Delineation of site‐specific management zones using estimation of distribution algorithms

In this paper, we present a novel methodology to solve the problem of delineating homogeneous site-specific management zones (SSMZ) in agricultural fields. This problem consists of dividing the field into small regions for which a specific rate of inputs is required. The objec- tive is to minimize the number of management zones, which must be homogeneous according to a specific soil property: physical or chem- ical. Furthermore, as opposed to oval zones, SSMZ with rectangular shapes are preferable since they are more practical for agricultural technologies. The methodology we propose is based on evolutionary computation, specifically on a class of the estimation of distribution algorithms (EDAs). One of the strongest contributions of this study is the representation used to model the management zones, which gener- ates zones with orthogonal shapes, e.g., L or T shapes, and minimizes the number of zones required to delineate the field. The experimental results show that our method is efficient to solve real-field and ran- domly generated instances. The average improvement of our method consists in reducing the number of management zones in the agricul- tural fields concerning other operations research methods presented in the literature. The improvement depends on the size of the field and the level of homogeneity established for the resulting management zones.IT1244-19 TIN2016-78365-R PID2019-104966GB-I0

An integrated study of earth resources in the State of California based on ERTS-1 and supporting aircraft data, volume 1

There are no author-identified significant results in this report

Photomorphic analysis techniques: An interim spatial analysis using satellite remote sensor imagery and historical data

The use of machine scanning and/or computer-based techniques to provide greater objectivity in the photomorphic approach was investigated. Photomorphic analysis and its application in regional planning are discussed. Topics included: delineation of photomorphic regions; inadequacies of existing classification systems; tonal and textural characteristics and signature analysis techniques; pattern recognition and Fourier transform analysis; and optical experiments. A bibliography is included

Third ERTS Symposium: Abstracts

Abstracts are provided for the 112 papers presented at the Earth Resources Program Symposium held at Washington, D.C., 10-14 December, 1973

Demarcation of Ground Water Potential Zones using Remote Sensing and GIS Applications

Now-a-days, due to the high demand of water for the human needs, groundwater sources are drastically extracted and causing to least the source. The entire Yearly furnish is contributing from the utmost resource called Groundwater. Globally, groundwater is extracting primarily for the purpose of agricultural fields, domestic and for industrial water supply. Majority of the surface water is in the form of saline water which is not useful for the needs of human beings for their daily needs. Very less amount of fresh surface water is existing on the ground surface. To compensate the needs, it is essential to identify, extract and manage the groundwater which is available at different levels at different areas of the globe. Proper planning is required for the extraction of groundwater using updated technologies for using and maintaining of natural resources like water resources. The prime strive of the selected project area is to map out potential groundwater regions in the Pendlimarri Mandal of Kadapa District by using Geospatial Technology. The main impartial target of the work is to select appropriate methods and assessment criteria of the technology to identify the potential underground demarcations in geographic information system environment with help of ArcGIS software. To demarcate zones of groundwater potential, various key parameters called geology, lineament density, LU / LC, geomorphology, groundwater depths, slope and drainage pattern were prepared by utilizing remote sensing data and secondary data which can collect from concern departments. The thematic layers are to be finally integrated by using weighted overlay analysis of spatial analyst tools of data management tools of ArcMap software to delineate underground water prospects regions output layout of the project. Disparate groundwater prospects levels were categorized, from the range excellent to poor including very good, good and moderate in between. At last, decided that that the applications of geoinformatics are essential and effectively applied for the demarcation of potential zones of groundwater

A site-specific and dynamic modeling system for zoning and optimizing variable rate irrigation in cotton

Cotton irrigation has been rapidly expanding in west Tennessee during the past decade. Variable rate irrigation is expected to enhance water use efficiency and crop yield in this region due to the significant field-scale soil spatial heterogeneity. A detailed understanding of the soil available water content within the effective root zone is needed to optimally schedule irrigation. In addition, site-specific crop-yield mathematical relationships should be established to identify optimum irrigation management. This study aimed to design and evaluate a site-specific modeling system for zoning and optimizing variable rate irrigation in cotton. The specific objectives of this study were to investigate (i) the spatial variability of soil attributes at the field-scale, (ii) site-specific cotton lint yieldwater relationships across all soil types, and (iii) multiple zoning strategies for variable rate irrigation scenarios. The field (73 ha) was sampled and apparent soil electrical conductivity (ECa) was measured. Landsat 8 satellite data was acquired, processed, and transformed to compare indicators of vegetation and soil response to cotton lint yields, variable irrigation rates, and the spatial variability of soil attributes. Multiple modeling scenarios were developed and examined. Although experiments were performed during two wet years, supplemental irrigation enhanced cotton yield across all soil types in comparison with rain-fed conditions. However, length of cropping season and rainfall distribution remarkably affected cotton response to supplemental irrigation. Geostatistical analysis showed spatial variability in soil textural components and water content was significant and correlated to yield patterns. There was as high as four-fold difference between available water content between coarse-textured and fine-textured soils on the study site. A good agreement was observed (RMSE = 0.052 cm3 cm-3 [cubic centimeter per cubic centimeter] and r = 0.88) between predicted and observed water contents. ECa and space images were useful proximal data to investigate soil spatial variability. The site-specific water production functions performed well at predicting cotton lint yield with RMSE equal to 0.131 Mg ha-1 [megagram per hectare] and 0.194 Mg ha-1 in 2013 and 2014, respectively. The findings revealed that variable rate irrigation with pie shape zones could enhance cotton lint yield under supplemental irrigation in west Tennessee

