Sustainable Agricultural Planning Research Articles

Irrigation strongly influences near‐surface conditions and induces breeze circulation: Observational and model‐based evidence

AbstractIrrigation is becoming increasingly common in agriculture and is essential to meet the growing demand for food. Studies of the impact of irrigated areas on local meteorology reveal a strong influence on near‐surface conditions, although the extent of this influence varies considerably between locations. In addition, though theoretical evidence suggests that irrigation can create breeze‐like atmospheric boundary‐layer circulations, observational evidence is still lacking. This study investigates the effects of irrigation on the surface and atmospheric boundary layer in the Ebro basin, an intensively irrigated area with a semi‐arid climate in northeastern Spain. Observational data from the international field campaign Land Surface Interactions with the Atmosphere over the Iberian Semi‐arid Environment are analysed together with coupled surface–atmosphere model output to better understand and quantify the impact of irrigation on the lower atmosphere. A simple parametrization of irrigation is shown to improve the accuracy of the model. Results demonstrate that irrigation increases the average latent heat flux by over 200 Wm, reduces air temperature by 4.7°C, and increases specific humidity by 50% at 2 m during the day over the irrigated region of the domain. Moreover, irrigation limits convection and strongly stabilizes the atmospheric boundary layer. Notably, the study provides evidence for an irrigation‐induced breeze from the irrigated area to the semi‐arid area. These findings highlight the importance of considering irrigation in numerical models for weather forecasting, climate modelling and sustainable agricultural planning.

A novel optimization approach for rural development based on sustainable agriculture planning, considering the energy and water consumption nexus

In today's world, there are many changes and transformations in different sectors of the economy, and today's world economy has become more dependent on each other. The economy of many developing countries is highly dependent on agriculture. In addition, in developed countries, the agricultural sector is of special importance, so they are currently turning to the production of more organic and healthy products. One of the reasons that caused the agricultural sector to receive more attention in the economy is the existential importance of this sector in providing the raw materials of a part of the industry and the food industry needed in that country. Accordingly, in this research, the optimal cultivation pattern is determined using a linear multi-objective mathematical model, including economic, social, and environmental objectives. Moreover, the uncertainty in the input parameters of the proposed model is considered, and a robust optimization approach is applied to deal with this uncertainty. After collecting the research data for the crop year 2021-2022, the proposed robust counterpart formulation is optimized using GAMS software. The numerical results show that increasing the level of conservatism in the robust model cause to decrease in the profit of agriculture as well as increasing the total consumed water. Moreover, the results of the current research can help the decision-makers of the agricultural sector in achieving the goal of obtaining the highest profit.

Food production potential and environmental sustainability of different integrated farming system models in northwest India

Accelerated energy use, negative environmental outcomes, and poor economic returns questioned the sustainability of contemporary agricultural production systems globally. The task is much more daunting in the northwestern part of India where the over exploitation of natural resources is a major concern for sustainable agricultural planning. An integrated farming system (IFS) encompasses various enterprises such as crops, dairy, poultry, and fisheries can offer a myriad of benefits in terms of enhanced farm productivity, profitability, and environmental sustainability. Hence, the study hypothesized that the complementary interaction between the different enterprises would improve food production and reduce negative environmental outcomes. Therefore, production potential and environmental sustainability in terms of energy efficiency, greenhouse gas emissions, and eco-efficiency of nine IFS models, namely, crop enterprise (M2); crop + dairy (M3); crop + dairy + fishery (M4); crop + dairy + fishery + poultry (M5); crop + dairy + fishery + poultry + duckery (M6); crop + dairy + fishery + poultry + duckery + apiary (M7); crop + dairy + fishery + poultry + duckery + apiary + boundary plantation (M8); crop + dairy + fishery + poultry + duckery + apiary + boundary plantation + biogas unit (M9); crop + dairy + fishery + poultry + duckery + apiary + boundary plantation + biogas unit + vermicompost (M10), were compared with the rice–wheat system (M1; the existing system). All the IFS models were tested between 2018 and 2021. The results revealed that the highest food production (61.5 Mg ha−1) was recorded under M10 followed by M9 (59.9 Mg ha−1). Concerning environmental sustainability, the combination of crop + dairy + fishery + poultry + duckery + apiary + boundary plantation + biogas unit + vermicompost (M10) recorded considerably higher energy output (517.6 × 103 MJ ha−1), net energy gain (488.5 × 103 MJ ha−1), energy ratio (17.8), and energy profitability (16.8 MJ MJ−1) followed by M9. Furthermore, the M10 had the lowest greenhouse gas (GHG) intensity (0.164 kg CO2 eq per kg food production). However, M9 had the highest eco-efficiency index (44.1 INR per kg GHG emission) followed by M10. Hence, an appropriate combination of diversified and complementary enterprises in a form of IFS model is a productive and environmentally robust approach for sustainable food production in the northwestern part of India.

