Assessment of the production and importance of cowpea [Vigna unguiculata (L.) walp]: Cases from selected districts of southern Ethiopia

Cowpea (Vigna unguiculata L.Walp) is an important legume in the hot, dry tropics and subtropics of sub-Saharan Africa, serving a multiple role for the livelihoods of millions of relatively low-income people. The entire plant can be used for either human or livestock consumption and with considerable drought-tolerating capacity. Tender young leaves, green pods and matured seeds are used as human food. Moreover, the crop serves for sustainable soil fertility improvement due to its excellent nitrogen-fixing capacity. However, its production and utilization are limited in Ethiopia partly due to dependence on the conventional agronomic practices and lack of information on its wide ranging uses. This study was conducted to assess the cowpea agronomy and the contributions the crop has in the livelihoods of farmers at Loka-Abaya and Humbo districts of Southern Ethiopia. Multi-stage sampling techniques were employed to achieve the set objectives. Both primary and secondary data were collected to solicit the required information. The data were subjected to descriptive and inferential statistics such as multiple linear regression model using the SPSS Software version 20 and STATA 13. Multiple linear regression model results showed that education, land size, climate information access, credit access, lack of market chain, availability of seed of improved varieties, and pests significantly (P<0.001) affected cowpea production in the studied areas. The trend analysis showed that the cowpea yield and production area coverage is increasing in Humbo District whereas, a decreasing trend was observed at the Loka Abaya. According to the household interview data, about 76 % of the respondents reported a decrease in the cultivated area of cowpea. According to the respondents, lack of access to improved seed and lack of extension support services contributed 79 % and 73 %, respectively to the low yield observed in the area. The majority of the respondents cultivate cowpea as intercropping and rotation with cereals and in the main field with the main purpose to replenish soil fertility (97 %). On the other hand, 62 % of the respondents cultivate cowpea for home consumption. According to the survey result, 48 % of the respondents use the matured grain for consumption. The production trends of the cowpea are highly variable mainly due to less attention paid by the extension systems to boost the yield of the crop, reliance of farmers on local varieties, pest occurrence and poor market chain. Therefore, modern production technologies including the supply of improved varieties of seed with their full production package should be introduced to the area so as to improve the yield and optimize its contribution towards achieving food security.

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An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327910666191218124350 ◽

2020 ◽

Vol 10 (4) ◽

pp. 473-483

Author(s):

Pundra Chandra Shaker Reddy ◽

Alladi Sureshbabu

Keyword(s):

Linear Regression ◽

Regression Model ◽

Multiple Linear Regression ◽

Linear Regression Model ◽

Weather Prediction ◽

Big Data Analytics ◽

Multiple Linear Regression Model ◽

Seasonal Rainfall ◽

Climate Data ◽

Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

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A Multiple Linear Regression Model for Maintainability Analysis Considering Maintainability Design Characteristics

Proceedings of the 4th International Conference on Computer Science and Application Engineering ◽

10.1145/3424978.3424988 ◽

2020 ◽

Author(s):

Zhenya Wu ◽

Jianping Hao ◽

Peng Wang ◽

Chenguang Liu ◽

Zhen Li

Keyword(s):

Linear Regression ◽

Regression Model ◽

Multiple Linear Regression ◽

Linear Regression Model ◽

Multiple Linear Regression Model ◽

Design Characteristics ◽

Maintainability Analysis

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Magnetization Transfer Ratio of Peripheral Nerve and Skeletal Muscle

Clinical Neuroradiology ◽

10.1007/s00062-021-01067-5 ◽

2021 ◽

Author(s):

Olivia Fösleitner ◽

Véronique Schwehr ◽

Tim Godel ◽

Fabian Preisner ◽

Philipp Bäumer ◽

...

Keyword(s):

Skeletal Muscle ◽

Body Weight ◽

Sciatic Nerve ◽

Linear Regression ◽

Peripheral Nerve ◽

Regression Model ◽

Multiple Linear Regression ◽

Linear Regression Model ◽

Magnetization Transfer ◽

Multiple Linear Regression Model

Abstract Purpose To assess the correlation of peripheral nerve and skeletal muscle magnetization transfer ratio (MTR) with demographic variables. Methods In this study 59 healthy adults evenly distributed across 6 decades (mean age 50.5 years ±17.1, 29 women) underwent magnetization transfer imaging and high-resolution T2-weighted imaging of the sciatic nerve at 3 T. Mean sciatic nerve MTR as well as MTR of biceps femoris and vastus lateralis muscles were calculated based on manual segmentation on six representative slices. Correlations of MTR with age, body height, body weight, and body mass index (BMI) were expressed by Pearson coefficients. Best predictors for nerve and muscle MTR were determined using a multiple linear regression model with forward variable selection and fivefold cross-validation. Results Sciatic nerve MTR showed significant negative correlations with age (r = −0.47, p < 0.001), BMI (r = −0.44, p < 0.001), and body weight (r = −0.36, p = 0.006) but not with body height (p = 0.55). The multiple linear regression model determined age and BMI as best predictors for nerve MTR (R2 = 0.40). The MTR values were different between nerve and muscle tissue (p < 0.0001), but similar between muscles. Muscle MTR was associated with BMI (r = −0.46, p < 0.001 and r = −0.40, p = 0.002) and body weight (r = −0.36, p = 0.005 and r = −0.28, p = 0.035). The BMI was selected as best predictor for mean muscle MTR in the multiple linear regression model (R2 = 0.26). Conclusion Peripheral nerve MTR decreases with higher age and BMI. Studies that assess peripheral nerve MTR should consider age and BMI effects. Skeletal muscle MTR is primarily associated with BMI but overall less dependent on demographic variables.

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