) database from the beginning of the century to the present (2001–2023). Our findings show that finance, technology and international cooperation are the key enablers of accelerated climate action. Achieving deep and lasting reductions in GHG emissions and securing a livable and sustainable future for all people will require rapid and deep transformations across all sectors and systems. CC has large negative impacts on the agricultural sector, such as reduced crop quantity and quality due to temperature increases, water scarcity and other negative environmental impacts. In addition, the use of pesticides and their environmental fate may be significantly affected by CC. Therefore, agricultural adaptation should be a priority in MCRs to improve crop performance and resilience to environmental pressures caused by CC. These changes may affect the behavior and fate of pesticides in soil and water, potentially leading to environmental pollution and negative impacts on human and ecosystem health. The local climate will clearly determine which areas are suitable for growing certain crops, potentially leading to changes in agricultural practices and pest management strategies. All these findings highlight the complex and multifaceted relationships among CC, agriculture and pesticide use and emphasize the need for comprehensive strategies to address the challenges posed by changing environmental conditions on agricultural practices and pest management in MCRs.

Accurate monitoring of crop organ biomass facilitates optimizing agronomic strategies to maximize yield or economic benefit. Unmanned aerial vehicle (UAV) is extensively employed for aboveground biomass (AGB) monitoring at the farm scale, but previous studies have mostly concentrated on total AGB rather than individual organ biomass. Furthermore, film-mulched crops, a widely used cropping pattern in northwest China, have received less attention for AGB monitoring. We aim to develop a novel model to precisely estimate the AGB of leaf (AGB_Leaf), stem (AGB_Stem), and reproductive organs (AGB_R) by UAV for film-mulched wheat and maize. The maize-wheat rotation field experiments with treatments of five nitrogen application amounts and three planting densities were conducted from 2021 to 2023, respectively. Firstly, we constructed allometric models at jointing, heading (tasseling), and grain filling stages by ground sampling data in 2021–2022. Next, the input feature set was obtained by feature selection methods (Lasso and Boruta) using UAV image data, and three traditional methods (partial least squares, ridge regression, and support vector machine) and three ensemble learning models (random forest, extreme gradient boosting, and local cascade ensemble (LCE)) were trained for AGB_Leaf inversion based on the physically-based PROSAIL model simulation dataset. Finally, the optimal AGB_Leaf inversion hybrid model was coupled with the allometric model to estimate the AGB_Stem and AGB_R in 2022–2023. The results indicated that both wheat and maize organ biomass conformed to the allometric pattern. While feature selection helped reduce computation and complexity, but didn’t improve monitoring accuracy. The normalized root mean square error (NRMSE) of the optimal hybrid model (PROSAIL + Boruta + LCE) on the measured wheat and maize AGB_Leaf datasets were 12.72 %–24.93 % and 19.65 %–25.16 %, respectively. After coupling the allometric model, the coefficient of determination (R²) of wheat and maize AGB_Stem were 0.64–0.85 and 0.63–0.68, and the NRMSE were 15.05 %–25.28 % and 24.10 %–27.06 %, respectively; and the corresponding R² of AGB_R was 0.67–0.76 and 0.72, and the NRMSE were 16.81 %–22.12 % and 21.66 %, for wheat and maize, respectively. Overall, the novel model performed well in film-mulched wheat and maize, providing a cost-effective approach for organ biomass monitoring. In the future, further validation of the model’s transferability is necessary to increase the potential for generalization in production practice.

Agriculture is a key contributor to environmental degradation and to global change. Consequently, the design of sustainable agricultural systems and the assessment of their relevance is a major priority for European agriculture. Different cropping systems, with variable objectives and constraints, can be used in cereal production in Spain. This study focused in comparing three winter barley cropping systems, ranging from intensive no-till to organic approaches. To assess the environmental impacts of each system, a Life Cycle Assessment was conducted. The findings indicate that the impacts varied depending on the chosen functional unit. When land area was considered the functional unit, the lowest impacts were obtained in the organic system, while the no-till system showed the highest. This difference was primarily attributed to variations in N fertilization. Nitrogen use had a significant impact across various categories, primarily due to the energy demands for its production and transportation, as well as the emissions of NH₃ and N₂O. However, when evaluating agricultural goods production as the functional unit, the organic system exhibited the highest impacts in terms of energy demand, freshwater ecotoxicity and freshwater eutrophication. These differences are explained by the loss of production in the fallow year and the low yields of the legume crop. The middle-way option provided the lowest impacts when economic net revenues were considered. The main reason for this was its higher total revenues associated to high crop production and EU subsidies.

