The hydro-mechanical properties of the vadose zone are strongly influenced by seasonal cycles. The hydraulic behavior of this zone is determined by the coupling of biotic and abiotic factors. The biotic factors are controlled by the physiology and anatomy of the vegetation growing in the area, while the abiotic factors depend on the local soil characteristics, such as water content, void ratio, and matrix structure. In this laboratory-scale investigation, we assess the influence of active biomass, water content, and suction on the particle and pore structure rearrangement. We use x-ray computed tomography and 3D digital image correlation to quantify plant roots at different stages of growth, soil deformation, and water content fluctuations. Our results show that the bulk porosity of vegetated soil is strongly affected by the induced water cycles. The global micro-structure rearrangement due to the double effects of plant water uptake and induced drying-wetting cycles translates into a final bulk porosity increase. @en

Agricultural field polygons within smallholder farming systems are essential to facilitate the collection of geo-spatial data useful for farmers, managers, and policymakers. However, the limited availability of training labels poses a challenge in developing supervised methods to accurately delineate field boundaries using Earth observation (EO) data. This letter introduces an open dataset for training and benchmarking machine learning methods to delineate agricultural field boundaries in polygon format. The large-scale dataset consists of 439 001 field polygons divided into 62 tiles of approximately 5× 5 km distributed across Vietnam and Cambodia, covering a range of fields and diverse landscape types. The field polygons have been meticulously digitized from satellite images, following a rigorous multistep quality control process and topological consistency checks. Multitemporal composites of Sentinel-2 (S2) images are provided to ensure cloud-free data. We conducted an experimental analysis testing a state-of-the-art deep learning (DL) workflow based on fully convolutional networks (FCNs), contour closing, and polygonization. We anticipate that this large-scale dataset will enable researchers to further enhance the delineation of agricultural fields in smallholder farms and to support the achievement of the Sustainable Development Goals (SDGs). The dataset can be downloaded from https://doi.org/10.17026/dans-xy6-ngg6.

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Climate change strongly affects the hydro-mechanical properties of soil. Due to drought and heavy rains the soil is subjected to severe hydro-mechanical loads, that, in turn, alter the microstructure of the soil. The most affected area is the so-called vadose zone, the layer of soil situated between the ground surface and the water table. Here the presence of vegetation has a strong impact, related to the elongation/expansion of the root architecture and the hydro-mechanical interactions with soil. Additionally, the presence of plant roots facilitate the evapotranspiration process from deeper soil layers. The research presents an experimental investigation, aimed to reproduce the typical hydro-mechanical conditions as found in the vadose zone in controlled laboratory conditions. Drying-wetting cycles are induced in soils samples, where maize plants are free to sprout and develop as well as in reference non-vegetated samples. The water content and distribution within the soil matrix are studied through 4D (3D+time) in-vivo x-ray computed tomography and effects on the soil-root microstructure are quantified with 3D image analysis. Those are correlated with above ground measurements such as fluorescence (through a spectroradiometer) that, in turn, provides leaf water potential, and the stomatal conductance that controls the evapotranspiration.@en

Vegetated soil plays an essential role in confronting climate change. Soil, together with its ecosystem, stores vast amounts of carbon; it is also the construction material most widely used for the built environment. The expected impacts of climate change, such as extreme wetting-drying cycles, pose an urgent need to understand the interplay between soil deformation, root growth, and water/solute uptake. The key to this challenge lies in the extension of unsaturated soil mechanics to incorporate bio-hydrological processes, such as root growth and water uptake. In this paper, we first provide an overview of the state-of-the-art knowledge of root-zone mechanics and bio-hydrology. We identify the main knowledge gaps and suggest an integrated, bottom-to-top approach to develop a multidisciplinary understanding of soil-water-root interaction. We explain how emerging experimental and numerical methods can be used to study rooted soil under wetting–drying cycles. We focus on the biophysical processes at the scale of plant roots, soil particles and their interfaces, and discuss potential up-scaling to the continuum/field scale. An outlook on possible further research involves effects of temperature and microbial activities.

