scholarly journals The implications of a changing climate on agricultural land classification in England and Wales

2012 ◽  
Vol 152 (1) ◽  
pp. 23-37 ◽  
Author(s):  
C. A. KEAY ◽  
R. J. A. JONES ◽  
J. A. HANNAM ◽  
I. A. BARRIE
Keyword(s):  
Agricultural Land ◽  
Formal Method ◽  
Climate Variables ◽  
Climate Data ◽  
Land Classification ◽  
England And Wales ◽  
Chemical Soil ◽  
Soil Site ◽  
Land Grading

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.

Spatially-explicit modelling of grassland classes – an improved method of integrating a climate-based classification model with interpolated climate surfaces

2014 ◽  
Vol 36 (2) ◽  
pp. 175 ◽  
Author(s):  
Xiaoni Liu ◽  
Hongxia Wang ◽  
Jing Guo ◽  
Jingqiong Wei ◽  
Zhengchao Ren ◽  
...  
Keyword(s):  
Environmental Management ◽  
Classification Model ◽  
Spatially Explicit ◽  
Forest Steppe ◽  
Climate Variables ◽  
Climate Data ◽  
Humid Forest ◽  
Cool Temperate ◽  
Total Study Area

Spatially-explicit modelling of grassland classes is important to site-specific planning for improving grassland and environmental management over large areas. In this study, a climate-based grassland classification model, the Comprehensive and Sequential Classification System (CSCS) was integrated with spatially interpolated climate data to classify grassland in Gansu province, China. The study area is characterised by complex topographic features imposed by plateaus, high mountains, basins and deserts. To improve the quality of the interpolated climate data and the quality of the spatial classification over this complex topography, three linear regression methods, namely an analytic method based on multiple regression and residues (AMMRR), a modification of the AMMRR method through adding the effect of slope and aspect to the interpolation analysis (M-AMMRR) and a method which replaces the inverse distance-weighted approach for residue interpolation in M-AMMRR with an ordinary kriging approach (I-AMMRR), for interpolating climate variables were evaluated. The interpolation outcomes from the best interpolation method were then used in the CSCS model to classify the grassland in the study area. Climate variables interpolated included the annual cumulative temperature and annual total precipitation. The results indicated that the AMMRR and M-AMMRR methods generated acceptable climate surfaces but the best model fit and cross validation result were achieved by the I-AMMRR method. Twenty-six grassland classes were classified for the study area. The four grassland vegetation classes that covered more than half of the total study area were ‘cool temperate-arid temperate zonal semi-desert’, ‘cool temperate-humid forest steppe and deciduous broad-leaved forest’, ‘temperate-extra-arid temperate zonal desert’, and ‘frigid per-humid rain tundra and alpine meadow’. The vegetation classification map generated in this study provides spatial information on the locations and extents of the different grassland classes. This information can be used to facilitate government agencies’ decision-making in land-use planning and environmental management, and for vegetation and biodiversity conservation. The information can also be used to assist land managers in the estimation of safe carrying capacities, which will help to prevent overgrazing and land degradation.

scholarly journals Agriculture Land Classification Based on Climate Data Using Big Data Analysis

2019 ◽  
Vol 8 (S3) ◽  
pp. 94-99
Author(s):  
M. Sirish Kumar ◽  
S. Jyothi ◽  
B. Kavitha
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Agricultural Land ◽  
Big Data Analysis ◽  
Limiting Factors ◽  
Physical Factors ◽  
Land Classification ◽  
England And Wales ◽  
Agriculture Land ◽  
Agricultural Use

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.

scholarly journals Impact of Agricultural Practices on Water Quality of Old Woman Creek Watershed, Ohio

Agriculture ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 426
Author(s):  
Israel A. Olaoye ◽  
Remegio B. Confesor ◽  
Joseph D. Ortiz
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Organic Nitrogen ◽  
Agricultural Land ◽  
Hydrological Model ◽  
Organic Phosphorus ◽  
Agricultural Practices ◽  
Climate Data ◽  
Old Woman Creek

The effect of agricultural practices on water quality of Old Woman Creek (OWC) watershed was evaluated in a hydrological model using the Parameter-elevation Regressions on Independent Slopes Model (PRISM) climate data and 20 different global circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). A hydrological model was set up in the Soil and Water Assessment Tool (SWAT), while calibration was done using a Multi-Objective Evolutionary Algorithm and Pareto Optimization with PRISM climate data. Validation was done using the measured data from the USGS gage station at Berlin Road in the OWC watershed and water quality data were obtained from the water quality lab, Heidelberg University. Land use scenario simulations were conducted by varying percentages of agricultural land from 20% to 40%, 53.5%, 65%, and 80% while adjusting the forest area. A total of 105 simulations was run for the period 2015–2017: one with PRISM data and 20 with CMIP5 model data for each of the five land use classes scenarios. Ten variables were analyzed, including flow, sediment, organic nitrogen, organic phosphorus, mineral phosphorus, chlorophyll a, CBOD, dissolved oxygen, total nitrogen, and total phosphorus. For all the variables of interest, the average of the 20 CMIP5 simulation results show good correlation with the PRISM results with an underestimation relative to the PRISM result. The underestimation was insignificant in organic nitrogen, organic phosphorus, total nitrogen, chlorophyll a, CBOD, and total phosphorus, but was significant in CMIP5 flow, sediment, mineral phosphorus, and dissolved oxygen. A weak negative correlation was observed between agricultural land percentages and flow, and between agricultural land percentages and sediment, while a strong positive correlation was observed between agricultural land use and the water quality variables. A large increase in farmland will produce a small decrease in flow and sediment transport with a large increase in nutrient transport, which would degrade the water quality of the OWC estuary with economic implications.

