Exploring biodiversity patterns at the landscape scale by linking landscape energy and land use/land cover heterogeneity.

Agricultural Biodiversity dynamics has been evaluated by social metabolism or by landscape structure-function analysis. In this study, by using ELIA modeling, we used both methods in combination to understand how the interplay between social metabolism and landscape structure-function can affect biodiversity pattern distribution. We used energy reinvestment (E) as an indicator of social metabolism and landscape heterogeneity (Le) as an indicator of landscape structure-function. We propose a research hypothesis to analyze biodiversity patterns considering four different clusters identified based on high or low E or Le. As cluster 1, we defined E as high and Le as low and associated natural ecosystems to it. These ecosystems are expected to contain high species abundance but low richness. As cluster 2, both E and Le were defined as high and semi-natural ecosystems were associated to it, where nature friendly farm system developed. In these ecosystems, high species abundance and richness are expected. Cluster 3 with low E and Le was associated intensive farmland, which is due to the simplification of the landscape. Here, low energy reinvestment and landscape heterogeneity confirm that ecosystem services related to biodiversity have been drastically reduced. Lastly, cluster 4 with low E but high Le refers to intensive mosaics of farmland and pasture. In this cluster, the biodiversity richness index is high due to spatial landscape diversity, but the biodiversity abundance index is low due to the lack of energy reinvestment. We evaluate the proposed hypothesis for biodiversity analysis in the Qazvin province, emphasizing the interplay between energy availability and landscape heterogeneity in shaping ecological communities. This study highlights the importance of understanding biodiversity patterns at spatial scale and emphasizes the need for interdisciplinary research to address conservation and sustainability challenges. Our approach would be very useful where there is lack of biodiversity data.

Primarily tests of a optoelectronic in-canopy sensor for evaluation of vertical disease infection in cereals.

BACKGROUND: Health scouting of crops by satellite, airplanes, unmanned aerial (UAV) and ground vehicles can only evaluate the crop from above. The visible leaves may show no disease symptoms, but lower, older leaves not visible from above can do. A mobile in-canopy sensor was developed, carried by a tractor to detect diseases in cereal crops. Photodiodes measure the reflected light in the red and infrared wavelength range at 10 different vertical heights in lateral directions. RESULTS: Significant differences occurred in the vegetation index NDVI of sensor levels operated inside and near the winter wheat canopy between infected (stripe rust: 2018, 2019 / leaf rust: 2020) and control plots. The differences were not significant at those sensor levels operated far above the canopy. CONCLUSIONS: Lateral reflectance measurements inside the crop canopy are able to distinguish between disease-infected and healthy crops. In future mobile in-canopy scouting could be an extension to the common above-canopy scouting praxis for making spraying decisions by the farmer or decision support systems. © 2021 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Rapid and low-cost insect detection for analysing species trapped on yellow sticky traps.

While insect monitoring is a prerequisite for precise decision-making regarding integrated pest management (IPM), it is time- and cost-intensive. Low-cost, time-saving and easy-to-operate tools for automated monitoring will therefore play a key role in increased acceptance and application of IPM in practice. In this study, we tested the differentiation of two whitefly species and their natural enemies trapped on yellow sticky traps (YSTs) via image processing approaches under practical conditions. Using the bag of visual words (BoVW) algorithm, accurate differentiation between both natural enemies and the Trialeurodes vaporariorum and Bemisia tabaci species was possible, whereas the procedure for B. tabaci could not be used to differentiate this species from T. vaporariorum. The decay of species was considered using fresh and aged catches of all the species on the YSTs, and different pooling scenarios were applied to enhance model performance. The best performance was reached when fresh and aged individuals were used together and the whitefly species were pooled into one category for model training. With an independent dataset consisting of photos from the YSTs that were placed in greenhouses and consequently with a naturally occurring species mixture as the background, a differentiation rate of more than 85% was reached for natural enemies and whiteflies.

Early Detection of Stripe Rust in Winter Wheat Using Deep Residual Neural Networks.

