scholarly journals Microcredit and Farmers’ Productivity in Osun State, Nigeria

2019 ◽  
pp. 1-11
Author(s):  
Omobolaji Olubukunmi Obisesan ◽  
Olusegun Bankole Ojewumi ◽  
Adekunle Akintunde Obisesan
Keyword(s):  
Interest Rate ◽  
Regression Model ◽  
Significant Relationship ◽  
Negative Relationship ◽  
Household Size ◽  
Descriptive Statistics ◽  
Tobit Regression ◽  
Lending Institutions ◽  
Tobit Regression Model ◽  
Planting Season

The present work examined micro-credit and farmers’ productivity in Osun State, Nigeria. A total of 140 respondents were interviewed using structured questionnaires. Data obtained was analyzed using descriptive statistics and Tobit regression model. The research showed there was a significant relationship between household size (β=0.623), farming experience (β=0.858) and loan condition (β=1.29) on the acquisition of credit by farmers. Income generated was used as a proxy for productivity, and it was in the minimum of N20,000 per planting season. Interest rate had a negative relationship with credit acquisition which implied the majority of the farmers patronized the informal sources of credit. The research showed that loan conditions from informal sources was favorable compared to that from formal sources; and a reason for the high patronage. This, therefore, suggests that formal lending institutions should relax agricultural lending condition and provide credit for agricultural purposes to increase the productivity of farmers. 

Distribution and Determinants of Fallow Land among Farmers of Central Dry Zone of Karnataka

2020 ◽  
pp. 390-395
Keyword(s):  
Arable Land ◽  
Negative Relationship ◽  
Fallow Land ◽  
Tobit Regression ◽  
Total Size ◽  
High Tendency ◽  
Fertility Status ◽  
Dry Zone ◽  
Land Holdings ◽  
Tobit Regression Model

Farmers in dry regions of India have a high tendency for leaving their land fallow. To decipher this phenomenon, the study was carried out in the Tumkur district of the central dry zone of Karnataka. Results from the study revealed a positive relationship between the size of land holdings and land fallow. The major reason stated by farmers for leaving their fallow land was the scarcity of rainfall or irrigation, and poor land fertility status. The Tobit regression model was fitted to study the determinants of the decision of farmers to keep the arable land fallow, where, dependent variable considered was the share of fallow land in the total size of landholding of farm households. It was found that the availability of water for irrigation and family labour has a negative relationship with fallow land. Other factors determining the extent of fallow land were the distance of land from residences, poor land fertility status and availability of credit.

scholarly journals Analysis of Severe Injury Accident Rates on Interstate Highways Using a Random Parameter Tobit Model

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Minho Park ◽  
Dongmin Lee
Keyword(s):  
Regression Model ◽  
Unobserved Heterogeneity ◽  
Random Parameter ◽  
Tobit Model ◽  
Severe Injury ◽  
Parameter Estimates ◽  
Random Parameters ◽  
Tobit Regression ◽  
Accident Rates ◽  
Tobit Regression Model

In this study, a random parameter Tobit regression model approach was used to account for the distinct censoring problem and unobserved heterogeneity in accident data. We used accident rate data (continuous data) instead of accident frequency data (discrete count data) to address the zero cell problems from data where roadway segments do not have any recorded accidents over the observed time period. The unobserved heterogeneity problem is also considered by using random parameters, which are parameter estimates that vary across observations instead of fixed parameters, which are parameter estimates that are fixed/constant over observations. Nine years (1999–2007) of panel data related to severe injury accidents in Washington State, USA, were used to develop the random parameter Tobit model. The results showed that the Tobit regression model with random parameters is a better approach to explore factors influencing severe injury accident rates on roadway segments under consideration of unobserved heterogeneity problems.

Tobit model-based analysis on the influencing factors of wheat loss during harvesting by the combine harvester

2021 ◽  
pp. 1-14
Author(s):  
Hong Ji ◽  
Xun He ◽  
Li Ding ◽  
Zhe Qu ◽  
Wenkang Huang ◽  
...  
Keyword(s):  
Regression Model ◽  
Influencing Factors ◽  
Loss Rate ◽  
Henan Province ◽  
Normal Operation ◽  
Tobit Regression ◽  
Wheat Field ◽  
Combine Harvester ◽  
Main Factors ◽  
Tobit Regression Model

