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In this data we provide different agronomic variables and sensor readings (reflectance spectra and canopy temperature) from an agricultural crop rotation under Mediterranean conditions. The data were obtained in Central Spain after collecting plant samples in a field experiment during two growing seasons: 2018-2020 and 2019-2021. These variables represent the crop response of two wheat (Triticum aestivum L.) genotypes to different precedent crops (legume versus non-legume), nitrogen fertilization and water levels, as well as the readings of leaf-clip and proximal sensors in different growth stages.+ Value of the data:Data relating agronomic and sensor information from different wheat genotypes under nitrogen and water interactions will be useful for understanding crop performance and optimizing irrigation and nitrogen fertilization. The present dataset could help researchers and farmers to identify suitable genotypes and their responses to nitrogen and water stress.+ Detailed description:The file ‘Grain yield, GNC and N output of wheat.xlsx’ contains the specific year in which each sample was taken is indicated in the database, the dry biomass (kg ha-1), carbon and nitrogen concentration (% C and %N), C/N ratio and N uptake (kg N ha-1) measured in wheat at flowering in the different treatments every year (Wheat Biomass.xlsx). In addition, the dataset contains the wheat grain yield (kg ha-1), % C, % N, C/N ratio and N output (kg N ha-1) at harvest in the two years were recorded. The files ‘Dualex readings.xlsx’ and ‘Greeseeker readings.xlsx’ contains the chlorophyll (Chl), flavonoid (Flav), anthocyanin (Anth) content and the nitrogen balance index (NBI, calculated as the ratio Chl/Flav) measured with the leaf-clip sensor Dualex®, and the normalized difference vegetation index (NDVI) taken with the proximal sensor GreenSeeker® at three different growth stages (GS) for all genotypes and treatments.The file ‘Reflectance of wheat.xlsx’ contains the hyperspectral wheat reflectance measured at five GS in all treatments.The file ‘Thermal data of wheat.xlsx’ contains the canopy temperature acquired at three GS in all treatments. Additionally, dry and wet bare soil temperature was measured as well as the air temperature. Suplementary material for the article is also available as a pdf file.These data are associated with the following article, please cite this article if data are used.Raya-Sereno, M. D., Camino, C., Pancorbo, J. L., Alonso-Ayuso, M., Gabriel, J. L., Beck, P. S. A., & Quemada, M. 2024. Assessing wheat genotype response under combined nitrogen and water stress scenarios coupling high-resolution optical and thermal sensors with radiative transfer models. European Journal of Agronomy, 154, 127102. https://doi.org/10.1016/j.eja.2024.127102 THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

Grassland production is sensitive to both precipitation and plant N accumulation and utilization, such that change in one variable influences grassland response to the second variable. We investigated effects of interannual variation in precipiation on the response of 'community'-scale values of relative growth rate (RGR) to two multiplicative components of RGR, nitrogen productivity (NP; rate of change in biomass/plant N), an index of N utilization efficiency, and plant N concentration ([N]), in two grassslands in Texas, USA. Grasslands included a planted mixture of perennial grass and forb species and a monoculture of the perennial C4 grass Panicum vigatum that was invaded by multiple plant species. RGR and its N components were measured at the spatial scale of 7-m diameter circular patches near the spring peak in mixture biomss during each of 5 years. We found that RGR varied substantially among patches and years and between the planted mixture and monoculture. RGR variation was strongly correlated with variation in NP. Precipitation during the 3 months prior to RGR measurement mediated that RGR response to NP by altering the correlation between NP and [N] in both grasslands. Reduced precipitation led to more negative NP-[N] correlation coefficients, which reduced proportional change in RGR per change in NP by as much as 30% even in the absence of a precipitation effect on means of RGR and NP. Our results highlight an under-appreciated aspect of the pervasive role of precipitation in grassland growth that was mediated via change in the growth benefit derived from plant N. We used remote sensing techniques to calculate relative growth rate (RGR), nitrogen productivity (NP), and plant N concentration ([N]) at the scale of 7-m diameter circular patches (n = 104) in each of two grassland types (mixture of perennial grass and forb species, planted monoculture of the grass switchgrass). 'Community'-scale values RGR and its N components (NP, [N]) were calculated near the spring biomass peak in each of 5 years. We examined correlations among spatial variation in RGR, NP, and [N] in each grassland as influenced by interannual variation in precipitation.