Stripe rust (Pst) is a major disease of wheat crops leading untreated to severe yield losses. The use of fungicides is often essential to control Pst when sudden outbreaks are imminent. Sensors capable of detecting Pst in wheat crops could optimize the use of fungicides and improve disease monitoring in high-throughput field phenotyping. Now, deep learning provides new tools for image recognition and may pave the way for new camera based sensors that can identify symptoms in early stages of a disease outbreak within the field. The aim of this study was to teach an image classifier to detect Pst symptoms in winter wheat canopies based on a deep residual neural network (ResNet). For this purpose, a large annotation database was created from images taken by a standard RGB camera that was mounted on a platform at a height of 2 m. Images were acquired while the platform was moved over a randomized field experiment with Pst-inoculated and Pst-free plots of winter wheat. The image classifier was trained with 224 × 224 px patches tiled from the original, unprocessed camera images. The image classifier was tested on different stages of the disease outbreak. At patch level the image classifier reached a total accuracy of 90%. To test the image classifier on image level, the image classifier was evaluated with a sliding window using a large striding length of 224 px allowing for fast test performance. At image level, the image classifier reached a total accuracy of 77%. Even in a stage with very low disease spreading (0.5%) at the very beginning of the Pst outbreak, a detection accuracy of 57% was obtained. Still in the initial phase of the Pst outbreak with 2 to 4% of Pst disease spreading, detection accuracy with 76% could be attained. With further optimizations, the image classifier could be implemented in embedded systems and deployed on drones, vehicles or scanning systems for fast mapping of Pst outbreaks.

Sensor-based outdoor monitoring of insects in arable crops for their precise control.

The implementation of precision farming technologies into agricultural practice requires, among other things, precise determination of the extent and intensity of insect infestation in the farmer' fields. Manual insect identification is time-consuming and has low efficiency, especially for large fields. Therefore, scientists and practitioners devote much effort to the automatization of this process. There are two complementary approaches to insect identification: (i) direct, in which the insect (ultimately the species) is determined, and (ii) indirect, in which the damage caused by the insects is monitored and forms the basis on which to formulate the information about insect infestation. A mini-review of both approaches is presented in this work. Additionally, the advantages and disadvantages of each are briefly described. Methods of insect identification are still characterized by relatively small selectivity and efficiency, therefore it is necessary to keep searching for new methods and improve the development of existing ones. The goal of such systems should be to work in real time and be inexpensive to run, enabling widespread use amongst farmers. A possible solution seems to be integrating various techniques (sensor fusion) into a single measurement system. © 2020 Society of Chemical Industry.

Biochar, soil and land-use interactions that reduce nitrate leaching and N2O emissions: A meta-analysis.

Biochar can reduce both nitrous oxide (N2O) emissions and nitrate (NO3-) leaching, but refining biochar's use for estimating these types of losses remains elusive. For example, biochar properties such as ash content and labile organic compounds may induce transient effects that alter N-based losses. Thus, the aim of this meta-analysis was to assess interactions between biochar-induced effects on N2O emissions and NO3- retention, regarding the duration of experiments as well as soil and land use properties. Data were compiled from 88 peer-reviewed publications resulting in 608 observations up to May 2016 and corresponding response ratios were used to perform a random effects meta-analysis, testing biochar's impact on cumulative N2O emissions, soil NO3- concentrations and leaching in temperate, semi-arid, sub-tropical, and tropical climate. The overall N2O emissions reduction was 38%, but N2O emission reductions tended to be negligible after one year. Overall, soil NO3- concentrations remained unaffected while NO3- leaching was reduced by 13% with biochar; greater leaching reductions (>26%) occurred over longer experimental times (i.e. >30 days). Biochar had the strongest N2O-emission reducing effect in paddy soils (Anthrosols) and sandy soils (Arenosols). The use of biochar reduced both N2O emissions and NO3- leaching in arable farming and horticulture, but it did not affect these losses in grasslands and perennial crops. In conclusion, the time-dependent impact on N2O emissions and NO3- leaching is a crucial factor that needs to be considered in order to develop and test resilient and sustainable biochar-based N loss mitigation strategies. Our results provide a valuable starting point for future biochar-based N loss mitigation studies.

Impact of sensor-controlled variable-rate fungicide application on yield, senescence and disease occurrence in winter wheat fields.