Based on the investigation data of wheat mechanized harvest in eight major wheat producing areas from the south to the north of Henan Province, the main factors affecting wheat mechanized harvest loss were identified and the influence of each factor was decomposed. In this article, the loss rate of wheat mechanical harvest was calculated by using the method of artificial measurement of wheat yield in the field, and the influencing factors of wheat mechanical harvest operation in 8 regions of Henan province were treated and analyzed by using Tobit regression model. In this paper, the loss rate of wheat mechanical harvest was calculated by using the method of wheat field artificial yield measurement and the influencing factors of wheat mechanical harvest operation in eight regions of Henan province were treated and analyzed by using Tobit regression model. The results show that the average harvest loss rate in the field amounts to 2.96%, the average harvest loss rate at the edge of field amounts to 3.06%, whereas the loss rate in the normal operation area amounts 2.86%. The main factors that caused the harvest loss of wheat field machinery were the maturity of wheat, the area of operation field, the diseases and pests, weather conditions and the accumulated working hours of harvester drivers in a single day. Therefore, the main technical measures to reduce the operation loss of wheat combine harvester were put forward to provide a theoretical basis for promoting the deep integration of agricultural machinery and agronomy.

Operating Efficiency Analysis of Energy Industries in Taiwan

2013 ◽  
Vol 869-870 ◽  
pp. 612-620 ◽  
Author(s):  
Nattanin Ueasin ◽  
Anupong Wongchai
Keyword(s):  
Regression Model ◽  
Stock Exchange ◽  
Efficiency Score ◽  
Operating Efficiency ◽  
Data Envelopment ◽  
Tobit Regression ◽  
Factors Affecting ◽  
Taiwan Stock Exchange ◽  
Energy Companies ◽  
Tobit Regression Model

The energy business has played an important role in an economic growth of Taiwan because the market share is in the high value that can make a significant contribution towards regional and local employment. However, Taiwan is lack of energy resources, making the country highly relies on an import for more than 98 percent of its all energy. At present, a top priority of the countrys policy is to develop clean, sustainable, independent, and efficient energy in order to eliminate the vulnerability from external disruption. Therefore, this research aims to assess the operating efficiency and to analyze factors affecting the efficiency scores of the registered energy companies in the Taiwan Stock Exchange (TWSE) recorded during 2003-2012. The super-efficiency data envelopment analysis (SE-DEA) was initially applied to reveal the additional efficiency scores, followed by the Tobit regression model used to analyze what factors determine the efficiency scores. The empirical results showed that seven DMUs performed efficiently, ranking from 7.29 to 1.02. The company with the best operating performance was Taiwan Cogeneration Corporation (TCC), while the Great Taipei Gas Corporation (GTG) revealed the worst efficiency score. Furthermore, the Tobit regression model explained that the higher number of the local employees, the greater the efficiency scores were. Besides, the lower number of the shareholders, the greater the efficiency scores were. As a result, the Taiwans government is supposed to encourage all energy companies to have a higher number of local employees and shareholders to increase their efficiency scores.

scholarly journals An Experimental Investigation of Consumer Willingness to Pay for Non-GM Foods When an Organic Option Is Present

2006 ◽  
Vol 35 (2) ◽  
pp. 374-385 ◽  
Author(s):  
John C. Bernard ◽  
Chao Zhang ◽  
Katie Gifford
Keyword(s):  
Experimental Investigation ◽  
Willingness To Pay ◽  
Regression Model ◽  
Tobit Regression ◽  
Milk Chocolate ◽  
Gm Foods ◽  
Consumer Demographics ◽  
Tortilla Chips ◽  
Marginal Effects ◽  
Tobit Regression Model

This research compared bids that consumers placed on non genetically modified (GM), organic, and conventional versions of food products in order to determine if the organic market well serves those seeking to avoid GM foods. Auction experiments using potato chips, tortilla chips, and milk chocolate were conducted with 79 subjects. Bids were modeled as a function of consumer demographics using a heteroskedastic tobit regression model. Results with the non-GM attribute nested into the organic characteristic showed that the latter's marginal effects were insignificant. This suggested the potential to further develop non-GM products for consumers not willing to pay extra for the remaining organic attributes.

scholarly journals Input-use Efficiency of Irrigation Farmers in Southwest Nigeria

2020 ◽  
pp. 1-12
Author(s):  
F. M. Oluwatusin ◽  
A. O. Kolawole ◽  
O. A. Aturamu ◽  
K. A. Abdu-Raheem
Keyword(s):  
Water Use Efficiency ◽  
Technical Efficiency ◽  
Water Use ◽  
Sprinkler Irrigation ◽  
Household Size ◽  
Descriptive Statistics ◽  
Tobit Regression ◽  
Input Use ◽  
Southwest Nigeria ◽  
Use Efficiency