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Planting a cover crop between the main cropping seasons is an agricultural management measure with multiple potential benefits for sustainable food production. In the maize production system of the Netherlands, an effective establishment of a winter cover crop is important for reducing nitrogen leaching to groundwater. Cover crop establishment after maize cultivation is obliged by law for sandy soils and consequently implemented on nearly all maize fields, but the winter-time vegetative ground cover varies significantly between fields. The objectives of this study are to assess the variability in winter vegetative cover and evaluate to what extent this variability can be explained by the timing of cover crop establishment and weather conditions in two growing seasons (2017–2018). We used Sentinel-2 satellite imagery to construct NDVI time series for fields known to be cultivated with maize within the province of Overijssel. We fitted piecewise logistic functions to the time series in order to estimate cover crop sowing date and retrieve the fitted NDVI value for 1 December (NDVIDec). We used NDVIDec to represent the quality of cover crop establishment at the start of the winter season. The Sentinel-2 estimated sowing dates compared reasonably with ground reference data for eight fields (RMSE = 6.6 days). The two analysed years differed considerably, with 2018 being much drier and warmer during summer. This drought resulted in an earlier estimated cover crop sowing date (on average 19 days) and an NDVIDec value that was 0.2 higher than in 2017. Combining both years and all fields, we found that Sentinel-2 retrieved sowing dates could explain 55% of the NDVIDec variability. This corresponded to a positive relationship (R2 = 0.50) between NDVIDec and the cumulative growing degree days (GDD) between sowing date and 1 December until reaching 400 GDD. Based on cumulative GDD derived from two weather stations within Overijssel, we found that on average for the past three decades a sowing date of 19 September (± 7 days) allowed to attain these 400 GDD; this provides support for the current legislation that states that from 2019 onwards a cover crop should be sown before 1 October. To meet this deadline, while simultaneously ascertaining a harvest-ready main crop, in practice implies that undersowing of the cover crop during spring will gain importance. Our results show that Sentinel-2 NDVI time series can assess the effectiveness and timing of cover crop growth for small agricultural fields, and as such has potential to inform regulatory frameworks as well as farmers with actionable information that may help to reduce nitrogen leaching.@en

An effective establishment of a winter cover crop is important for reducing ni-trogen leaching to groundwater in the maize-based cropping system of the Netherlands. Cover crop establishment after maize cultivation is obliged by law for sandy soils and consequently imple-mented in nearly all maize fi elds, but the vegetative ground cover in winter varies signifi cantly between fi elds. This study’s objective is to evaluate to what extent dif-ferences in winter vegetative cover can be explained by the timing of cover crop es-tablishment and weather conditions in two growing seasons (2017-18). We focussed on the Province of Overijssel.@en

Crop growth stage is critical for making decisions in nutrient management and for evaluating crop productivity. In this study, a simple visible and near-infrared (V-NIR) camera system was developed for monitoring the leaf area index (LAI) and quantifying the quick growth stage (QGS) of Italian ryegrass. RAW format images in the red, green and NIR channels over two growing seasons of 2014–15 and 2015–16 were captured hourly each day by the V-NIR camera system installed in three Italian ryegrass fields at the farm of Hiroshima University. Multiple linear regression (MLR) models that predict the forage LAI from the imagery data were calibrated and validated, with high coefficient of determination (R2 = 0.79) and low root-mean-square error (RMSE = 1.09) between the measured and predicted LAIs. The predicted LAI to which three vegetation indices were compared was fitted against a logistic model to extract forage QGS from smoothed time-series data under various micro-meteorological and nutrient conditions. The result shows the time-series data of LAI can be applied for mon-itoring seasonal changes regardless of the environmental conditions. The RMSE of the predicted phenology dates against the field-measured LAI was 0.58 and 5.2 days for the start- and end-QGS, respectively, under the high-yield condition in season 1. However, in season 2, only the start-QGS was identifiable, with an RMSE of 2.65 days under the nutritional stress condition. The forage LAI and QGS were predicted and identified with acceptable accuracy and reliability, which suggests that the V-NIR camera system can be employed as a cost-effective approach for monitoring seasonal changes in crop growth, aiding in better personalized crop and nutrient management.@en

Automated monitoring systems with different temporal and spatial resolutions can achieve precision agriculture management. Unmanned aerial vehicle (UAV) systems open new possibilities for effectively characterizing the variability within cropping systems with high spatial and temporal resolution. In this study, a UAV with a low-cost visible and near-infrared camera assessed the spatial variability in the herbage biomass (BM) and leaf area index (LAI) in an Italian ryegrass field. Using multiple linear regression (MLR) models, high coefficients of determination (R2) and low root-mean-squared error (RMSE) values were obtained between the observed and predicted herbage BM (R2 = 0.84, RMSE = 90.43 g m−2) and LAI (R2 = 0.88, RMSE = 0.82). The MLR models successfully recovered high-resolution spatial distributions of the herbage BM and LAI from the ortho-photos. The reconstructed maps verified that the proposed method can effectively characterize spatial field variations and assess forage growth to optimize field-level forage crop management.@en

