Determinants of Inorganic Fertilizer Use Intensity on Cereal Crop among Smallholder Farmers: The Case of Toke Kutaye District, West Shewa Zone, Oromia National Regional State, Ethiopia

The study was aimed to analysis determinants of inorganic fertilizer use intensity on cereal crops among small holders in Toke Kutaye District, West Shewa Zone, Ethiopia. Correctional data were collected from 156 respondents using two stage random sampling methods. Data analyses were carried out using descriptive statistics and Double hurdle model. Result of the first hurdle reveals that out of twelve explanatory variables Sex ,Education, Off/non-farm income, Land size and Improved seed were determine positively whereas Age and Distance from nearest market determine small holders use of inorganic fertilizer negatively. The result of second stage of double hurdle model indicate that, out of twelve explanatory variables Sex, family size and Land size were positively affect extent (intensity) of inorganic fertilizer use whereas Age and Distance of household from nearest market determine use intensity negatively. Therefore, these results implied that there is a room to increase inorganic fertilizer use intensity on cereal crop productions. Hence, Farmers capacity to purchase this input beginning from lower income farmers to model farmers should be acknowledged; and should be designed the means to address those who have no ability to use inorganic fertilizer in their own farm through diverse development interventions.

Farm typology of smallholders integrated farming systems in Southern Coastal Plains of Kerala, India

Adoption of an integrated farming system (IFS) is essential to achieve food and nutritional security in small and marginal holdings. Assessment of IFS to know the resource availability and socio-economic condition of the farm household, farm typology plays a critical role. In this regard, a sample survey of 200 marginal households practicing mixed crop-livestock agriculture was conducted during 2018–2019 at Southern Coastal Plains, which occupies 19,344 ha in Thiruvananthapuram district, Kerala, India. Farming system typology using multivariate statistical techniques of principal component analysis and cluster analysis characterized the diverse farm households coexisting within distinct homogenous farm types. Farming system typology identified four distinct farm types viz. resource constrained type-1 households with small land owned, high abundance of poultry, very low on-farm income, constituted 46.5%; resource endowed type-2 households oriented around fruit and vegetable, plantation crop, with a moderate abundance of large ruminant and poultry, high on-farm income, constituted 12.5%; resource endowed type-3 household oriented around food grain, extensive use of farm machinery, with a moderate abundance of large ruminant, low on-farm income, constituted 21.5%; and resource endowed type-4 household oriented around fodder, with high abundance of large ruminant, medium on-farm income, constituted 19.5% of sampled households. Constraint analysis using constraint severity index assessed the severity of constraints in food grain, horticulture, livestock, complementary and supplementary enterprises in each farm type, which allowed targeted farming systems interventions to be envisaged to overcome soil health problems, crops and animal production constraints. Farming system typology together with constraint analysis are therefore suggested as a practical framework capable of identifying type-specific farm households for targeted farming systems interventions.

Attributes of Farm Income Operating on Conservation Agriculture: TheMultivariate and ANN Analytics

Conservation agriculture (CA) is the combination of environmental management, modernand scientific agriculture, which employs farmers’ ability to utilize, innovate, and adapt tochanging situations, as well as their holistic acceptance of knowledge along with ensuringsustainability. Farm-level adoption of CA is related to reduced labour and agricultural inputs,more consistent yields, and increased soil nutrient exchange capacity. A good quality landyields good results to everyone, confers good health on the entire family, and causes growthof money, cattle, and grain. The present study depicts hard evidences by identifying markervariables impacting income augmentation through conservation agriculture. A score of 50farmers has been selected from two blocks of Cooch Behar district of West Bengal, bynon-probability snowballing sampling techniques with a total of eighteen independentvariables along with income from major crop is used as the dependent variable through astructured interview schedule. A basket of multivariate analytical techniques has been appliedalong with Artificial Neural Network (ANN) as well. The results depict that a blend ofdiversified farming and farming experiences in CA contributed immensely to scale up incomefrom conservation agriculture approaches.

Sustainability indicators in Agriculture: A Review and Bibliometric analysis using Scopus database.

Indicators are being used in many agricultural sustainability assessment methods, but disputes about a common indicator for the definition of sustainability have resulted in so many various indicators and methods of measurement. The objective of this review is to provide a bibliometric analysis of sustainability pillars and indicators that has been widely applied. In addition, this paper evaluates the impact of pillars and indicators on scientific research through the analysis of their citation and trend. Using Scopus database, a total of 30 articles have been selected. The search revealed more than 500 indicators, and the top 3 indicators of each pillar which were considered in 7 articles or more are (soil erosion, crop diversity and pesticides) for environmental pillar, (education and training) for social pillar are and (Profitability, productivity and farm income) for economic pillar. Results showed that the environmental pillar is the most tackled in terms of the number of articles (n=22) and the most cited with a mean citation of about 60. The pesticide is the oldest indicator in terms of its average year of publication in 2011, the most cited indicator of more than 250 in 2005 and has the highest mean citation of about 42. The least cited indicators are farm income and training with less than 10 mean citation. Nowadays, the economic pillar is considered one of the most discussed and widely implemented with a total of 7 published articles in 2020.

