Remotely Sensed Agroclimatic Classification and Zoning in Water-Limited Mediterranean Areas towards Sustainable Agriculture

Agroclimatic classification identifies zones for efficient use of natural resources leading to optimal and non-optimal crop production. The aim of this paper is the development of a methodology to determine sustainable agricultural zones in three Mediterranean study areas, namely, “La Mancha Oriental” in Spain, “Sidi Bouzid” in Tunisia, and “Bekaa” valley in Lebanon. To achieve this, time series analysis with advanced geoinformatic techniques is applied. The agroclimatic classification methodology is based on three-stages: first, the microclimate features of the region are considered using aridity and vegetation health indices leading to water-limited growth environment (WLGE) zones based on water availability; second, landform features and soil types are associated with WLGE zones to identify non-crop-specific agroclimatic zones (NCSAZ); finally, specific restricted crop parameters are combined with NCSAZ to create the suitability zones. The results are promising as compared with the current crop production systems of the three areas under investigation. Due to climate change, the results indicate that these arid or semi-arid regions are also faced with insufficient amounts of precipitation for supporting rainfed annual crops. Finally, the proposed methodology reveals that the employment and use of remote sensing data and methods could be a significant tool for quickly creating detailed, and up to date agroclimatic zones

Agroclimatic zoning for eucalyptus in the state of Paraná and the new scenarios defined by global climate change.

Brazil is a country with one the largest areas of forest plantations in the world. The state of Paraná (PR) has the largest area of designated plantations in the country. The main cultivated species belong to the genus Eucalyptus. In this work, the areas of better favorability for planting the main species of commercial value eucalyptus were defined. Additionally, changes may also occur in these zones in the coming decades, due to global climate change. For this purpose, future scenarios were elaborated using a stochastic time series simulation software, to assess the possible changes of the climate and indicate potential consequences regarding the changes of eucalyptus plantation zones. The results show that there will be an increase in areas favorable to the commercial plantations of E. grandis and E. urograndis, species cultivated in the Cfa climate zone (subtropical zones). For E. benthamii, a species cultivated mostly in the Cfb climate zone (temperate zones), there will be a reduction of suitable areas for commercial plantations in Paraná, with displacement to areas located to the south and at higher altitudes, where edaphic limitations may occur

Spatial aspects of the design and targeting of agricultural development strategies:

Two increasingly shared perspectives within the international development community are that (a) geography matters, and (b) many government interventions would be more successful if they were better targeted. This paper unites these two notions by exploring the opportunities for, and benefits of, bringing an explicitly spatial dimension to the tasks of formulating and evaluating agricultural development strategies. We first review the lingua franca of land fragility and find it lacking in its capacity to describe the dynamic interface between the biophysical and socioeconomic factors that help shape rural development options. Subsequently, we propose a two-phased approach. First, development strategy options are characterized to identify the desirable ranges of conditions that would most favor successful strategy implementation. Second, those conditions exhibiting important spatial dependency – such as agricultural potential, population density, and access to infrastructure and markets – are matched against a similarly characterized, spatially-referenced (GIS) database. This process generates both spatial (map) and tabular representations of strategy-specific development domains. An important benefit of a spatial (GIS) framework is that it provides a powerful means of organizing and integrating a very diverse range of disciplinary and data inputs. At a more conceptual level we propose that it is the characterization of location, not the narrowly-focused characterization of land, that is more properly the focus of attention from a development perspective. The paper includes appropriate examples of spatial analysis using data from East Africa and Burkina Faso, and concludes with an appendix describing and interpreting regional climate and soil data for Sub-Saharan Africa that was directly relevant to our original goal.Spatial analysis (Statistics), Agricultural development., Burkina Faso., Africa, Sub-Saharan.,

AGROCLIMATIC ZONING FOR EUCALYPTUS IN THE STATE OF PARANÁ AND THE NEW SCENARIOS DEFINED BY GLOBAL CLIMATE CHANGE

Brazil is a country with one the largest areas of forest plantations in the world. The state of Paraná (PR) is in the first of three places of designated plantations areas in the country. The main cultivated species are the genus Eucalyptus. In this work, the areas of better favorability for planting the main species of commercial value eucalyptus were defined. Additionally, changes may also occur in these zones in the coming decades, due to global climate change. For this purpose, future scenarios were elaborated, using stochastic tie series simulation software, verifying the possible changes of the climate and indicating potential consequences regarding the changes of eucalyptus plantation zones. The results show that there will be an increase in areas favorable to the commercial plantations of E. grandis and E. urograndis, species cultivated in Cfa climate (subtropical zones). For E. benthamii, a species cultivated mostly in the Cfb (temperate zones), there will be a reduction of area for use in commercial plantations in Paraná, with displacement to areas located to the south and at higher altitudes, where edaphic limitations may occur

Use and Improvement of Remote Sensing and Geospatial Technologies in Support of Crop Area and Yield Estimations in the West African Sahel