PEMANFAATAN LIMBAH JAGUNG MENJADI PUPUK ORGANIK UNTUK PENYUBURAN LAHAN PERTANIAN

2018 ◽  
Vol 6 (1) ◽  
Author(s):  
Daru Mulyono
Keyword(s):  
Agricultural Land ◽  
Organic Fertilizer ◽  
Economic Value ◽  
Soil Microorganism ◽  
Appropriate Technology ◽  
Inorganic Fertilizer ◽  
Plant Materials ◽  
Chemical Soil ◽  
Stem Leaf

The use of maize waste plant materials (stem, leaf, and husk cover) have high economic value to be processed become organic fertilizer for agricultural land fertilizer. Maize have several and quite high contents of macro and micro nutrients. This activity was hoped that the farmers can overcome the increasing price of inorganic fertilizer recently and furthermore farmers can reap higher income. Beside higher income the use of organic fertilizer can improve the nature and behaviourof land through improving of soil chemical, soil physical, and soil microorganism. Therefore, the appropriate technology for processing of maize become organic fertilizer is very important to be diffused or socialized to farmers.Keywords: fertilizer, maize waste

PKM Kelompok Tani Tan Selo 1 dan Tan Selo 2 Desa Sukomaju Kecamatan Srono Kabupaten Banyuwangi Jawa Timur dalam Meningkatkan Nilai Ekonomis Pisang dan Strategi Pemasarannya

2018 ◽  
Vol 2 (2) ◽  
pp. 73
Author(s):  
Fitri Nurmasari ◽  
Raup Padillah
Keyword(s):  
Community Service ◽  
Online Shopping ◽  
Agricultural Land ◽  
Economic Value ◽  
Marketing Strategies ◽  
Service Programs ◽  
Community Service Programs ◽  
Farmer Groups ◽  
The Village

Banyuwangi Regency is one of the agricultural centers in East Java province and Indonesia. Mostly,Banyuwangi people work as farmers due to the fertil soil and wide amount of agricultural land in Banyuwangi . Thelarge number of people who work as farmers initiating the formation of farmer groups. One of the farmer groups in theSrono sub-district of Banyuwangi is the "Tan Selo 1" farmers group located in the village of Sukomaju and the "TanSelo 2" farmers group in Sukonatar village. The normal average price of one banana bunch in Banyuwangi is between50-60 thousand depending on the type and quality of bananas. Problems arise when the quantity of bananas in the marketarose, the price of 1 bunch of bananas decreases dramatically. The price of 1 bunch which is usually set at 50-60thousand drops drastically to only 20-30 thousand. This is certainly a problem for farmers in the Tan Selo group. The lackof knowledge of Tan Selo farmers about alternative variants of processed banana based products and the lack ofknowledge of the marketing strategies make it hard for the Tan Selo farmers to increase the economic value of bananaswhich have been used as an alternative income for farmers. Therefore, the solutions offered to overcome the problems offarmers include: equipping and improving farmers' knowledge about the variety of processed banana-based foods andtheir marketing strategies, conducting training to make variations on banana-based foods, conducting training oneffective marketing strategies. Overall, a series of community service programs were carried out perfectly as it expected.The percentage of participants' understanding in choosing high quality bananas is 85%, the percentage of participants’ability in processing banana-based foods is 86%, and percentage of participants who successfully sell processed foodproducts by utilizing online shopping sites is 70%

scholarly journals Chemical and Phytocoenological Characteristics of Two Different Slovak Peatlands

Plants ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 1290
Author(s):  
Danica Fazekašová ◽  
Gabriela Barančíková ◽  
Juraj Fazekaš ◽  
Lenka Štofejová ◽  
Ján Halas ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Profile ◽  
Agricultural Land ◽  
Nitrogen Supply ◽  
Point Of View ◽  
Statistical Testing ◽  
Plant Residues ◽  
Soil Reaction ◽  
Chemical Parameters

This paper presents the results of pedological and phytocoenological research focused on the detailed research of chemical parameters (pH, organic carbon, and nutrients), risk elements (As-metalloid, Cd, Co, Cr, Cu, Ni, Pb, and Zn), and species composition of the vegetation of two different peatlands on the territory of Slovakia—Belianske Lúky (a fen) and Rudné (a bog). Sampling points were selected to characterize the profile of the organosol within the peatland, the soil profile between the peatland and the agricultural land, and the soil profile of the outlying agricultural land, which is used as permanent grassland. Based on phytocoenological records, a semi-quantitative analysis of taxa in accordance with the Braun–Blanquet scale was performed. The study revealed that the thickness of the peat horizon of the fen in comparison with the bog is very low. In terms of the quality of organic matter, the monitored peatlands are dominated by fresh plant residues such as cellulose and lignin. Differences between individual types of peatlands were also found in the soil reaction and the supply of nitrogen to the organic matter of peat. The values of the soil exchange reaction were neutral on the fen, as well as slightly alkaline but extremely low on the bog. A significantly higher nitrogen supply was found in the organic matter of the fen in contrast to the bog. At the same time, extremely low content of accessible P and an above-limit content of As in the surface horizons were also found on the fen. From the phytocoenological point of view, 22 plant species were identified on the fen, while only five species were identified on the bog, which also affected the higher diversity (H’) and equitability (e). The results of the statistical testing confirmed the diversity of the studied peatlands and the different impact of environmental variables on plant diversity.

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