Can available scientific information be mobilised to ensure the efficacy of microbial biocontrol agents against plant diseases in the field?

Crop Protection, Год журнала: 2025, Номер unknown, С. 107115 - 107115

Опубликована: Янв. 1, 2025

Язык: Английский

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Within-Field Temporal and Spatial Variability in Crop Productivity for Diverse Crops—A 30-Year Model-Based Assessment

Agronomy, Год журнала: 2025, Номер 15(3), С. 661 - 661

Опубликована: Март 6, 2025

Within-field soil physical and chemical heterogeneity may affect spatio-temporal crop performance. Managing this can contribute to improving resource use productivity. A simulation experiment based on comprehensive data collected at the patchCROP landscape laboratory in Tempelberg, Brandenburg, Germany, an area characterized by heterogeneous conditions, was carried out quantify impact of within-field heterogeneities their interactions with interannual weather variability yield summer winter crops. Our hypothesis that crop–soil water holding capacity vary depending crop, some crops being more sensitive stress conditions. Daily climate from 1990 2019 were a nearby station, management model inputs data. previously validated agroecosystem used simulate growth for each auger profile over 30-year period. total 49 profiles classified plant available (PAWC), seasonal rainfall also lowest highest. The results revealed spatial higher than temporal most crops, except sunflower. Spatial ranged 17.3% rapeseed 45.8% lupine, while 10.4% soybean 36.8% Maize sunflower showed significant interaction between PAWC rainfall, unlike legume lupine soybean. As significant, wheat. Grain variations tended be years low responses under high consistent across categories. simulated cropping system design allocating resources according conditions predicted

Язык: Английский

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Nitrogen‐based proximal sensing and data fusion for management zone delineation

Agrosystems Geosciences & Environment, Год журнала: 2025, Номер 8(1)

Опубликована: Фев. 24, 2025

Abstract Delineation of management zones (MZ) based on soil mineral nitrogen (SMN) dynamics can enhance site‐specific management, reduce nitrate leaching, and improve nutrient efficiency. We tested proximal sensing as an alternative to standard laboratory methods capture the spatial variability SMN, (NO 3 − ), moisture (SM) combined these data with topographic remote inputs delineate MZ using fusion k ‐means clustering. Two conventionally managed fields winter oilseed rape ( Brassica napus L.) barley Hordeum vulgare were chosen for Field‐A Field‐B. Fresh samples analyzed in KCl extraction, while global positioning system‐labeled from a sensor (FarmLab) accessed via cloud storage. FarmLab estimated NO SMN higher than results p < 0.05), whereas SM showed no significant difference between two methods. Bland–Altman analysis, which assesses limit agreement ensure consistency, revealed discrepancies NO₃⁻ by both methods, particularly Field‐B, limits ranging −17.40 29.66 mg kg −1 . Results clustering, method grouping into similar categories, evaluated 11 feature sets, combine multiple sources (laboratory data, satellites, data) create comprehensive dataset analysis at different time points autumn spring. The that optimal clustering result varied depending field date. Feature sets variables performed well Field‐A, sensing, topography, improved This study demonstrates how device within‐field examines similarities differences FarmLab). Despite potential integrating in‐season MZ. approach be scaled up farm landscape scale, allowing farmers leverage monitoring, enables efficient promotes sustainable farming practices economic environmental benefits.

Язык: Английский

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How scale affects N2O emissions in heterogeneous fields of a diversified agricultural landscape

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 31, 2025

Abstract Nitrous oxide (N 2 O) emissions from agricultural soils vary due to factors such as soil organic matter, moisture, and crop type, leading short-term variations concentrated zones of high emissions, known “hot moments” “hotspots.” These peaks, occurring at various scales, contribute significantly total N O emissions. This is particularly relevant for sandy soils, where porosity low water-holding capacity promote gas diffusion create moisture variability, highly heterogeneous We investigated fluxes along a transect in six agriculturally used patches (0.52 ha) with varying texture, yield potential rotation. measured bi-weekly over years, using non-flow-through non-steady-state (NFT-NSS) manual closed chamber system, covering different crops weather conditions. Hot moments accounted 6–71% were mostly driven by physical properties. On small scale, texture environment determined spatial heterogeneity being more pronounced sandier soils. patch level, differed strongly than on microplot level mainly crop-type management. Our findings highlight the importance accounting intrinsic variability topography, microclimate within patches. Additionally, broader differences across management-influenced must be considered better understand drivers dual-scale approach emphasizes need high-resolution monitoring mitigation strategies refine models. At same time, it guides farmers toward soil-specific fertilization reduce maintain yields diverse landscapes.

Язык: Английский

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Improving spatial transferability of deep learning models for small-field crop yield prediction

ISPRS Open Journal of Photogrammetry and Remote Sensing, Год журнала: 2024, Номер 12, С. 100064 - 100064

Опубликована: Апрель 1, 2024

Predicting crop yield using deep learning (DL) and remote sensing is a promising technique in agriculture. In smallholder agriculture (< 2 ha), where 84% of the farms operate globally, it crucial to build model that can be useful across several fields (high spatial transferability). However, enhancing transferability small-scale setting faces significant challenges, including autocorrelation, heterogeneity scale dependence dynamics, as well need address limited data points. This study aimed test hypothesis cross validation (SCV) more suitable practice than random (RCV) enhance for prediction farming setting. We compared performances DL models predict settings three types two architectures based on RCV with without overlapping samples SCV. Notably, we conducted performance tests external, equally sized instead field used training. high resolution RGB imagery taken drone input. Our results show SCV outperformed those when were tested external (on average r = 0.37 SCV, 0.18 overlap 0.07 without), even though showed substantially lower (CV) (r w/o 0.73 0.98/0.73, respectively). The suggest leads over-optimism by overfitting structure remembering image-specific information (so called memorization). offers first empirical evidence preferable small making transferable.