1. Although organic farming was originally promoted as an alternative farming system to address agronomic, environmental, and ecological issues, its conventionalisation has led to an intensification and specialisation of production. In light of this, several studies have questioned the environmental benefits of organic farming as well as its agronomic viability. Thus, there is a need to improve organic vegetable systems to reduce their environmental impact without affecting their productivity. To tackle this challenge, European farmers and researchers have recently started to focus on agroecological service crops (ASCs). However, few studies have simultaneously evaluated the agronomic, environmental, and ecological aspects of ASC management under different European pedo-climatic conditions. 2. We evaluated effects of the ASC management strategies: no-till roller crimping (NT-RC) and green manuring (T-GM) on cropping system performance using agronomic, environmental, and ecological indicators, to exemplify the need for multidimensional analysis to understand management implications for addressing environmental and agronomic challenges. We combined the results from eleven organic vegetable field trials conducted in seven European countries over a period of two years to test for general trends. 3. Our results provide solid evidence that NT-RC management across different pedo-climatic conditions in Europe enhances the activity density of ground and rove beetles, and improves both the potential energy recycling within the system and weed control. However, in NT-RC plots lower cash crop yield and quality, energetic efficiency of production, and activity density of spiders was observed compared to T-GM. 4. Synthesis and applications: Multidimensional analyses using agronomic, environmental, and ecological indicators are required to understand the implications of agricultural management in agroecosystem functioning. Introducing agroecological service crops combined with the use of no-till roller crimping is a promising strategy for improving agronomic performance (e.g., fewer weeds) and reducing environmental (e.g., increasing the potentially recyclable energy), and ecological (e.g., enhancing the activity density of beneficial taxa such as ground and rove beetles) impacts. However, our study also indicates a need for agronomic and environmental improvements while promoting a wider acceptance of this strategy.29-Nov-2021 -- Dataset specifications: This dataset gathers data from 11 organic arable vegetable field trials located in Belgium (BE), Denmark (DK), Estonia (EE), France (FR), Italy (IT), Slovenia (SI), and Spain (ES). Two parallel field experiment types were carried out during two crop cycles. Field experiment type A (FtA) involved the introduction of cold-rainy season ASCs into the crop rotation, followed by a spring-summer cash crop. Field experiment type B (FtB) was performed only at the IT and ES locations where the Mediterranean climatic conditions enabled introduction of the ASCs in the warm-dry season (i.e., summer), followed by the transplantation of an autumn-winter cash crop. This dataset contains: Ecological and environmental indicators: Activity density of ground (Carabidae) and rove (Staphylinidae) beetles and spiders (Araneae); Beta-glucosidase enzyme activity assessment; Nitrogen leaching potential measured by soil mineral nitrogen assessment at cash crop harvest; and the potentially recyclable energy use efficiency indicator (PRE-EUE) (Navarro-Miró, Iocola, et al., 2019). Agronomic indicators: Cash crop marketable yield, and the cash crop quality; The energy efficiency of the marketable production was determined by the energy-use efficiency indicator (M-EUE) (Barut, Ertekin, & Karaagac, 2011). Weed control was analysed by determining weed density (individuals m-2). Information about Repetition/block and sample can be found in Appendix "S1. Trial details". Repetition/block and sample are sometimes not included because the measure under consideration has been taken at higher level (i.e., ASC, termination, plot).

Dados de medições dendrométricas coletados em três testes de procedências e progênies de K. senegalensis no Brasil. THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

