295 research outputs found

    Digital agriculture: research, development and innovation in production chains.

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    Digital transformation in the field towards sustainable and smart agriculture. Digital agriculture: definitions and technologies. Agroenvironmental modeling and the digital transformation of agriculture. Geotechnologies in digital agriculture. Scientific computing in agriculture. Computer vision applied to agriculture. Technologies developed in precision agriculture. Information engineering: contributions to digital agriculture. DIPN: a dictionary of the internal proteins nanoenvironments and their potential for transformation into agricultural assets. Applications of bioinformatics in agriculture. Genomics applied to climate change: biotechnology for digital agriculture. Innovation ecosystem in agriculture: Embrapa?s evolution and contributions. The law related to the digitization of agriculture. Innovating communication in the age of digital agriculture. Driving forces for Brazilian agriculture in the next decade: implications for digital agriculture. Challenges, trends and opportunities in digital agriculture in Brazil

    Unmanned aerial vehicle and proximal sensing of vegetation indices in olive tree (Olea europaea)

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    Remote and proximal sensing platforms at the service of precision olive growing are bringing new development possibilities to the sector. A proximal sensing platform is close to the vegetation, while a remote sensing platform, such as unmanned aerial vehicle (UAV), is more distant but has the advantage of rapidity to investigate plots. The study aims to compare multispectral and hyperspectral data acquired with remote and proximal sensing platforms. The comparison between the two sensors aims at understanding the different responses their use can provide on a crop, such as olive trees having a complex canopy. The multispectral data were acquired with a DJI multispectral camera mounted on the UAV Phantom 4. Hyperspectral acquisitions were carried out with a FieldSpec® HandHeld 2™ Spectroradiometer in the canopy portions exposed to South, East, West, and North. The multispectral images were processed with Geographic Information System software to extrapolate spectral information for each cardinal direction’s exposure. The three main Vegetation indices were used: normalized difference vegetation index (NDVI), normalized difference red-edge index (NDRE), and modified soil adjusted vegetation index (MSAVI). Multispectral data e could describe the total variability of the whole plot differentiating each single plant status. Hyperspectral data were able to describe vegetation conditions more accurately; they appeared to be related to the cardinal exposure. MSAVI, NDVI, and NDRE showed correlation r =0.63**, 0.69**, and 0.74**, respectively, between multispectral and hyperspectral data. South and West exposures showed the best correlations with both platforms

    Digital agriculture: research, development and innovation in production chains.

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    Digital transformation in the field towards sustainable and smart agriculture. Digital agriculture: definitions and technologies. Agroenvironmental modeling and the digital transformation of agriculture. Geotechnologies in digital agriculture. Scientific computing in agriculture. Computer vision applied to agriculture. Technologies developed in precision agriculture. Information engineering: contributions to digital agriculture. DIPN: a dictionary of the internal proteins nanoenvironments and their potential for transformation into agricultural assets. Applications of bioinformatics in agriculture. Genomics applied to climate change: biotechnology for digital agriculture. Innovation ecosystem in agriculture: Embrapa?s evolution and contributions. The law related to the digitization of agriculture. Innovating communication in the age of digital agriculture. Driving forces for Brazilian agriculture in the next decade: implications for digital agriculture. Challenges, trends and opportunities in digital agriculture in Brazil.Translated by Beverly Victoria Young and Karl Stephan Mokross

    Remote Sensing of Grassland Variables Across Seasons and Using Multiple Spectral Devices