This research was conducted to exploit the waste of used plant parts from the widely marketed moth orchid cultivar (Phalaenopsis Sogo Yukidian “V3”). Various extracts of roots, stems, and leaves were evaluated for total phenolics, total flavonoids, and antioxidant capacity. The bound extract from stems contained the highest total phenolics (5.092 ± 0.739 mg GAE (gallic acid equivalent)/g DW (dry weight)). The maximum total flavonoids (2.218 ± 0.021 mg RE (rutin equivalent)/g DW) were found in the hexane extract of leaves. Ethyl acetate extract from roots showed the greatest antioxidant activity compared to other extracts. Of these extracts, the IC50 values of these samples were 0.070 mg/mL, and 0.450 mg/mL in a free radical 1,-diphenyl-picryl-hydrazyl (DPPH) assay and reducing power method, respectively. The lipid peroxidation inhibition (LPI) was found to be 94.2% using the β-carotene bleaching method. Five phenolic compounds including caffeic acid, syringic acid, vanillin, ellagic acid, and cinnamic acid were quantified by high performance liquid chromatography (HPLC). It is suggested that the roots of the hybrid Phalaenopsis Sogo Yukidian “V3” cultivar may be exploited as an effective source of antioxidants.@en

Concentrations of chlorophyll-a (Chl-a) and total suspended solids (TSS) are significant parameters used to assess water quality. The objective of this study is to establish a quantitative model for estimating the Chl-a and the TSS concentrations in irrigation ponds in Higashihiroshima, Japan, using field hyperspectral measurements and statistical analysis. Field experiments were conducted in six ponds and spectral readings for Chl-a and TSS were obtained from six field observations in 2014. For statistical approaches, we used two spectral indices, the ratio spectral index (RSI) and the normalized difference spectral index (NDSI), and a partial least squares (PLS) regression. The predictive abilities were compared using the coefficient of determination (R2), the root mean squared error of cross validation (RMSECV) and the residual predictive deviation (RPD). Overall, iterative stepwise elimination based on PLS (ISE–PLS), using the first derivative reflectance (FDR), showed the best predictive accuracy, for both Chl-a (R2 = 0.98, RMSECV = 6.15, RPD = 7.44) and TSS (R2 = 0.97, RMSECV = 1.91, RPD = 6.64). The important wavebands for estimating Chl-a (16.97% of all wavebands) and TSS (8.38% of all wavebands) were selected by ISE–PLS from all 501 wavebands over the 400–900 nm range. These findings suggest that ISE–PLS based on field hyperspectral measurements can be used to estimate water Chl-a and TSS concentrations in irrigation ponds.@en

Information on the spring growth status of winter forage crops is crucial for evaluating productivity and nutrient management. This study aimed to determine the spring quick growth stage (QGS) of Italian ryegrass using a ground-based camera system. The camera system, installed in two Italian ryegrass fields at the farm of Hiroshima University, captured images automatically three times per day in red, green and blue channels over the growing season in 2012–13. Four presumed color intensities/indices were fitted using a logistic model to construct smoothed time-series data. Among the color intensities/indices, excess green was suggested to be the best parameter for monitoring seasonal changes. The root mean squared error of the estimated phenology dates against plant height was 7.7 days for the start-QGS and 2.8 days for the end-QGS. These results from a single year should be broadened to examine other methodologies for image processing and extended to multi-year data.@en

The objectives of this study were to determine the dominant frequency of plant species, and provide useful information toward the conservation management of animal feed in grazing areas, Hiroshima, Japan. The numbers of plant species were 32 and 21 in spring and summer, respectively. The appearance frequency of plant species more than 50% in the spring, consist of Veronica arvensis (75.57%), Trifolium repens (73.86%), Paspalum dilatatum (69.32%), Lamium purpureum (68.75%), Trifolium dubium (65.34%), Cerastium glomeratum (63.64%), whereas in summer, it consists of Paspalum notatum (98.30%), Trifolium repens (81.25%), Paspalum dilatatum (78.98%), and Kyllinga brevifolia (74.43%). It was observed that the plant high of the plant species were 16.64 and 21.55 cm; vegetation cover rates were 77.18 and 81.36%; chlorophyll content were 41.72 and 36.28 mg/g Fw, and species numbers were 17.91 and 10.18, in spring and summer, respectively. Findings of this research propose that Trifolium repens (clover), Rumex japonicus (weed), and Paspalum dilatatum (grass) are dominant species in the studied areas that can be utilized as animal feeds. @en