Statistical Analysis of Rainfall Distribution in Biratnagar, Nepal: A case study

Rainfall is a key component in the construction of many engineering projects, including canals, bridges, culverts, and road drainage systems. The primary goal of this study is to ascertain the annual, monthly and seasonal fluctuations in rainfall in Biratnagar, Nepal over a period of 10 years (2010-2019). The mean, standard deviation, coefficient of variation, skewness, kurtosis, Precipitation Concentration Index (PCI), normal rainfall along with Mann-Kendall test were calculated to check the rainfall variations. The annual data showed the biggest varieties in rainfall, with an average annual rainfall of 1542.43 mm during a ten years period. Furthermore, an erratic monthly rainfall pattern was found by the non-parametric Mann-Kendall test. However, the test value for each month was non-significant at the 5% level of significance. We also found that the rainfall is least in the winter and most during monsoon season. Based on these calculations, It is seen that the pattern of rainfall is inconsistent or unpredictable. However, present study will aid in the understanding of rainfall variations and trends, assisting in the management of water resources, ecosystems, irrigation, sustainable agricultural planning, engineering structural design, and other areas.

Spatiotemporal Rainfall Variability and Trend Analysis of Shimsha River Basin, India.

Karnataka state has the second highest rainfed agricultural land in India, where agricultural output relies heavily on rainfall. The Shimsha basin, a sub-basin of Cauvery in the state, comes under a semi-arid region and predominantly consists of rainfed agricultural land. Rainfall patterns have changed dramatically with time resulting in frequent floods and droughts. Understanding the spatiotemporal distribution of rainfall and its change patterns in the area would benefit sustainable agriculture planning and water resources management practices. The current study aims to determine the variability and trend in rainfall. The daily rainfall data of the Shimsha basin from 1989 to 2018 is collected, and the annual, seasonal, and monthly rainfall totals and the number of rainy days are derived. All the time series are subjected to statistical methods to examine rainfall variability and trend. The mean, standard deviation, coefficient of variation (CV), and Standardized Anomaly Index are used for the preliminary and variability analysis, while the coefficient of skewness and kurtosis are used to understand the rainfall distribution characteristics. The homogenous and serially independent series are identified by homogeneity and serial correlation tests. The trend in the homogenous and serially independent series is identified by Mann-Kendall and Spearman's rank correlation tests, while the magnitude of the trend is quantified using the Sen's slope technique, and the trend change point is evaluated using the sequential Mann-Kendall test. Based on the study, the average rainfall in the study area is 801.86mm, with CV ranging from 43.3 to 22.27%. The southwest monsoon (SWM) season brings the greatest rain to the basin, followed by the post-monsoon (PM), summer, and winter seasons. In the annual time frame, except one station, all other stations have shown significant or insignificant increasing trends. The seasonal rainfall has shown insignificant rising trends during the summer and winter seasons while insignificant increasing and decreasing trends during the PM season. The SWM season has indicated significant increasing trends, insignificant increasing and decreasing trends. Overall, the study area has noticed an increased annual and seasonal rainfall except for the post-monsoon season, during which the rainfall showed a considerable decline. The findings of the study are helpful in water resource management, agricultural planning, and socioeconomic development in the study area.