In arid and semi-arid West Africa, agricultural production and regional food security depend largely on small-scale subsistence farming and rainfed crops, both of which are vulnerable to climate variability and drought. Efforts made to improve crop monitoring and our ability to estimate crop production (areas planted and yield estimations by crop type) in the major agricultural zones of the region are critical paths for minimizing climate risks and to support food security planning. The main objective of this dissertation research was to contribute to these efforts using remote sensing technologies. In this regard, the first analysis documented the low reliability of existing land cover products for cropland area estimation (Chapter 2). Then two satellite remote sensing-based datasets were developed that 1) accurately map cropland areas in the five countries of Sahelian West Africa (Senegal, Mauritania, Mali, Burkina Faso and Niger; Chapter 3), and 2) focus on the country of Mali to identify the location and prevalence of the major subsistence crops (millet, sorghum, maize and non-irrigated rice; Chapter 4). The regional cropland area product is distributed as the West African Sahel Cropland area at 30 m (WASC30). The development of the new dataset involved high density training data (380,000 samples) developed by USGS in collaboration with CILSS for training about 200 locally optimized random forest (RF) classifiers using Landsat 8 surface reflectances and vegetation indices and the Google Earth Engine platform. WASC30 greatly improves earlier estimates through inclusion of cropland information for both rainfed and irrigated areas mapped with a class-specific accuracy of 79% across the West Africa Sahel. Used as a mask in crop monitoring systems, the new cropland area data could bring critical insights by reducing uncertainties in xv identification of croplands as crop growth condition metrics are extracted. WASC30 allowed us to derive detailed statistics on cultivated areas in the Sahel, at country and agroclimatic scales. Intensive agricultural zones were highlighted as well. The second dataset, mapping crop types for the country of Mali, is meant to separate signals of different crop types for improved crop yield estimation. The crop type map was used to derive detailed agricultural statistics (e.g. acreage by crop types, spatial distribution) at finer administrative scales than has previously been possible. The crop fraction information by crop type extracted from the map, gives additional details on farmers preferences by regions, and the natural adaptability of different crop types. The final analysis of this dissertation explores the use of ensemble machine learning techniques to predict maize yield in Mali (Chapter 5). Climate data (precipitation and temperature), and vegetation indices (Normalized Difference Vegetation Index, NDVI, the Enhanced Vegetation Index, EVI, and the Normalized Difference Water Index, NDWI) are used as predictors, while actual yields collected in 2017 by the Malian Ministry of Agriculture are the reference data. Random forest presented better predictive performance as compared to boosted regression trees (BRT). Results showed that climate variables have more predictive power for maize yield compared to vegetation indices. Among vegetation indices, the NDWI appeared to be the most influential predictor, maybe because of water requirement of maize and the sensitivity of this index to water in semi-arid regions. Tested with two different independent datasets, one constituted by 20% of the reference information, and another including observed yields for year 2018 (a one-year-left analysis), maize yield predictions were promising for year 2017 (RMSE = 362 kg/ha), but showed higher error for 2018 (RMSE = 707 kg/ha). That is, the fitted model may not capture accurately year to year variabilities in predicted maize yield. In this analysis, predictions were limited to field samples (~600 fields) across the country of Mali. It would be valuable in the future to predict maize yield for each pixel of the new developed crop type map. That will lead to a detailed spatial analysis of maize yield, allowing identification of low yielding regions for targeted interventions which could improve food security. Keywords: Agricultural identification of croplands as crop growth condition metrics are extracted. WASC30 allowed us to derive detailed statistics on cultivated areas in the Sahel, at country and agroclimatic scales. Intensive agricultural zones were highlighted as well. The second dataset, mapping crop types for the country of Mali, is meant to separate signals of different crop types for improved crop yield estimation. The crop type map was used to derive detailed agricultural statistics (e.g. acreage by crop types, spatial distribution) at finer administrative scales than has previously been possible. The crop fraction information by crop type extracted from the map, gives additional details on farmers preferences by regions, and the natural adaptability of different crop types. The final analysis of this dissertation explores the use of ensemble machine learning techniques to predict maize yield in Mali (Chapter 5). Climate data (precipitation and temperature), and vegetation indices (Normalized Difference Vegetation Index, NDVI, the Enhanced Vegetation Index, EVI, and the Normalized Difference Water Index, NDWI) are used as predictors, while actual yields collected in 2017 by the Malian Ministry of Agriculture are the reference data. Random forest presented better predictive performance as compared to boosted regression trees (BRT). Results showed that climate variables have more predictive power for maize yield compared to vegetation indices. Among vegetation indices, the NDWI appeared to be the most influential predictor, maybe because of water requirement of maize and the sensitivity of this index to water in semi-arid regions. Tested with two different independent datasets, one constituted by 20% of the reference information, and another including observed yields for year 2018 (a one-year-left analysis), maize yield predictions were promising for year 2017 (RMSE = 362 kg/ha), but showed higher error for 2018 (RMSE = 707 kg/ha). That is, the fitted model may not capture accurately year to year variabilities in predicted maize yield. In this analysis, predictions were limited to field samples (~600 fields) across the country of Mali. It would be valuable in the future to predict maize yield for each pixel of the new developed crop type map. That will lead to a detailed spatial analysis of maize yield, allowing identification of low yielding regions for targeted interventions which could improve food security