Язык: Английский

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Differentiating between crop and soil effects on soil moisture dynamics

Hydrology and earth system sciences, Год журнала: 2024, Номер 28(11), С. 2401 - 2419

Опубликована: Июнь 6, 2024

Abstract. There is an urgent need to develop sustainable agricultural land use schemes. Intensive crop production has induced increased greenhouse gas emissions and enhanced nutrient pesticide leaching groundwater streams. Climate change also expected increase drought risk as well the frequency of extreme precipitation events in many regions. Consequently, management schemes require sound knowledge site-specific soil water processes that explicitly take into account interplay between heterogeneities crops. In this study, we applied a principal component analysis set 64 moisture time series from diversified cropping field featuring seven distinct crops two weeding strategies. Results showed about 97 % spatial temporal variance data was explained by first five components. Meteorological drivers accounted for 72.3 17.0 attributed different seasonal behaviour While third (4.1 %) fourth (2.2 components were interpreted effects texture on variance, respectively, effect depth represented fifth (1.7 %). However, neither topography nor weed control had significant variance. Contrary common expectations, rooting pattern heterogeneity seemed not play major role. Findings study highly depend local conditions. consider presented approach generally applicable large range site

Язык: Английский

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Towards Optimising the Derivation of Phenological Phases of Different Crop Types over Germany Using Satellite Image Time Series

Remote Sensing, Год журнала: 2024, Номер 16(17), С. 3183 - 3183

Опубликована: Авг. 28, 2024

Operational crop monitoring applications, including type mapping, condition monitoring, and yield estimation, would benefit from the ability to robustly detect map phenology measures related calendar management activities like emergence, stem elongation, harvest timing. However, this has proven be challenging due two main issues: first, lack of optimised approaches for accurate retrievals, second, cloud cover during growth period, which hampers use optical data. Hence, in current study, we outline a novel calibration procedure that optimises settings produce high-quality NDVI time series as well thresholds retrieving start season (SOS) end (EOS) different crops, making them more comparable ground phenological measures. As first step, introduce new method, termed UE-WS, reconstruct data by integrating robust upper envelope detection technique with Whittaker smoothing filter. The experimental results demonstrate method can achieve satisfactory performance reducing noise original producing profiles. second threshold optimisation approach was carried out each phenophase three crops (winter wheat, corn, sugarbeet) using an framework, primarily leveraging state-of-the-art hyperparameter optimization (Optuna) narrowing down search space parameter then applying grid pinpoint optimal value within refined range. This process focused on minimising error between satellite-derived observed days year (DOY) based German Meteorological Service (DWD) covering years (2019–2020) federal states Germany. calculation median temporal difference DOY observations DWD held separate (2021) those derived satellite reveal it typically ranged ±10 almost all phases. validation dates phases against field-based resulted RMSE less than 10 R-squared approximately 0.9 or greater. findings how optimising required deriving crop-specific phenophases could timely spatially explicit information at field levels.

Язык: Английский

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Framework for Smart Farming Using Artificial Intelligence of Things

Advances in business information systems and analytics book series, Год журнала: 2024, Номер unknown, С. 143 - 168

Опубликована: Сен. 16, 2024

The advancement of technology has led to the introduction smart technologies in various sectors, including farming, forming what is now known as farming. Smart farming involves using perform monitoring and automation tasks traditionally performed by humans. However, despite these advancements, farmers still need help adopting techniques crop Therefore, this chapter presents a framework designed guide implementation activities. This employs concept artificial intelligence things (IoT), which utilizes (AI) for decision-making data analysis based on from internet (IoT) devices. structured around five modules: sensor module, network processing module. These modules enable effective factors essential optimal growth performing

Язык: Английский

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Financial Analysis and Cost Implications of Implementing an Agroforestry System in Brazil

Agricultural & Rural Studies, Год журнала: 2024, Номер 2(4), С. 0023 - 0023

Опубликована: Дек. 9, 2024

Agroforestry Systems (AFS) integrate agricultural and forest production, providing ecosystem environmental services. They are considered important tools for addressing problems caused by modern development. Despite their proven productive benefits, more studies needed to support the viability adoption of AFS rural producers. This study accounts primary costs implementing 1 hectare a biodiverse in Brazil. The results show that acquisition seedlings propagules constitutes highest costs, with avocado being most expensive. Operational particularly grading purchase inputs, also represent significant expenses. Future research should focus on tracking evolution implementation substituting expensive external supplies, optimizing operational times area preparation. These efforts will enhance design AFS, local producer needs ensuring profitable maintenance.

Язык: Английский

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A participatory impact assessment of digital agriculture: A Bayesian network-based case study in Germany

Agricultural Systems, Год журнала: 2024, Номер 224, С. 104222 - 104222

Опубликована: Дек. 13, 2024

Язык: Английский

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