Sustainable agricultural lands management by analyzing and predicting urban growth—a case study of Mitidja Plain, Algeria

The suitability of agricultural lands is basic data for both sustainable agricultural planning and optimal orientation of future urbanization projects. Indeed, unplanned urban expansion leads to many social and ecological problems. The prediction of urban expansion will strongly help the decision makers and local authorities to create a suitable future urban planning while preserving the regional ecosystem and especially the agricultural lands. Using multitemporal satellite images acquired in 1997, 2001, and 2013 over the Mitidja plain in Algeria, the aim of this study is the analysis of the land use and land cover change (LULCC) spatio-temporal distributions from 1997 to 2013 to model and then predict the future LULCC in 2025. The cellular automata Markov chain model (CA-Markov) that we developed is used for LULCC prediction with a focus on urban sprawl and agricultural lands degradation over the Mitidja plain. Once calibrated and validated, the CA-Markov model generates land suitability maps that indicate the best locations for urban expansion while safekeeping the agricultural lands. The results are interesting and present valuable tools for urban planners and policymakers to ensure the sustainability management of agricultural areas over the study region.

Understanding Changes in the Hydrometeorological Conditions towards Climate-Resilient Agricultural Interventions in Ethiopia

Climate resilient agriculture (CRA) is very important to achieve long-term improvement in productivity and farm incomes under climate uncertainty. The present study focuses on investigating the plausible changes in the hydrometeorological conditions using big-data analysis techniques in the study of Ethiopia. The original contribution of this work envisages the importance of the CRA system in water-scarce areas for sustainable agriculture planning and management under changing climatic conditions. In the present research, a TerraClimate model was the basis for weather (precipitation and temperature) and hydrological data (runoff, actual evapotranspiration, potential evapotranspiration, vapor pressure deficit and climate water deficit); these data were used to determine the spatial distribution of the standardized anomaly index (SAI) and the slope of the linear regression for long-term (1958–2020) trend analysis. Future climate trend analysis (2021–2100) has been performed through the CMIP6 (EC-Earth3) shared socio-economic pathway (SSP 2) 4.5 dataset. Gravity Recovery and Climate Experiment (GRACE) with CSR and JPL data were utilized for the generation of water storage heat maps from 2002 to 2021. The results show that the average annual rainfall data for over 62 years was found to be 778.42 mm and the standard deviation is 81.53 mm. The results also show that the western part of the study area has the highest temperature trend, which diminishes as one moves eastward; the minimum temperature trend has been found in the western part of the study area. It was found that the equivalent water thickness (EWT) range of both CSR and JPL products was −15 to 40 cm. These results can help local climate-resilient development planning and enhance coordination with other institutions to access and manage climate finance.

Sustainable Food Supply by Peri-Urban Diversified Farms of the Agri-Food Region of Central Córdoba, Argentina

Peri-urban vegetable cropping areas, such as horticultural farms, provide several ecosystem services, such as the provision of fresh food. However, food supply must be estimated on the basis of the current and potential demand of future populations, taking into account the landscape carrying capacity towards sustainable agricultural planning. From this perspective, the study aimed at estimating the resilience of the “Agri-food Region of Central Córdoba” (ARCC) and its role in supporting the provisioning of ecosystem services, such as proximal services, provided by the diversified agricultural landscape in the peri-urban area of Córdoba (Argentina). A direct field survey has been carried out to collect data on the main species and types of crops, the annual productivity, and the area covered by each species and type of horticultural crops. The data have been statistically elaborated to test the spatial and temporal variability of productivity as well as the spatial autocorrelation. In relation to crop diversification, a total of 30 vegetable species have been recorded in the diversified farms under study, with 15 species identified as the most frequent crops as on the basis of the area dedicated to each vegetable species sampled in the farms (in %). The productivity of 30 species has been integrated into a single value of “vegetable crop productivity mean” (kg/m2), proposed and measured in this study, which has been 3.46 kg/m2. It can be a useful monitoring indicator in diversified production contexts. The estimated food supply (ton/year) of vegetable crops for the 170 farmlands has been 72,881 ton/year. An accurate measurement of the biomass harvested on a given surface area can be useful to assign productivity data to the pixel of each land use/cover class, providing accurate input data for remotely sensed-based models supporting decision-making on food provision in peri-urban systems. In this sense, the paper proposes a methodological framework that can be useful worldwide when up-to-date official productivity data are not available, but they are a necessary basis for planning, decision-making, and the implementation of public policies. Thus, diversity in farming systems can combine high ecological and socio-economic benefits, in terms of ecosystem service provision and sustainable food production.