The improvement of strategic crops production via a goal programming model with novel multi-interval weights

Nowadays, the need to increase agricultural production has becomes a challenging task for most of the countries. Generally, there are many resource factors which affect the deterioration of production level, such as low water level, desertification, soil salinity, low on capital, lack of equipment, impact of export and import of crops, lack of fertilizers, pesticide, and the ineffective role of agricultural extension services which are significant in this sector. The main objective of this research is to develop fuzzy goal programming (FGP) model to improve agricultural crop production, leading to increased agricultural benefits (more tons of produce per acre) based on the minimization of the main resources (water, fertilizer and pesticide) to determine the weight in the objectives function subject to different constraints (land area, irrigation, labour, fertilizer, pesticide, equipment and seed). FGP and GP were utilized to solve multi-objective decision making problems (MODM). From the results, this research has successfully presented a new alternative method which introduced multi-interval weights in solving a multi-objective FGP and GP model problem in a fuzzy manner, in the current uncertain decision making environment for the agricultural sector. The significance of this research lies in the fact that some of the farming zones have resource limitations while others adversely impact their environment due to misuse of resources. Finally, the model was used to determine the efficiency of each farming zone over the others in terms of resource utilization

Automated Field Boundary Detection Using Modern Machine Learning Techniques

The Agricultural Conservation Planning Framework (ACPF) is a framework for watershed analysis that is supported by a unique land management database. Implementing the ACPF Framework comprises several steps. One of the most important steps in this framework is manually editing the United States Department of Agriculture (USDA) Farm Service Agency (FSA) Common Land Unit (CLU) boundaries to match cropping patterns per USDA National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) and National Agricultural Imagery Program (NAIP) aerial imagery. This step uses lot of man-hours and is highly susceptible to human errors. The use of latest deep-learning techniques will help alleviate some of these issues. In this project, various Machine learning techniques have been implemented to assist in this particular step of ACPF and the correctness of those techniques have been analyzed in detail. The ACPF Database also facilitates data for the Daily Erosion Project (DEP), a daily estimator of sheet and rill erosion across the western US Corn Belt [1]. In this report, we will detail field boundary digitization for the ACPF which can then be applied to DEP as well. We calculate the accuracy of the machine learning models by comparing their output produced with the manually edited boundaries. The accuracy is quantized using Kappa Coefficient. The machine learning techniques used for this process include, Maximum likelihood Classification, Random Trees Classification, and Support Vector Machine Classification. Our last experiment is to create a Convolutional Neural Network (CNN) model to classify the crop type present in each field area. We use a small set of image chips of fields for training the CNN, and then apply it to the remaining images, and display the correctness of the Neural network model. Sample results from counties in Kansas and Nebraska are presented in this report

GIS applications for poverty targeted aquaculture development in the lower Mekong Basin.

In the lower Mekong Basin, marginal socio-economic conditions prevail amongst rural small scale farming households which heavily depend on highly seasonal, rain-fed farming systems for their livelihood. Persistent rural poverty is aggravated by frequently occurring droughts and floods. A yearly flood-drought cycle, while essential to their household economy based on rice and fisheries, renders rural poor livelihoods vulnerable to recurrent periods of food insecurity. This research demonstrates how a combination of publicly accessible Remote Sensing imagery and disaggregated poverty maps, within a comprehensive rural development framework, can provide an effective method to target pro-poor aquaculture development interventions at the local level. An agro-ecosystems analysis is performed in order to capture the seasonal dynamics of water- and aquatic resource exploitation. A holistic farming systems approach emphasises the potential of ponds in integrated rural smallholder systems to reduce poverty and vulnerability under rain fed conditions. A Geographic Information System (GIS), an efficient spatial inventory tool and decision support system in resolving real world problems, is used to identify where rural poor households can potentially benefit from the integration of aquaculture into existing production systems. A time series of satellite derived vegetation index data reveals distinct agro-ecosystem seasonality over large parts of the study area, which is indicative for farming systems under rain fed conditions. The developed methodology is capable of identifying functionally different agro-ecosystems. Socio-economic indicators for Cambodian parts of the lowland areas point to widespread rural poverty and vulnerability to recurrent food insecurity, which is directly related to agro-ecosystems seasonality and annual climate variability. Dependence of farming households on low productivity rain fed rice agro-ecosystems in Cambodia’s southern provinces is in stark contrast to the highly productive farming systems directly bordering it, in the freshwater fluvial zone of the Vietnamese Mekong Delta. A rapid increase in rice productivity in this densely populated area went hand-in hand with a considerable reduction in rural poverty. In this flood-prone but fertile area, resource competition and falling market prices of rice may have prompted the development of a range of integrated farming systems. The incorporation of ponds on farm in these systems facilitates reuse of nutrients from farm by-products for low-input aquatic resource production. In Northeast Thailand, crop production and low-input aquaculture have been successfully integrated along a tradition of water- and living aquatic resources management in farmer managed systems under resource poor conditions. A spatially linked commune level rural development database for Sisaket province in Northeast Thailand provides a useful framework for planning of aquaculture development through systems that are appropriate and relevant to local socio-economic and agro-ecological conditions. It was concluded that the socio-economic and agro-ecological context of rural poverty in Southeast Cambodia offers scope for similar pathways to improve rural wellbeing and reduce vulnerability to poverty and food insecurity by integrating aquatic resources development in pond based systems as part of an interdisciplinary approach towards rural development