Study on the Crop Suitability Analysis System Based on WebGIS in Henan

In agriculture, land use and land classification address questions such as “where”, “why” and “when” a particular crop is grown within a particular agroecology. To date, there are several land suitability analysis (LSA) methods, but there is no consensus on the best method for crop suitability analysis. We conducted a scoping review to evaluate methodological strategies for LSA. Secondary to this, we assessed which of these would be suitable for neglected and underutilised crop species (NUS). The review classified LSA methods reported in articles as traditional (26.6%) and modern (63.4%). Modern approaches, including multi-criteria decision-making (MCDM) methods such as analytical hierarchy process (AHP) (14.9%) and fuzzy methods (12.9%); crop simulation models (9.9%) and machine learning related methods (25.7%) are gaining popularity over traditional methods. The MCDM methods, namely AHP and fuzzy, are commonly applied to LSA while crop models and machine learning related methods are gaining popularity. A total of 67 parameters from climatic, hydrology, soil, socio-economic and landscape properties are essential in LSA. Unavailability and the inclusion of categorical datasets from social sources is a challenge. Using big data and Internet of Things (IoT) improves the accuracy and reliability of LSA methods. The review expects to provide researchers and decision-makers with the most robust methods and standard parameters required in developing LSA for NUS. Qualitative and quantitative approaches must be integrated into unique hybrid land evaluation systems to improve LSA.

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AFRICAP - The impact of climate change on agriculture in Tanzania, Malawi, Zambia and South Africa

10.5194/egusphere-egu2020-1927 ◽

2020 ◽

Author(s):

Sarah Chapman ◽

Cathryn Birch ◽

Edward Pope ◽

Susannah Sallu ◽

Catherine Bradshaw ◽

...

Keyword(s):

Climate Change ◽

South Africa ◽

Sub Saharan Africa ◽

Cmip5 Models ◽

Comprehensive Treatment ◽

Climate Projections ◽

Suitability Analysis ◽

Crop Suitability ◽

Sub Saharan ◽

The Impact

Sub-Saharan Africa is one of the most food insecure regions in the world and is highly vulnerable to climate change. We use a comprehensive set of bias-corrected global (CMIP5) and regional (CORDEX-Africa) models and a new convection-permitting pan-Africa simulation (and its parameterized counterpart) to examine changes in rainfall and temperature and the impact on agricultural suitability of maize, cassava and soy in sub-Saharan Africa by 2100 (RCP8.5). This is the first time a convection-permitting projection has been used to examine agricultural suitability in Africa. Increasing temperatures and declining rainfall led to large parts of sub-Saharan Africa becoming unsuitable for multiple staple crops, which may necessitate a transition to more heat and drought resistant crops to ensure food and nutrition security. Soy was resilient to temperature increases, however maize and cassava were not, leading to declines in crop suitability. Inclusion of sensitivity to extreme temperatures led to larger declines in maize suitability than when this was excluded. The variation in rainfall projections within the multi-model ensemble was examined in detail for Tanzania, Malawi, Zambia and South Africa. In each country the range of projections included wetting and drying, but the majority of models projected rainfall declines, except in Tanzania, leading to declines in crop suitability. Overall, the CORDEX and CMIP5 models gave similar results for agricultural suitability. Explicit-convection led to more temperature extremes, but had little systematic impact on temperature and rainfall, and the resulting suitability analysis. Global model uncertainty, rather than convection parameterizations, still makes up the largest part of the uncertainty in future climate. Explicit-convection may have more impact if suitability included a more comprehensive treatment of extremes. This work highlights the key uncertainty from global climate projections for crop suitability projections, and the need for improved information on sensitivities of African crops to extremes, in order to give better predictions and make better use of the new generation of explicit-convection models.

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CROP SUITABILITY ANALYSIS FOR CEREAL CROPS OF UTTAR PRADESH, INDIA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-5-353-2018 ◽

2018 ◽

Vol XLII-5 ◽

pp. 353-360

Author(s):

P. Singh ◽

R. K. Upadhyay ◽

H. P. Bhatt ◽

M. P. Oza ◽

S. P. Vyas

Keyword(s):

Crop Production ◽

Uttar Pradesh ◽

Cereal Crops ◽

Suitability Analysis ◽

Climate Data ◽

Analytical Hierarchical Process ◽

Crop Suitability ◽

Gis Data ◽

Suitability Map ◽

Growing Conditions

Abstract. The crop suitability is the process of assessing the appropriateness or ability of a given type of land on the basis of growing conditions of a particular crop. The study focused on the crop suitability analysis of cereal crops for their production in Uttar Pradesh. Information about crop suitability is essential for proper management of agriculture in the study area. Remote sensing and GIS data provide a reliable information and technique to find suitable land for crops. The research was based on GIS based Multi-Criteria Decision Approach. The AMSR-2 (Advance Microwave Scanning Radiometer) soil moisture data, Carto-DEM, soil chemical and physical properties and climate data were used to identify the crop suitability in the study area. Weightage of different factors was arrived at based on input and feedback from experts. An Analytical Hierarchical Process (AHP) was used in ArcGIS environment to generate suitability map for the cereals crop. The suitability map has been categorised in the form of highly suitable, moderately suitable, low suitable and non-agricultural/ non-suitable region of the study area for Wheat, Rice, Sorghum, Maize and Pearl Millet/Bajra.The overall study indicates that the study area has a huge potential of cereal crop production. Therefore, improved levels of agricultural production can be achieved by cultivating crop in highly and moderately suitable areas; and practicing diversification of marginally suitable areas to crops other than that for which it is low suitable.