Sugarcane yield estimation through remote sensing time series and phenology metrics

Accurate agricultural yield prediction is a fundamental tool for sustainable agricultural planning and to ensure food security in regions critically affected by climate change and extreme weather events. Existing regression-based crop yield estimation approaches typically rely on a specific set of predictor variables, but have not been compared systematically. This paper demonstrates and compares the utilization and the combinatorial use of three different sets of object-based predictors for sugarcane yield estimation through the agricultural monitoring platform ag|knowledge which utilizes earth observation data of the Sentinel-2 satellites, captured between 2018 and 2019 for a study area of about 10,000 hectares in Ethopia. We compare several regression models using a range of different predictor variables, such as (i) multi-temporal variables (i.e., parcel-based vegetation index time series), (ii) time series descriptors (i.e., phenological metrics) and (iii) spatio-temporal variables. We achieve R² scores of up to 0.84 for the estimation of sugarcane yield and up to 0.82 for the estimation of sugar quantity through Random Forest regression, based on the combinatorial use of all predictor variables. Our experiments demonstrate that dimensionality-independent phenological metrics achieve good yield estimation results which could be a very useful variable set for model transfer and domain adaptation.

Evaluation of Soil Physico-Chemical and Biological Properties under Different Tea (Camellia sinesis) Gardens of Himachal Pradesh, India

Assessment of different soil attributes provides an insight into the soil-related constraints and potentials for sustainable agricultural planning. In the present investigation, 37 tea gardens were selected at random from tea growing areas of Himachal Pradesh during 2017-19 to evaluate the status of different soil attributes in surface (0-0.30 m) and sub-surface (0.30-0.60 m) soils. All the collected soil samples were analyzed for particle density, porosity, water stable aggregates, electrical conductivity, exchangeable Ca and Mg and microbial count as per standard procedures. The particle density, porosity and water stable aggregates in soils of tea gardens varied from 2.58 to 2.64 Mg m3, 39 to 57% and 26 to 58%. The electrical conductivity, exchangeable Ca and Mg were in the range of 85 to 210 dS m-1, 1.2 to 4.0 cmol (p+) kg-1 and 0.6 to 2.3 cmol (p+) kg-1 with a mean value of 134±35.8, 2.7±0.8 and 1.4±0.4, respectively. With regards to microbial count, the bacterial population ranged from 49 to 71 x 106 CFU, fungal population ranged from 36 to 69 x 104 CFU and actinomycetes population varied from 31 to 58 x 104 CFU.

“Illegal” Gold Mining Operations in Ghana: Implication for Climate-Smart Agriculture in Northwestern Ghana

Globally, climate-smart agriculture is highly recognized as an approach for sustainable agriculture and food systems. In Africa and other developing countries, climate-smart agriculture is observed to reposition and modify agricultural systems for improved food and nutritional security. Despite the relevance of the approach to sustainable agricultural planning, illegal gold mining in many parts of the society is placing constraints to its implementation and adoption through its contest with agricultural land for space and activities. Illegal gold mining is on the rise due to the lucrativeness of the non-regulated gold rush opportunities with hard consequences on sustainable agriculture and resilience food systems. As a result, this study seeks to investigate illegal gold mining and its environmental implication for climate-smart agriculture in Ghana. The study used a single case study using a mixed-methods approach to research. The study adopted purposive and systematic sampling techniques to select the study communities and respondents, respectively. Questionnaire and interviews were used to gather the primary data from respondents at the household level, as the unit of analysis. Descriptive statistics and thematic analysis reveal that known agricultural practices such as terracing, crop rotation, use of domestic waste/manure, and irrigation of crops were affected adversely by activities of illegal mining. The study recommends the need for conscious efforts from the Ministry of Lands and Natural Resources to sustain the ban on illegal mining with intensified monitoring and supervision while a systematic scheme involving relevant stakeholders is developed and implemented to ban illegal mining in Ghana completely. The Ministry of Food and Agriculture needs to develop an approach to support the adoption of climate-smart agricultural practices by smallholder farmers to meet the food demand of their households.