“Least-cost theory” posits that C3 plants should balance rates of photosynthetic water loss and carboxylation in relation to the relative acquisition and maintenance costs of resources required for these activities. Here we investigated the dependency of photosynthetic traits on climate and soil properties using a new Australia-wide trait dataset spanning 528 species from 67 sites. We tested the hypotheses that plants on relatively cold or dry sites, or on relatively more fertile sites, would typically operate at greater CO2 drawdown (lower ratio of leaf internal to ambient CO2, Ci:Ca) during light-saturated photosynthesis, and at higher leaf N per area (Narea) and higher carboxylation capacity (Vcmax 25) for a given rate of stomatal conductance to water, gsw. These results would be indicative of plants having relatively higher water costs than nutrient costs. In general, our hypotheses were supported. Soil total phosphorus (P) concentration and (more weakly) soil pH exerted positive effects on the Narea-gsw and Vcmax 25-gsw slopes, and negative effects on Ci:Ca. The P effect strengthened when the effect of climate was removed via partial regression. We observed similar trends with increasing soil cation exchange capacity and clay content, which affect soil nutrient availability, and found that soil properties explained similar amounts of variation in the focal traits as climate did. Although climate typically explained more trait variation than soil did, together they explained up to 52% of variation in the slope relationships and soil properties explained up to 30% of the variation in individual traits. Soils influenced photosynthetic traits as well as their coordination. In particular, the influence of soil P likely reflects Australia’s geologically ancient low-relief landscapes with highly leached soils. Least-cost theory provides a valuable framework for understanding trade-offs between resource costs and use in plants, including limiting soil nutrients. Data collection/generation of data: Light-saturated rates of photosynthesis were measured at ambient CO2 levels using a portable gas exchange system. Stomatal conductance to water vapor and Ci:Ca correspond to light-saturated rates of photosynthesis. Carboxylation capacity, Vcmax, was estimated via the one-point method when it was not provided from A-ci curves. Concentrations of leaf nitrogen and phosphorus were measured using an elemental analyzer. LMA was measured by dividing the leaf dry mass by the surface area of a fresh leaf. Climate data were extracted for each site from the eMAST data project. Soil data were measured for a subset of sites and extracted from the CSIRO SoilGrids for the remaining sites. Data processing: Raw data were used to calculate species means for all sites, which were then compiled to produce one data file. This was done using RStudio Version 1.3.959. Environmental/experimental conditions: All data were collected on native plant species growing under natural conditions in the field. Describe any quality-assurance procedures performed on the data: Ci:Ca values less than 0 or greater than 1 were excluded, as were stomatal conductance values greater than 3000 millimol per square meter per second. Vcmax 25 values greater than 500 micromol CO2 per square meter per second were also excluded from the final analysis. Microsoft Excel

This dataset contains information on surface-level photosynthetically active radiation, smoke plume location, aerosol optical depth, and cloud fraction from four publicly available sources: U.S. Department of Agriculture's UV-B Monitoring and Research Program (UVMRP) National Oceanic and Atmospheric Administration/National Enviromental Satellite, Data, and Information Service's Hazard Mapping System (HMS) Smoke Product National Aeronautics and Space Administration's Multi-Angle Implementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth Product (MCD19A2) National Aeronautics and Space Administration's Moderate Resolution Imaging Spectroradiometer (MODIS) Atmosphere L3 Daily Product (MOD08_D3, MYD08_D3) The dataset covers 10 UVMRP stations located across the contiguous U.S.: Davis, California Pullman, Washington Pawnee, Nunn, Colorado Poplar, Montana Fargo, North Dakota Billings, Oklahoma Grand Rapids, Minnesota Bondville, Illinois Starkville, Mississippi Geneva, New York These sites were selected to provide broad spatial coverage of the regions analyzed in the Brey et al. (2018) smoke climatology, capture much of the smoke variability across the U.S., align with agricultural areas, and reduce the impact of metropolitan air pollution. The UVMRP staff were instrumental in providing the underlying UVMRP data and advise on working with the data. Extensive cleaning was conducted to remove data anomalies, quality control issues, and high solar zenith angles (> 75 degrees). Additional processing of underlying records created additional factors, such as average diffuse fraction, used for analysis. We also averaged values to a daily resolution. A detailed description of the site selection, data cleaning, and data processing methods used to produce this final merged dataset are available in the article by Corwin et al. entitled "Smoke-driven changes in photosynthetically active radiation during the U.S. agricultural growing season." Wildfire smoke is frequently present over the U.S. during the agricultural growing season and will likely increase with climate change. Studies of smoke impacts have largely focused on air quality and human health; however, understanding smoke’s impact on photosynthetically active radiation (PAR) is essential for predicting how smoke affects plant growth. We compare surface shortwave irradiance and diffuse fraction (DF) on smoke-impacted and smoke-free days from 2006-2020 using data from multifilter rotating shadowband radiometers at ten U.S. Department of Agriculture (USDA) UV-B Monitoring and Research Program stations and smoke plume locations from operational satellite products. On average, 20% of growing season days are smoke-impacted, but smoke prevalence increases over time (r = 0.60, p < 0.05). Smoke presence peaks in the mid- to late growing season (i.e., July, August), particularly over the northern Rocky Mountains, Great Plains, and Midwest. We find an increase in the distribution of PAR DF on smoke-impacted days, with larger increases at lower cloud fractions. On clear-sky days, daily average PAR DF increases by 10 percentage points when smoke is present. Spectral analysis of clear-sky days shows smoke increases DF (average: +45%) and decreases total irradiance (average: -6%) across all six wavelengths measured from 368-870 nm. Optical depth measurements from ground and satellite observations both indicate that spectral DF increases and total spectral irradiance decreases with increasing smoke plume optical depth. Our analysis provides a foundation for understanding smoke’s impact on PAR, which carries implications for agricultural crop productivity under a changing climate.