This study estimated the technical and water-use efficiency of irrigation farmers in Southwest Nigeria. A multistage sampling procedure was employed to interview one hundred and eighty-five irrigation farmers. Descriptive statistics, Data Envelopment Analysis Program (DEAP) and Tobit regression model were used to describe and analyze the determinants of inefficiency among irrigation farmers. The result of the descriptive statistics showed that there were more male farmers (82.2%) with a mean household size of 3.7. The mean area of irrigated land was 1.29ha. with about 84 percent of farmers cultivating on rented land. The average income from irrigation farming was N 38,650.27 per month. The sprinkler irrigation (Centre Pivot) was used by 85.4% of the farmers. The water fee for farmers was N 10,000 and the average cost of diesel for irrigating their farmland was estimated to be N 34,400 per production. The variation in input use and the output produced is considerably large. The DEAP results showed that on average, the farmers achieved only 79% Constant Return to Scale (CRS) technical efficiency, 85% Variable Return to Scale (VRS) technical efficiency and 22% water use efficiency. The Tobit regression results showed that farmers’ characteristics like age (0.008), household size (0.048), area of land irrigated (0.000), income (0.075) and drip irrigation method (0.041) were significant under CRS, whereas gender, education, experience, labour and sprinkler irrigation method were not significant. Under VRS model, significant variables includes age (0.032), gender (0.003), education (0.080), experience (0.059) and area of land irrigated (0.000), while gender (0.004), household size (0.086), education (0.027), experience (0.045), area of land irrigated (0.000) and labour (0.089) were significant for sub-vector VRS. It appears that farmers have little incentives to use water in an efficient way. In this sense, a public-private partnership could trigger better management of irrigation facilities and efficient water use among farmers.

scholarly journals MODEL REGRESI TOBIT KONSUMSI SUSU CAIR PABRIK (Studi Kasus Rumah Tangga di Provinsi Bali)

2014 ◽  
Vol 3 (2) ◽  
pp. 75 ◽  
Author(s):  
I PUTU JERYANA ◽  
I PUTU EKA NILA KENCANA ◽  
G. K. GANDHIADI
Keyword(s):  
Regression Model ◽  
Sampling Technique ◽  
Information Criterion ◽  
Simple Random Sampling ◽  
Education Level ◽  
Tobit Regression ◽  
Response Variable ◽  
Independent Variables ◽  
Dairy Milk ◽  
Tobit Regression Model

Regression analysis is used to study the relationship between dependent (response) variable with one or more independent (causal) variables. While response data were censored, then Tobit regression model could be applied.  According to Greene (2003), censored data were data with incomplete observation or the dependent variable has a value of zero, while for the other observations have particular value.  This research aimed to model dairy milk’s consumption from households at Bali Province.  By using data from Survey SosialEkonomiNasional (SUSENAS) or Social Economy’s National Survey (SENS) for year 2012, 615 households were selected as sampling unit using simple random sampling technique, and found 123 households who consumed dairy milk.  The independent variables in our model were last education level completed by head of household’s (X1), head of household’s work (X2), age of head of household’s (X3),  amount of expenditure for food consumption’s (X4), number of household members (X5), and household income (X6), the response variable was budget for buying dairy milk (Y).  From six independent variables, is found only last education level by head household and amount of expenditure for food consumption had siginficant effect on Y’s.  The final Tobit regression model were obtained using AIC (Akaike Information Criterion) method is Y = -3314724 + 565429,7 X1 + 0,014278 X4 with pseudo R2 as much as 16.79 per cent.

scholarly journals Determinants of the Adoption of Quality Protein Maize among Farming Households in Niger State, Nigeria

2017 ◽  
Vol 2 (1) ◽  
pp. 8-12
Author(s):  
Opeyemi E. Ayinde ◽  
Tahirou Abdoulaye ◽  
Olatunji Y. Ambali ◽  
Grace O. Akinsola
Keyword(s):  
Random Sampling ◽  
Regression Models ◽  
Information Dissemination ◽  
Sampling Procedure ◽  
Household Size ◽  
Descriptive Statistics ◽  
Quality Protein Maize ◽  
Tobit Regression ◽  
Adoption Level ◽  
Farming Households

AbstractThe study was conducted to examine the determinants of quality protein maize (QPM) adoption among farming households in Niger State, Nigeria. A twostage random sampling procedure was employed to obtain data from selected 120 maize farming households. Descriptive statistics, logistic and tobit regression models were used for the analysis. Results of the analysis show that the awareness and adoption level of QPM production in the study area were 95% and 87% respectively. Among the determinants of QPM adoption status, the coefficient of age (-0.12734) and primary occupation (-1.38295) of the respondents were negative and significant at 5% level of probability while the tenancy attribute’s coefficient (2.62533) was positively significant at 10%. The level of adoption of QPM was positively and significantly influenced by household size (0.00729) and years of schooling (0.01148) at 10% and 5% level of significant respectively. The study recommends intensification of information dissemination on the adoption of QPM in the study area.

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