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Multi-criteria suitability analysis for neglected and underutilised crop species in South Africa

PLoS ONE ◽

10.1371/journal.pone.0244734 ◽

2021 ◽

Vol 16 (1) ◽

pp. e0244734

Author(s):

Hillary Mugiyo ◽

Vimbayi G. P. Chimonyo ◽

Mbulisi Sibanda ◽

Richard Kunz ◽

Luxon Nhamo ◽

...

Keyword(s):

South Africa ◽

Cropping Systems ◽

Arable Land ◽

Model Performance ◽

Area Under The Curve ◽

Land Suitability ◽

Suitability Analysis ◽

Crop Species ◽

Crop Suitability ◽

Suitability Maps

Several neglected and underutilised species (NUS) provide solutions to climate change and creating a Zero Hunger world, the Sustainable Development Goal 2. Several NUS are drought and heat stress-tolerant, making them ideal for improving marginalised cropping systems in drought-prone areas. However, owing to their status as NUS, current crop suitability maps do not include them as part of the crop choices. This study aimed to develop land suitability maps for selected NUS [sorghum, (Sorghum bicolor), cowpea (Vigna unguiculata), amaranth and taro (Colocasia esculenta)] using Analytic Hierarchy Process (AHP) in ArcGIS. Multidisciplinary factors from climatic, soil and landscape, socio-economic and technical indicators overlaid using Weighted Overlay Analysis. Validation was done through field visits, and area under the curve (AUC) was used to measure AHP model performance. The results indicated that sorghum was highly suitable (S1) = 2%, moderately suitable (S2) = 61%, marginally suitable (S3) = 33%, and unsuitable (N1) = 4%, cowpea S1 = 3%, S2 = 56%, S3 = 39%, N1 = 2%, amaranth S1 = 8%, S2 = 81%, S3 = 11%, and taro S1 = 0.4%, S2 = 28%, S3 = 64%, N1 = 7%, of calculated arable land of SA (12 655 859 ha). Overall, the validation showed that the mapping exercises exhibited a high degree of accuracies (i.e. sorghum AUC = 0.87, cowpea AUC = 0.88, amaranth AUC = 0.95 and taro AUC = 0.82). Rainfall was the most critical variable and criteria with the highest impact on land suitability of the NUS. Results of this study suggest that South Africa has a huge potential for NUS production. The maps developed can contribute to evidence-based and site-specific recommendations for NUS and their mainstreaming. Also, the maps can be used to design appropriate production guidelines and to support existing policy frameworks which advocate for sustainable intensification of marginalised cropping systems through increased crop diversity and the use of stress-tolerant food crops.

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Automated Image Analysis Of Nuclear And Cytoplasmic Changes In Exfoliated Cells From Tracheas Treated With Carcinogen

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100078638 ◽

1977 ◽

Vol 35 ◽

pp. 220-221

Author(s):

S.F. Stinson ◽

J.C. Lilga ◽

M.B. Sporn

Keyword(s):

Image Analysis ◽

Pattern Analysis ◽

Relative Proportion ◽

Automated Image Analysis ◽

Image Analyzer ◽

Cross Sectional ◽

Exfoliated Cells ◽

Neoplastic Lesions ◽

Analysis System ◽

Morphologic Criterion

Increased nuclear size, resulting in an increase in the relative proportion of nuclear to cytoplasmic sizes, is an important morphologic criterion for the evaluation of neoplastic and pre-neoplastic cells. This paper describes investigations into the suitability of automated image analysis for quantitating changes in nuclear and cytoplasmic cross-sectional areas in exfoliated cells from tracheas treated with carcinogen.Neoplastic and pre-neoplastic lesions were induced in the tracheas of Syrian hamsters with the carcinogen N-methyl-N-nitrosourea. Cytology samples were collected intra-tracheally with a specially designed catheter (1) and stained by a modified Papanicolaou technique. Three cytology specimens were selected from animals with normal tracheas, 3 from animals with dysplastic changes, and 3 from animals with epidermoid carcinoma. One hundred randomly selected cells on each slide were analyzed with a Bausch and Lomb Pattern Analysis System automated image analyzer.

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Digital Processing of STEM Images

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100097752 ◽

1981 ◽

Vol 39 ◽

pp. 236-237

Author(s):

A. V. Crewe ◽

M. Ohtsuki

Keyword(s):

High Resolution ◽

Low Dose ◽

Assembly Language ◽

Processing System ◽

Principal Component ◽

Digital Processing ◽

Image Analysis System ◽

Black And White ◽

Analysis System ◽

Dose Imaging

We have assembled an image processing system for use with our high resolution STEM for the particular purpose of working with low dose images of biological specimens. The system is quite flexible, however, and can be used for a wide variety of images.The original images are stored on magnetic tape at the microscope using the digitized signals from the detectors. For low dose imaging, these are “first scan” exposures using an automatic montage system. One Nova minicomputer and one tape drive are dedicated to this task.The principal component of the image analysis system is a Lexidata 3400 frame store memory. This memory is arranged in a 640 x 512 x 16 bit configuration. Images are displayed simultaneously on two high resolution monitors, one color and one black and white. Interaction with the memory is obtained using a Nova 4 (32K) computer and a trackball and switch unit provided by Lexidata.The language used is BASIC and uses a variety of assembly language Calls, some provided by Lexidata, but the majority written by students (D. Kopf and N. Townes).

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