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    The regeneration and conservation of semi-natural grasslands is considered important to land managers such as Natural England, especially grasslands protected by legislation such as UK Biodiversity Action Plan (BAP) priority habitats or Sites of Special Scientific Interest (SSSI). Monitoring the condition of these grasslands is necessary, but conventional methods of measuring grassland condition are time consuming and limited in their spatial coverage. This thesis tested the hypothesis that remote sensing (RS) techniques can provide a cost- and time-effective solution to grassland condition monitoring. This thesis used partial least squares regression (PLSR) to explore the relationship between grassland spectral reflectance and the mass or % cover of a range of condition-related grassland variables plus a metric (an average and equally weighted measure of whether the CSM criteria were sufficiently met referred to as CSMcondition) representing condition as defined in the UK by the Common Standards Monitoring. The relationship between grassland variables and CSM-condition was also assessed. Each study differed with the grasslands targeted, the seasons when data were collected and the devices deployed. The first study was conducted on a range of different grassland types, the second study was conducted on chalk grasslands of differing levels of improvement across three seasons (spring, summer and autumn) and the third study was conducted on these same chalk grasslands but using data from three different spectral devices collected during the summer. All three studies were conducted at patch level (1m2) with the third study including the extrapolated predictions from trained statistical models to field level (200x1m) using spectral data from a CROPSCAN MSR 16R hand-held device. All three studies used spectral data from a CROPSCAN MSR 16R hand-held device and the third study included the analysis of spectral data from a Spectral Vista Corporation (SVC) HR1024i hand-held device and a Rikola camera mounted on an uncrewed aerial vehicle (UAV). The results suggest that some of the condition-related variables considered in this thesis are predicted with reasonable accuracy and precision at patch level, but producing reliable results requires a sufficient quantity of data to train the statistical models (at least 30 quadrats of samples in the context of this thesis) especially if the results are to be extrapolated to field level as additional data are required for the external validation of the results. When analysing data collected at patch level during the summer; the mass of bryophytes, dead material and graminoids plus the % cover of forbs can be predicted to a moderate level of accuracy when analysing data from all seven grasslands. When analysing data from all Parsonage Down NNR grasslands; the mass of bryophytes, the % cover of live material, % cover-based live:dead ratio and CSM-condition could be predicted to a high level of accuracy. Moisture content plus the % cover of dead material, forbs and gram:forb ratio were all predicted to a moderate level of accuracy as well as CSM-condition predicted by grassland variable values. When using data from all Ingleborough NNR grasslands; the % cover of forbs and biomass plus the mass of bryophytes, dead material and live material could be predicted to a moderate level of accuracy. When using patch level data collected across three seasons; the % cover of dead material, live material and live:dead ratio plus the mass of graminoids could be predicted when using three seasons of data collected on one grassland, or for all three Parsonage grasslands, to at least a moderate level of accuracy although some models trained with % cover data had a high accuracy. Forbs (mass and % cover) plus the mass of gram:forb ratio, live material and live:dead ratio could be predicted to at least a moderate level of accuracy for some grasslands. When using data from all grasslands collected in one season to predict grassland variables; the mass of a range of grassland variables could be predicted to a moderate level of accuracy for the spring and autumn months but not when using % cover data. When the use of data from three different spectral devices were compared to see which produced the most accurate models; using CROPSCAN and SVC data produced similar results, with slightly stronger results from the CROPSCAN, but using data from the Rikola camera produced weaker results. When the results of trained PLSR models were extrapolated to field level, the projected predicted grassland variable values from models trained with CROPSCAN MSR 16R data looked promising but the results have not been externally validated using a separate data set. Variable importance in projection (VIP) was used to establish which spectral bands are most important for predicting each grassland variable plus CSM-condition and which grassland variables are most important in predicting CSM-condition. It was generally found that the upper parts of the spectral range of each device (NIR and SWIR) were the most crucial for predicting grassland variables, with the red edge (647nm) and particular visible bands (470nm) also having some importance. When grassland variables were used to predict CSM-condition, which variables were most important depended on whether the grassland variable was mass-based or % cover-based. When using mass data; graminoid:forb ratio mass and live:dead ratio masswere consistently important across grasslands and seasons with biomass, graminoid:bryophyte mass and moisture content having importance for particular grasslands and seasons. When using % cover data; forbs cover, graminoids cover and live:dead ratio cover were consistently important across grasslands and seasons with dead material cover and live material cover having importance for particular grasslands and seasons. This thesis also explored which grassland variables could be predicted most consistently by calculating coefficient of variance (CV) on data collected across grasslands, seasons and/or using different spectral devices. Overall, these results suggest that none of the grassland variables considered in this thesis can be consistently predicted strongly across all the different grasslands or seasons considered in this thesis. When using % cover variable data; forbs cover and live:dead ratio cover produced relatively consistent results across grasslands, seasons and when using data from different spectral devices while bryophytes cover, graminoids cover and gram:forb ratio cover were consistent under some specific circumstances. When using mass data; moisture content stands out as relatively consistent compared to other variables across grasslands, seasons and when using different spectral devices. When using CROPSCAN MSR 16R spectral data as predictors, live material mass and live:dead ratio mass plus biomass produced relatively consistent results. Dead material mass produced relatively consistent results when using different devices as predictors, but not when using data collected over different season

    Trend assessment of changing climate patterns over the major agro-climatic zones of Sindh and Punjab