Land suitability analysis for paddy crop using GIS-based Fuzzy-AHP (F-AHP) method in Koch Bihar district, West Bengal

Land Suitability Analysis (LSA) is one of the most important methods for developing a precise land suitability map. The present study was aimed to identify suitable paddy crop-growing regions in the Koch Bihar district of West Bengal, India. For the study, 18 criteria were chosen, and their weightage was assigned based on expert opinion and the GIS-based Fuzzy Analytical Hierarchy process (F-AHP) method adopted. Results indicated the most important factors for developing LSA for paddy cultivation are rainfall (16.72%), soil texture (13.22%), slope (11.55%), elevation (09.63%), soil moisture (08.27%), temperature (07.11%), and pH (05.03%). It has been observed that highly suitable (14.54%), moderately suitable (46.07%), marginally suitable (24.20%), and not suitable (15.19%) areas for paddy cultivation. The Area Under Curve (AUC) value (0.869) confirms the reliability of the F-AHP approach in LSA. The findings of the study provide sustainable agricultural planning and delineate potential sites for paddy cultivation.

Concept Analysis and Implementation of Sustainable Agriculture based on Farmer Group Development and Agribusiness Patterns

This study aimed to analyze the concept and implementation of sustainable agriculture based on farmer group development and agribusiness patterns. This study uses a qualitative research type. The criteria that influence the success of sustainable agriculture include socio-cultural, economic, agricultural technology, institutions, and government policies. The resource persons in this study were farmers in the Nanggalo sub-district, amounting to 12 people. The types of data used in this study are primary data and secondary data collected through observation, literature study, and in-depth interviews. This study uses a decision-making method to determine the priority of various choices, namely the Analytical Hierarchy Process (AHP). The results of the research based on the combined opinion weighting factor matrix of all criteria indicate that the order of priority of the most important criteria according to respondents' assessment for sustainable agricultural planning in Nanggalo District, Padang city is Institutional criteria (25.05%), Social Culture (20.73%), Agricultural Technology (20.69%), Economics (18.21%), and Government Policy (15.31%).

Site and Regional Trend Analysis of Precipitation in Central Macedonia, Greece

The purpose of this paper is to investigate the trend of precipitation in Kilkis region (Greece) at the site and regional level in various time scales. At the site level, the precipitation trend was analyzed using three tests: 1) Mann-Kendall, 2) Sen’s T and 3) Spearman while the trend slope was estimated using the Sen’s estimator. At a regional level, nonparametric spatial tests such as Regional Average Mann-Kendall (RAMK) and BECD’s (Bootstrap Empirical Cumulative Distributions) were elaborated with and without the effect of cross correlation. The trend of precipitation was noticed generally downward at annual time scale and statistically significant at 5% level of significance only in only one station. The results of the analysis of trends at the regional level showed in total the influence of cross correlation in the time series since the number of trends detected is reduced when cross correlation is preserved. Precipitation data from 12 stations were used. The study results benefit water resource management, drought mitigation, socio-economic development, and sustainable agricultural planning in the region.