Emerging biorefinery technologies can lead to new applications and new markets for various types of crop biomass. This may allow significant changes in agricultural production from crop rotations dominated by annual grain and seed crops towards annual or perennial cropping systems composed with the aims of higher biomass yield and environmental sustainability. In this study, we investigated 7 annual and 7 perennial cropping systems on a sandy loam soil, with large differences in N fertilization. Yield of dry matter (DM) and crude protein (CP) was measured over nine growing seasons from 2013 to 2021. A conventional four-year cash crop rotation with cereals and winter oil seed rape served as a reference and achieved mean annual yields of DM and CP of 10.5 and 0.85 Mg ha⁻¹ y⁻¹, respectively, across the nine years. Continuous maize and triticale had significantly higher DM and CP yields, with 57 and 15 % increases in DM yield compared to the reference crop rotation, respectively. Optimized four-year crop rotations with various annual crops including triticale, maize, beet, hemp or faba bean and various intermediate crops achieved 51–84 % and 42–78 % higher yield of DM and CP, respectively. Perennial cropping systems with festulolium and tall fescue with three or four harvests per year achieved 63–65 % higher DM yield and 192–200 % higher CP yield (2.47–2.55 Mg ha⁻¹ y⁻¹) compared to the cash crop rotation. Perennial cropping systems with miscanthus and willow had high DM but low CP yield. As a measure of nitrogen use efficiency, partial factor productivity of DM yield (PFP_DM) and CP yield (PFP_CP) were calculated, and both varied significantly between cropping systems, with highest PFP_DM for M. × giganteus and willow (114–192 kg DM kg N⁻¹) and lowest for festulolium and tall fescue (38–40 kg DM kg N⁻¹). PFP_CP was highest for the optimized crop rotations (6.88–7.94 kg CP kg N⁻¹) and lowest for miscanthus (2.94–4.98 kg CP kg N⁻¹). Across 12 of the cropping systems, which included both protein crops and lignocellulosic crops, there was a non-linear DM yield response to N fertilization rate with PFP_DM decreasing from 134.9 to 37.2 kg DM kg N⁻¹ when increasing the N rate from 50 to 500 kg ha⁻¹ y⁻¹. On the other hand, there was a linear CP yield response and, therefore, a constant PFP_CP of 5.94 kg CP kg⁻¹ N across N fertilization rates. The results clearly indicate that cropping systems can be modified to achieve higher DM and CP yields but also that choice of cropping system and optimal N fertilization may need adjustment depending on the use of the harvested biomass, the possibilities for biorefining into various components and products as well as the economic value of the components.

The recent exponential growth of craft beer sector led many brewers to seeking strategies to differentiate their beers from other similar products. For emerging hop-producing countries, one of these strategies relied on the use of local hops, thus taking advantage of the “terroir” effect. This market trend has increased the demand for raw materials from breweries, with positive effects on agricultural production, including hops. However, lack of knowledge along all the hop supply chain heavily hinders the development of a steady and efficient hop production. Particularly, growers urgently need information about the best agronomic practices, which should be adopted to achieve a sustainable hop production, under both conventional and organic farming. Unfortunately, studies on basic hop farming are scarce and often inadequate. The aim of this review is to analyze what agronomic research has done working on hops in the new growing areas (e.g., Brazil, Florida, France, Italy and Virginia) and what it has still to do to facilitate the hop growers in building and conducting their own hopyard. Through this analysis, we also aimed to provide directions for policymakers and scientific community that want to develop a hop supply chain starting from its basis. The review highlighted that while the screening of existing hop commercial cultivars was adequately referenced, the agronomic practices and growing technics suited for each new growing zone are still little studied or completely unknown. The use of artificial LED lighting is a key theme at the lowest latitudes of Florida and Brazil, organic management is pivotal in Italy and France, while alternative trellis design and hop breeding plans represent the shared research interest in all the emerging hop growing zones.

In the current situation, climate change has substantially disturbed precipitation occurrence in the Mediterranean region, by increasing its variability and decreasing the total annual amount, which both negatively affect rainfed crop productivity. We hypothesize that a simple cost-effective method for enhancing crop adaptation to new climate conditions would consist of modifying the crop sowing date. Traditional nitrogen (N) fertilization rates could also be adjusted to the current situation given the interdependent water/N relation in plant nutrition. Based on this hypothesis, during a 4-year field experiment with bread wheat (Triticum aestivum L., var. Pistolo), the effects of three sowing dates (October, November, February) and three N fertilization rates (54 kg N ha⁻¹, 27 kg N ha⁻¹, 0 kg N ha⁻¹) on crop development, yield, grain quality, soil N content and N use efficiency were analyzed. The results showed that water scarcity was the predominant limiting factor, because it outweighed N deficiency with half-fertilized crops being as productive as fully fertilized treatments. Nevertheless, sowing date was the most influential factor, with up to a 30 % yield increase noted for the November-sown wheat compared to that sown in October, while delaying wheat sowing to February decreased crop yields. Grain protein content remained the same between the November- and October-sown crops, but increased in the February one crops. Optical sensor measurements showed that an optimal assessment of the current water/N nutritional status of crops can be achieved with these tools.