Agricultural land classification (ALC) in England and Wales

2019 ◽  
Vol 8 (S3) ◽  
pp. 94-99
Author(s):  
M. Sirish Kumar ◽  
S. Jyothi ◽  
B. Kavitha

The Agricultural Land Classification (ALC) provides a frame work for classifying land according to the extent at which it’s physical or chemical characteristics impose long-term limitations on agricultural use. The major physical factors that influence agricultural criteria for grading are based on their physical margins of land for agricultural use, such as climate (temperature, rainfall, aspect, exposure and frost risk), site (gradient, micro-relief and flood risk) and soil (texture, structure, depth and stoniness and chemical properties which cannot be corrected) and exchanges these factors as soil wetness, draughtiness and erosion. These factors together interact with the basis for classifying land into one of five grades, the grade or sub-grade of land being determined by the most limiting factors that can be classified into grades from 1 (excellent) to 5 (very poor). These grades are classified by using temperature and average rain fall. In this we classified Agriculture Land Classification (ALC) by using Big Data Analysis based on climatic conditions of England and Wales data.Here we analyzed England and Wales data because it has the accurate climatic grades data. These grades data is huge so we analyses the data in Big DATA analysis.


2012 ◽  
Vol 152 (1) ◽  
pp. 23-37 ◽  
Author(s):  
C. A. KEAY ◽  
R. J. A. JONES ◽  
J. A. HANNAM ◽  
I. A. BARRIE

SUMMARYThe agricultural land classification (ALC) of England and Wales is a formal method of assessing the quality of agricultural land and guiding future land use. It assesses several soil, site and climate criteria and classifies land according to whichever is the most limiting. A common approach is required for calculating the necessary agroclimatic parameters over time in order to determine the effects of changes in the climate on land grading. In the present paper, climatic parameters required by the ALC classification have been re-calculated from a range of primary climate data, available from the Meteorological Office's UKCP09 historical dataset, provided as 5 km rasters for every month from 1914 to 2000. Thirty-year averages of the various agroclimatic properties were created for 1921–50, 1931–60, 1941–70, 1951–80, 1961–90 and 1971–2000. Soil records from the National Soil Inventory on a 5 km grid across England and Wales were used to determine the required soil and site parameters for determining ALC grade. Over the 80-year period it was shown that the overall climate was coolest during 1951–80. However, the area of land estimated in retrospect as ‘best and most versatile (BMV) land’ (Grades 1, 2 and 3a) probably peaked in the 1951–80 period as the cooler climate resulted in fewer droughty soils, more than offsetting the land which was downgraded by the climate being too cold. Overall there has been little change in the proportions of ALC grades among the six periods once all 10 factors (climate, gradient, flooding, texture, depth, stoniness, chemical, soil wetness, droughtiness and erosion) are taken into account. This is because it is rare for changes in climate variables all to point in the same direction in terms of ALC. Thus, a reduction in rainfall could result in higher grades in wetter areas but lead to lower classification in drier areas.


2019 ◽  
Vol 10 ◽  
pp. 60-68 ◽  
Author(s):  
Jūrate Sužiedelyte Visockiene ◽  
Egle Tumeliene

According to the official statistics the areas of abandoned agricultural land in Lithuania are gradually decreasing, but very slightly. The aim of this study is to research spatial determination and abandoned land classification in the territory of Vilnius District Municipality. Vilnius District Municipality was chosen for the research because it, although located near the capital of the country and has a high population density, it is still the district having the largest percent of abandoned land plots. A fast, cost-effective and sufficiently accurate method for determination of abandoned land plots would allow to constantly monitor, to fix changes and foresee the abandoned land plots reduction possibilities. In the study there was used the multispectral RGB and NIR color Sentinel-2 satellite images, the layer of the administrative boundary of Vilnius County and layer of abandoned agriculture land, which is available in Lithuanian Spatial Information Portal (www.geoportal.lt). The data was processed by Geographic Information System (GIS) techniques using classical classification Region Growing Algorithm. The research shows that NIR image classification result is more reliable than the result from RGB images.


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