Potential expansion of Hass avocado cultivation under climate change scenarios threatens Mexican mountain ecosystems

Analysis of potential areas for crop establishment is necessary for sustainable agricultural planning, conservation of natural ecosystems, and achievement of food security’s current global objective. This study aims to model the current potential distribution of the optimal areas for Hass avocado crop in Mexico, along with the likely impact of climate change on the crops and the surrounding mountain ecosystems in the state of Michoacán, the principal producer. The maximum entropy approach was used to model the current and future potential distribution of the avocado using points of presence of avocado cultivation in Mexico and climatic variables under 10 global climate models (GCMs) and three representative concentration pathways (RCPs) from the IPCC. We estimated a current potential area for the establishment of Hass avocado crops in Mexico of 54 597 km2, associated with the temperate forests of the Transversal Volcanic Belt and the Sierra Madre del Sur. The loss of area for the establishment of avocado crops in Mexico was 31.1% under the most optimistic scenario, RCP 2.6, whereas 43.0% would be lost under the most pessimistic scenario, RCP 8.5. Currently, the potential distribution for the establishment of Hass avocado crops in the state of Michoacán is 22 561 km2. Given optimistic scenarios RCP 2.6 and RCP 8.5 of climate change by 2050, temperate forests mountain would represent 59 and 72.3%, respectively, of the potential area for establishing avocado in the state. Commercial pressure and climate change can lead to forest mountain ecosystem deforestation to establish new avocado crops and exacerbate water resource scarcity problems, jeopardising the entire production system’s sustainability. Territorial planning should prioritise conservation policies to avoid land-use change and establish strategies to maintain avocado crop sustainability in the long-term under climate change scenarios.

Deciphering Soil Spatial Variability through Geostatistics and Interpolation Techniques

Detailed knowledge of soil properties is fundamentally important for optimizing agriculture practices and management. Meanwhile, the spatial distribution of soil physicochemical properties is considered a fundamental input of any sustainable agricultural planning. In the present study, ordinary kriging, regression kriging and IDW were chosen for deciphering soil spatial variability and mapping soil properties in a reclaimed area of the Behera Governorate of Egypt where soil arose from two different types, one sandstone and the other limestone. Geostatistics were used to show the interrelationships and conditions of soil properties (available phosphorus, potassium and nitrogen, EC, pH, Sp, ESP, CEC, OC, SAR, and CaCO3). The results of mapping spatial soil variability by Geostatistics could be used for precision agriculture applications. Based on the soil test results, nutrient management recommendations should be applied regarding variable rates of fertilizers. The performance of the maps was evaluated using Mean square error (MSE). Inverse distance weight (IDW) showed higher efficiency than Kriging as a prediction method for mapping the studied soil properties in the study area. The results of the present study suggest that the application of the selected fit model worldwide in any relevant study of soil properties of different geological sources is feasible.

Long-Term Hydro–Climatic Trends in the Mountainous Kofarnihon River Basin in Central Asia

Hydro–climatic variables play an essential role in assessing the long-term changes in streamflow in the snow-fed and glacier-fed rivers that are extremely vulnerable to climatic variations in the alpine mountainous regions. The trend and magnitudinal changes of hydro–climatic variables, such as temperature, precipitation, and streamflow, were determined by applying the non-parametric Mann–Kendall, modified Mann–Kendall, and Sen’s slope tests in the Kofarnihon River Basin in Central Asia. We also used Pettitt’s test to analyze the changes during the 1951–2012 and 1979–2012 time periods. This study revealed that the variations of climate variables have their significant spatial patterns and are strongly regulated by the altitude. From mountainous regions down to plain regions, the decadal temperature trends varied from −0.18 to 0.36 °C/decade and the variation of precipitation from −4.76 to −14.63 mm yr−1 per decade. Considering the temporal variation, the temperature trends decreased in winter and significantly increased in spring, and the precipitation trends significantly decreased in spring but significantly increased in winter in the high-altitude areas. As consequence, total streamflow in headwater regions shows the obvious increase and clear seasonal variations. The mean monthly streamflow decreased in fall and winter and significantly increased in the spring and summer seasons which can be attributed to the influence of global warming on the rapid melting of snow and ice. Although the abrupt change points in air temperature and precipitation occurred around the 1970s and 1990s in the low-altitude areas and 2000s in the high-altitude areas during the 1951–2012 and 1979–2012 periods, the general trends of hydro–climatic variables keep consistent. This study benefits water resource management, socio–economic development, and sustainable agricultural planning in Tajikistan and its downstream countries.

