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

Abstract

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.

Analysis of Comprehensive Land Consolidation in Cultivated Land Reserve Resource Areas Based on Two-Level Geographical Unit Division

“…In this paper, RS image segmentation is used to obtain basic units, with which the destruction of natural geographic boundaries is avoided to a large extent. In the method of unit division, the multi-indicator superposition division [39][40][41] and synergistic relationship division of different indicators are more common [42]. The units divided by these kinds of methods are mostly in a discrete and broken state, and the work of CLC is difficult with these kinds of units.…”

The Spatiotemporal Evolution of the Growing Degree Days Agroclimatic Index for Viticulture over the Northern Mediterranean Basin

“…Additionally, a wide range of agroclimatic indices have been developed to establish a connection between atmospheric and agricultural environments on a climatic timescale [37][38][39][40]. These indices often quantify the thermal environment during crop growth, making them vital for applications in various agricultural aspects such as the evaluation of agrometeorological conditions and the related trends for staple crops [41][42][43][44], the estimation of the agrometeorological requirements for a plant phenology's principal stages [45], the assessment and projection of agroclimatic suitability with respect to the evolution of viticultural zoning [46][47][48][49], the prediction of abiotic and biotic hazards in fruit production [50], yield-production predictions [51,52], and also for policy and decision making and the implementation of adaptation strategies [53][54][55].…”

The Spatiotemporal Evolution of the Growing Degree Days (GDD) Agroclimatic Index for the Viticulture over the Northern Mediterranean Basin

“…Additionally, a wide range of agroclimatic indices have been developed to establish a connection between the atmospheric and agricultural environments on a climatic timescale [37][38][39][40]. These indices often quantify the thermal environment during crop growth making them vital for applications on various agricultural aspects such as the evaluation of agrometeorological conditions and related trends for staple crops [41][42][43][44], the estimation of the agrometeorological requirements on the plant phenology's principal stages [45], the assessment and projections on agroclimatic suitability as on the evolution of viticultural zoning [46][47][48][49], the prediction of abiotic and biotic hazards in fruit production [50], for yield-production prediction [51,52], but even for policy and decision making and the implementation of adaptation strategies [53][54][55].…”