BACKGROUND: Field experiments examining target-oriented variable-rate fungicide spraying were performed in 2015 and 2016. The spray volume was adapted in real time to the local green coverage level of winter wheat (Triticum aestivum L.), which was detected using a camera sensor. RESULTS: Depending on the growth heterogeneity in the three strip trials in 2015, fungicide savings in the sensor-sprayed strip compared with the adjacent uniformly sprayed strip were 44%, 45% and 1%. In the 2016 field trial, the saving was 12%. There was no greater level of senescence or disease occurrence, and no higher yield losses in the camera-controlled variable-rate sprayed strips compared with the adjacent uniformly sprayed strips. CONCLUSIONS: From an ecological and economical point of view, sensor-controlled variable-rate spraying technology, which uses the level of green crop coverage as the plant parameter to adapt the spray volume locally, can be an alternative to the common practice of uniform spraying. © 2017 Society of Chemical Industry.

Proximal Soil Sensing - A Contribution for Species Habitat Distribution Modelling of Earthworms in Agricultural Soils?

BACKGROUND: Earthworms are important for maintaining soil ecosystem functioning and serve as indicators of soil fertility. However, detection of earthworms is time-consuming, which hinders the assessment of earthworm abundances with high sampling density over entire fields. Recent developments of mobile terrestrial sensor platforms for proximal soil sensing (PSS) provided new tools for collecting dense spatial information of soils using various sensing principles. Yet, the potential of PSS for assessing earthworm habitats is largely unexplored. This study investigates whether PSS data contribute to the spatial prediction of earthworm abundances in species distribution models of agricultural soils. METHODOLOGY/PRINCIPAL FINDINGS: Proximal soil sensing data, e.g., soil electrical conductivity (EC), pH, and near infrared absorbance (NIR), were collected in real-time in a field with two management strategies (reduced tillage / conventional tillage) and sandy to loam soils. PSS was related to observations from a long-term (11 years) earthworm observation study conducted at 42 plots. Earthworms were sampled from 0.5 x 0.5 x 0.2 m³ soil blocks and identified to species level. Sensor data were highly correlated with earthworm abundances observed in reduced tillage but less correlated with earthworm abundances observed in conventional tillage. This may indicate that management influences the sensor-earthworm relationship. Generalized additive models and state-space models showed that modelling based on data fusion from EC, pH, and NIR sensors produced better results than modelling without sensor data or data from just a single sensor. Regarding the individual earthworm species, particular sensor combinations were more appropriate than others due to the different habitat requirements of the earthworms. Earthworm species with soil-specific habitat preferences were spatially predicted with higher accuracy by PSS than more ubiquitous species. CONCLUSIONS/SIGNIFICANCE: Our findings suggest that PSS contributes to the spatial modelling of earthworm abundances at field scale and that it will support species distribution modelling in the attempt to understand the soil-earthworm relationships in agroecosystems.

Soil pH mapping with an on-the-go sensor.

Soil pH is a key parameter for crop productivity, therefore, its spatial variation should be adequately addressed to improve precision management decisions. Recently, the Veris pH Manager™, a sensor for high-resolution mapping of soil pH at the field scale, has been made commercially available in the US. While driving over the field, soil pH is measured on-the-go directly within the soil by ion selective antimony electrodes. The aim of this study was to evaluate the Veris pH Manager™ under farming conditions in Germany. Sensor readings were compared with data obtained by standard protocols of soil pH assessment. Experiments took place under different scenarios: (a) controlled tests in the lab, (b) semicontrolled test on transects in a stop-and-go mode, and (c) tests under practical conditions in the field with the sensor working in its typical on-the-go mode. Accuracy issues, problems, options, and potential benefits of the Veris pH Manager™ were addressed. The tests demonstrated a high degree of linearity between standard laboratory values and sensor readings. Under practical conditions in the field (scenario c), the measure of fit (r(2)) for the regression between the on-the-go measurements and the reference data was 0.71, 0.63, and 0.84, respectively. Field-specific calibration was necessary to reduce systematic errors. Accuracy of the on-the-go maps was considerably higher compared with the pH maps obtained by following the standard protocols, and the error in calculating lime requirements was reduced by about one half. However, the system showed some weaknesses due to blockage by residual straw and weed roots. If these problems were solved, the on-the-go sensor investigated here could be an efficient alternative to standard sampling protocols as a basis for liming in Germany.