Long-Term Homogeneity, Trend, and Change-Point Analysis of Rainfall in the Arid District of Ananthapuramu, Andhra Pradesh State, India

The objective of this study is to evaluate the homogeneity, trend, and trend change points in the rainfall data. Daily rainfall data was collected for the arid district of Ananthapuramu, Andhra Pradesh state, India from 1981 to 2016 at the subdistrict level and aggregated to monthly, annual, seasonal rainfall totals, and the number of rainy days. After quality checks and homogeneity analysis, a total of 27 rain gauge locations were considered for trend analysis. A serial correlation test was applied to all the time series to identify serially independent series. NonParametric Mann–Kendall test and Spearman’s rank correlation tests were applied to serially independent series. The magnitude of the trend was calculated using Sen’s slope method. For the data influenced by serial correlation, various modified versions of Mann–Kendall tests (pre-whitening, trend-free pre-whitening, bias-corrected pre-whitening, and two variants of variance correction approaches) were applied. A significant increasing summer rainfall trend is observed in six out of 27 stations. Significant decreasing trends are observed at two stations during the southwest monsoon season and at two stations during the northeast monsoon season. To identify the trend change points in the time series, distribution−free cumulative sum test, and sequential Mann–Kendall tests were applied. Two open−source library packages were developed in R language namely, ”modifiedmk” and ”trendchange” to implement the statistical tests mentioned in this paper. The study results benefit water resource management, drought mitigation, socio−economic development, and sustainable agricultural planning in the region.

Assessment of Soil Degradation and Large Scale Soil Mapping Using GIS: A Case Study of Village Ramagarh from Purna Valley, Maharashtra

The importance of soil-physiographic relationship in soil survey and mapping provide a fair understanding of variability across the landscape needed for sustainable agricultural planning. Keeping this in view the swell- shrink soils of the Ramagarh village of Purna valley in Amravati district, Maharashtra in semi-arid region of central India were studied for their morphological, physical and chemical characteristics and soils were mapped at 1: 8000 scale in geographical information system (GIS) platform. The soils of Ramagarh village are very deep, dark grayish brown to very dark grayish brown in colour, clayey in texture and exhibits medium, moderate, sub angular blocky structure in the surface layers and the subsoil horizons have medium, weak to strong angular blocky structure. Soils are alkaline in reaction, calcareous in nature and low to medium organic carbon content. The pH, CaCO3 and exchangeable sodium percentage (ESP) increase with depth in all the soils. Because of high smectitic clay content and ESP down the profile, these soils have impeded drainage and show ponding of water in the rainy season. The soils of the uplands are classified as Sodic Haplusterts and low land soils belong to Typic Haplusterts category at sub group level. The study indicates that the soils are sodic chemically degraded in 18.2% area of the TGA of the village. The higher ESP was related to corresponding decrease in exchangeable calcium and increase in exchangeable magnesium.

Changes to Land Area Used for Grain Maize Production in Central Europe due to Predicted Climate Change

Regional biogeographical models are considered to be important tools for supporting decisions relating to sustainable agricultural planning for climate change. These models are useful for a better understanding of the impact of climate change on individual crop species due to their sensitivity to regional ecological conditions. This paper deals with the application of a regional biogeographical model in order to predict the impact of climate change on growing conditions for grain maize in Central Europe. The model is based on a detailed knowledge of the relationships between the climatic characteristics of vegetation zones in landscapes with ecological growing conditions suitable for grain maize in the region under study. The results gained from using the model indicate a substantial increase in the total area suitable for growing of grain maize in the study region. By 2070, this area is expected to be triple the size it is today. Special maps are used to visualize prediction scenarios in order to support decision-making in regional planning in the study region, where grain maize is an important agricultural crop. This biogeographical model can be used in other European regions, where basic data related to vegetation zones are available.