Identification of Karst Forms Using LiDAR Technology: Cozumel Island, Mexico

Morphological relief analysis allows the identification of geomorphological forms and cartographic-environmental studies make extensive use of the medium (1:50,000) and large scale (1:250,000), where the topographical contrast is evident. However, at a detailed scale (<1:20,000) and for territories where the contrast of relief does not exceed 10 m in height, the morphological analyses must be adapted accordingly, because they contribute information to altimetry studies and to the topographic configuration of units. Thus, through visual interpretation and manipulation of high-resolution topographical LiDAR data from Cozumel Island, a relief analysis is presented at a detailed scale for the purpose of recognizing the geomorphological units of karst origin, using altimetry and slope cartography, digital models of elevation, and shading that permits the identification of 109 new exokarstic doline and uvala formations

Symposium on Climate Change and Variability – Agro Meteorological Monitoring and Coping Strategies for Agriculture. Oscarsborg, Norway. June 3-6 2008. Book of abstracts

‘The Symposium on Climate Change and Variability – Agro Meteorological Monitoring and Coping Strategies for Agriculture’ is organized by the Management Committee of COST734’ Impact of Climate Change and Variability on European Agriculture’ and the Commission for Agricultural Meteorology (CAgM) of WMO. The content of the symposium is closely connected to the themes of the working groups of COST734 and the term of reference of the ‘WMO Expert Team on Climate Risks in Vulnerable Areas” The symposium is devoted to the very important issue of agricultural crop production and climate change. The discussion is placed in the light of agro meteorology, in Europe and in the rest of the world. The event will serve as a meeting place between meteorologists and agronomists. The cooperation between these two groups of researchers is important to find optimal mitigation and adaptation strategies with respect to impacts of climate change/variability on agriculture. The book of abstracts for the symposium contains altogether 52 contributions. 26 of the abstracts are oral contributions, and 26 of the abstracts will be presented as posters.publishedVersio

This article is published in cooperation with Terclim 2022 (XIVth International Terroir Congress and 2nd ClimWine Symposium), 3-8 July 2022, Bordeaux, France.

Terroir is not just a geographical site, but also a complex concept aiming to express the "collective knowledge of the interactions" between the environment and the vines mediated through human action, "providing distinctive characteristics" to the final product (OIV 2010).In the popular press, it is often treated and communicated without a proper understanding of the mechanistic relationships between the wine characteristics and the site. These relationships are primarily rooted in the physical environment, particularly in the interactions between the soil-plant and atmosphere system, affecting grapevine physiology, grape composition and wine. Comprehension of the phenomena starts with viticulture zoning techniques, a crucial first step in mapping, describing and further studying terroirs. Viticulture zoning can be carried out with diverse empiricism and expertise and achieving different level of details in describing complex biophysical processes. Spatial and temporal scales can vary across studies, and not all of them have been able to capture the multidisciplinary nature of the terroir.The scientific understanding of the mechanisms ruling vineyard variability and grape composition is one of the most critical scientific focuses of terroir research. This knowledge can contribute to the analysis of climate change impacts on terroir resilience, the identification of new suitable land for viticulture, and the precise management of vineyards to reach a specific oenological goal.This article gives an overview of the latest approaches to terroir studies and of new zoning technology, with particular attention to their importance in supporting terroir resilience to climate change

Spatial variation in biodiversity, soil degradation and productivity in agricultural landscapes in the highlands of Tigray, northern Ethiopia

There is a growing concern about food security and sustainability of agricultural production in developing countries. However, there are limited attempts to quantify agro-biodiversity losses and relate these losses to soil degradation and crop productivity, particularly in Tigray, Ethiopia. In this study, spatial variation in agro-biodiversity and soil degradation was assessed in 2000 and 2005 at 151 farms in relation to farm, productivity, wealth, social, developmental and topographic characteristics in Tigray, northern Ethiopia. A significant decrease in agro-biodiversity was documented between 2000 and 2005, mainly associated with inorganic fertilizer use, number of credit sources and proximity to towns and major roads. Agro-biodiversity was higher at farms with higher soil fertility (available P and total N) and higher productivity (total caloric crop yield). Low soil organic matter, few crop selection criteria and steep slopes contributed to soil erosion. Sparsely and intensively cultivated land use types, as determined from satellite images, were associated with high and low agro-biodiversity classes, respectively, as determined during on-farm surveys in 2005. This study gives insight into the recent changes in and current status of agro-biodiversity and soil degradation at different spatial scales, which can help to improve food security through the maintenance of agro-biodiversity resource

Integrative framework and methods for coastal area management: proceedings

Coastal zone management, ASEAN,