Potato (Solanum tuberosum) is the main crop that is vegetatively propagated(cloning). It is the third most important food crop after wheat and rice in terms of human consumption. This crop is affected by various types of diseases like bacterial, fungal, and viral diseases. As, a traditional practice some viral disease are identified by visual symptoms and some by laboratory methods like Enzyme-linked immunosorbent assay (ELISA), real time reverse-transcription- polymerase chain reaction (RT-PCR) etc., the above methods are tedious, computational costs along with time will be high and it is requiring a more labor-intensive and controlled lab structure to carry out the experiment. So, there is an urgent need for developing an automated model which employs machine learning, deep learning methodology which are subset of Artificial Intelligence. This model helps agriculturist/ farmers for early detection of viral disease. and take effective measures for huge yield loss. The dataset shows different types of viral diseases that the potato crop foliar and tubers get effected with namley Mosaic Virus, Potato Leaf Roll Virus(PLRV), on foliar, and on tuber Potato Spindle Tuber Viriod (PSTVD), Potato Virus Y(PVY)-tuber cracking. The dataset of foliar and tuber comprises of Nineteen hundred and seventy two images Mosaic(666), PLRV(527), Healthy leaf(135), PSTVD(85), PVY cracking (559). Each image of size in pixels is 4288*2848 and camera employed is Nikon D90,its configuration details include f-stop-f/5.3, ISO speed -ISO-250, focal length-80mm, contrast -medium, No flash. The dataset is collected from potato fields of University of Agricultural Sciences, Dharwad where 3 acres of land is cultivated with certified seeds from modipuram regional Centre and 1 acre is cultivated with uncertified seeds. The scientist from the research institute shared the image data from Indian Council of Agricultural Research- Central Potato Research Institute (ICAR- CPRI) shimla. The data set is captured in the day light by placing the leaf or tuber on black background. The image data collected has different symptoms like plant affected with mosaic virus has yellow color spread throughout the leaf the leaf can be classified as mild, medium and severe mosaic. The color feature plays a major role in classifying the mosaic viral disease the symptoms include yellow color propagated throughout the leaf. The PLRV symptoms are rolling of leaf towards upward direction the texture of the leaf is little crunchy in nature. The main features here are shape and texture for accurately classifying the disease. In tuber PSTVD where the tubers are elongated that is based on shape features the classifier gives the accurate results. PVY cracking it is one of the strains of potato virus Y where the tubers have cracks on external part which is noninfectious. The cracks generally start at bud and extend lengthwise. THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

Forest harvesting alters habitat, impacts wildlife, and disrupts ecosystem function. Across the boreal forest of Canada, forest harvesting affects ungulate prey species and their predators, with cascading impacts on other species, including threatened woodland caribou. We used camera and vegetation data and occupancy modeling to investigate what characteristics in and around forestry harvest blocks influenced the occupancy and co‐occurrence of primary prey (elk, moose, mule deer, white‐tailed deer) and predators (black bear, cougar, grizzly bear, wolf) in harvest blocks. Occupancy was linked to forage, the surrounding habitat and anthropogenic disturbance, and silviculture practices. Black and grizzly bear occupancy was influenced by the presence of deer, and bear–deer co‐occurrence was influenced by site‐specific silviculture practices and surrounding anthropogenic disturbance. In the context of caribou recovery, our results indicate that forestry within caribou ranges could consider site‐specific silviculture practices and landscape‐level planning to reduce use of harvest blocks by primary prey. Future caribou recovery efforts may also consider the roles of deer and bears in caribou predation risk. Our study provides the first insights into the impacts of forestry and silviculture on boreal forest predator–prey co‐occurrence and provides practical management applications to mitigate the impacts of anthropogenic activities on the boreal forest ecosystem into the future. Wildlife (detection) data were collected using remote cameras and processed using detectionHistory in camtrapR to generate detection histories. Site data include forest stand characteristics, hiding cover, and forage (vegetation) collected at vegetation survey plots, and silviculture data obtained from forestry partners. Camera data were processed using camtrapR. All datasets were processed and analyzed in R Studio. Any program that can open csv files. R Studio is required to analyze datasets.