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    The agriculture sector, due to its significant dependence on climate patterns and water availability, is highly vulnerable to changing climate patterns. Pakistan is an agrarian economy with 30% of its land area under cultivation and 93% of its water resources being utilized for agricultural production. Therefore, the changing climate patterns may adversely affect the agriculture and water resources of the country. This study was conducted to assess the climate variations over the major agro-climatic zones of Sindh and Punjab, which serve as an important hub for the production of major food and cash crops in Pakistan. For this purpose, the climate data of 21 stations were analyzed using the Mann–Kendall test and Sen's slope estimator method for the period 1990–2022. The results obtained from the analysis revealed that, in Sindh, the mean annual temperature rose by ~0.1 to 1.4°C, with ~0.1 to 1.2°C in cotton-wheat Sindh and 0.8 to 1.4°C in rice-other Sindh during the study period. Similarly, in Punjab, the mean annual temperature increased by ~0.1 to 1.0°C, with 0.6 to 0.9°C in cotton-wheat Punjab and 0.2 to 0.6°C in rainfed Punjab. Seasonally, warming was found to be highest during the spring season. The precipitation analysis showed a rising annual precipitation trend in Sindh (+30 to +60 mm) and Punjab (+100 to 300 mm), while the monsoon precipitation increased by ~50 to 200 mm. For winter precipitation, an upward trend was found in mixed Punjab, while the remaining stations showed a declining pattern. Conclusively, the warming temperatures as found in the analysis may result in increased irrigation requirements, soil moisture desiccation, and wilting of crops, ultimately leading to low crop yield and threatening the livelihoods of local farmers. On the other hand, the increasing precipitation may favor national agriculture in terms of less freshwater withdrawals. However, it may also result in increased rainfall-induced floods inundating the crop fields and causing water logging and soil salinization. The study outcomes comprehensively highlighted the prevailing climate trends over the important agro-climatic zones of Pakistan, which may aid in devising an effective climate change adaptation and mitigation strategy to ensure the state of water and food security in the country

    CERNAS: Current Evolution and Research Novelty in Agricultural Sustainability

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    Climate changes pose overwhelming impacts on primary production and, consequently, on agricultural and animal farming. Additionally, at present, agriculture still depends strongly on fossil fuels both for energy and production factors ,such as synthetized inorganic fertilizers and harmful chemicals such as pesticides. The need to feed the growing world population poses many challenges. The need to reduce environmental impacts to a minimum, maintain healthy ecosystems, and improve soil microbiota are central to ensuring a promising future for coming generations. Livestock production under cover crop systems helps to alleviate compaction so that oxygen and water can sufficiently flow in the soil, add organic matter, and help hold soil in place, reducing crusting and protecting against erosion. The use of organic plant production practices allied to the control of substances used in agriculture also decisively contributes to alleviating the pressure on ecosystems. Some of the goals of this new decade are to use enhanced sustainable production methodologies to improve the input/output ratios of primary production, reduce environmental impacts, and rely on new innovative technologies. This reprint addresses original studies and reviews focused on the current evolution and research novelty in agricultural sustainability. New developments are discussed on issues related to quality of soil, natural fertilizers, or the sustainable use of land and water. Also, crop protection techniques are pivotal for sustainable food production under the challenges of the Sustainable Development Goals of the United Nations, allied to innovative weed control methodologies as a way to reduce the utilization of pesticides. The role of precision and smart agriculture is becoming more pertinent as communication technologies improve at a rapid rate. Waste management, reuse of agro-industrial residues, extension of shelf life, and use of new technologies are ways to reduce food waste, all contributing to higher sustainability in food supply chains, leading to a more rational use of natural resources. The unquestionable role of bees as pollinators and contributors to biodiversity is adjacent to characterizing beekeeping activities, which in turn contributes, together with the valorization of endemic varieties of plant foods, to the development of local communities. Finally, the short circuits and local food markets have a decisive role in the preservation and enhancement of rural economies.info:eu-repo/semantics/publishedVersio

    Land Surface Monitoring Based on Satellite Imagery

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    This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest fires and drought

    CERNAS – Current Evolution and Research Novelty in Agricultural Sustainability

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    This book addresses original studies and reviews focused on the current evolution and research novelty in agricultural sustainability. New developments are discussed on issues related with quality of soil, natural fertilizers or the sustainable use of land and water. Also crop protection techniques are pivotal for the sustainable food production under the challenges of the Sustainable Development Goals of the United Nations, allied to innovative weed control methodologies, as a way to reduce the utilization of pesticides. The role of precision and smart agriculture is becoming more pertinent as the communication technologies improve at a high rate. Waste management, reuse of agro industrial residues, extension of shelf life and use of new technologies are ways to reduce food waste, all contributing to a higher sustainability of the food supply chains, leading to a more rational use of natural resources. The unquestionable role of bees as pollinators and contributors for biodiversity is subjacent to the work of characterization of beekeeping activities, which in turn contribute, together with the valorization of endemic varieties of plant foods, for the development of local communities. Finally, the short circuits and local food markets have a decisive role in the preservation and enhancement of rural economies.info:eu-repo/semantics/publishedVersio

    Climate resilient and sustainable forest management : IBFRA conference 28-31 August 2023. Book of abstracts

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    The 20th IBFRA (The International Boreal Forest Research Association) conference held in Helsinki Finland 28-31 August 2023 brings together researchers, companies, policy makers and members of the civil society. The conference main theme is Climate resilient and sustainable forest management. The abstracts of the conference are in this publication
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