Assessing the Impact of Climate Resilient Technologies in Minimizing Drought Impacts on Farm Incomes in Drylands

Asia is the region most vulnerable to climate change and India is ranked as one of the most climate vulnerable countries in the world, frequently affected by natural disasters. In this study, we investigated the impact of drought on crop productivity, farmer’s employment and income. The difference-in-difference model (DID) and stepwise multiple linear regression (MLR) were employed to quantify the impact of adopting climate resilient technologies (CRTs) on farm household income during a drought. The factors influencing farm incomes were analyzed using MLR. The study used survey data collected from the drought prone district of Telangana, India. Sixty farmers each from a village adopted under the National Innovations in Climate Resilient Agriculture (NICRA) program and a control village were interviewed. Primary data on the socio-economic characteristic of farmers, cropping pattern, income composition, productivity of major crops, employment and climate resilient interventions adopted by farmers were collected using a well-structured schedule. The results reveal that income crop cultivation was the major contributor to household income (60%) followed by livestock rearing. Farmers reported that droughts decreased the income from crops by 54 per cent and income from livestock rearing by 40 per cent. The farmers belonging to the climate resilient village had 35 per cent higher incomes compared to those in the control village and it was estimated to be Rs. 31,877/farm household/year during droughts using the DID estimate. Farm size, livestock possession, adoption of CRTs and investment in agriculture were the determining factors influencing farm income. Thus, farmers especially in drought prone regions need to be encouraged and supported to adopt cost effective, location specific climate resilient technologies.

Effect of Ondo State Agricultural Inputs Supply Agency on profitability of crop farmers in Owo Lga, Ondo State, Nigeria

This study assessed the effect of Ondo State Agricultural Inputs Supply Agency (OSAISA) on the profitability of crop farmers in Owo Local Government Area of Ondo State, Nigeria. The study specifically described the socio-economic characteristics of arable crop farmers, compared the profitability of OSAISA patronizing food crop farmers (PF) and non-patronizing food crop farmers (NPF) and identified the various constraints encountered by patronizing farmers in dealing with OSAISA. One hundred and twenty food crop farmers random sampling procedure. Information was obtained from the respondents using a well-structured questionnaire. Data collected were analyzed with both descriptive statistics and budgeting technique. Findings revealed that 88.3% and 86.7% of the PF and NPF, respectively were males. About 50.0% of PF and 56.7% of NPF were between 41 and 50 years of age. The net farm income of the PF was greater than the NPF and benefit cost ratio for PF was more sustainable and viable than that of NPF. The major constraint faced by the OSAISA’ PF was inadequate capital to purchase the desired inputs. Based on the results, the study concludes that OSAISA contributes tremendously to the profitability of patronizing farmers in the study area. It is, therefore, recommended that farmers should be given easy access to acquire loan to meet their input demand and farming business in general; including adequate and timely supply of inputs for effective and efficient productivity.

Analysis on Resource Use Efficiency of Smallholder's Rice Production Farmers in Federal Capital Territory of Nigeria: Probit Model Approach

This study examined the resource-use efficiency of smallholder rice production farmers in Federal Capital Territory, Nigeria. The problem of resource use among small-scale rice production farmers is preponderance in the country. Hence, the study investigated the drivers of the problem in the Federal Capital Territory of Nigeria. Specifically, the study was designed to determine the factors influencing the resource-use efficiency of the respondents. A multi-stage sampling technique was used to select a total sample size of one hundred and seventy-five (175) rice farmers in Federal Capital Territory, Nigeria. Seven estimators such as age, household size, farming experience, educational level, extension services, access to credit, and off-farm income in the Probit model were found statistically significant. Results show that the probability of resource use efficiency of inputs used by the farmers increases with age, farm size, household size, educational level, extension services, experiences in farming, access to credits, but decreases where they have off-farm income. Mc Fadden Pseudo-R2 gives 0.6772, and the Probit model explains a significant proportion of the variations in smallholder farmers' resource use. The study concluded that the socio-economic variables in the model play an important role in influencing resource use efficiency. The study recommends that government agencies and donors should provide simplified, accessible and obtainable credits and grants to existing and prospective rice farmers in order to sustain the current giant stride in rice production in the country.

Participation in farmer organizations and adoption of farming technologies among rice farmers in Ghana

PurposeThe authors examine the factors influencing membership in farmer organizations (FO) and their effects on the decision to adopt farm technologies by rice farmers in Ghana.Design/methodology/approachThis study uses a farm survey of 900 households from Northern Ghana and a recursive bivariate probit (RBP) model that accounts for selection bias and endogeneity.FindingsThe results indicate that the household head’s decision to adopt machinery and row planting increases by 38.4 and 25.3%, respectively, upon joining a farmer organization. Membership in farmer organization is positively influenced by off-farm income, asset value, farmer organization location and farmer location in Upper West region but negatively by males, age and total livestock units owned. Machinery adoption is positively influenced by membership in farmer organizations and respondent being male but negatively influenced by the years of schooling, farm size, farm distance and location of a farmer in Ghana's Upper East and West regions. Similarly, row planting adoption is positively influenced by membership in farmers' organization but adversely by farm size, farm distance and a farmer's location in Upper East region of Ghana.Research limitations/implicationsIt can be concluded that membership in farmers' organizations significantly impacts farm household head’s decision to adopt machinery and row planting in rice production, which potentially enhance crop productivity.Practical implicationsThese results show the importance of agricultural stakeholders in encouraging the formation and strengthening of farmer organizations to support the adoption of modern farming technologies.Originality/valueDeveloping literature has demonstrated that farmer organizations promote the adoption of agricultural innovations. However, most of these studies have concentrated on conventional agricultural innovations and have used methods that fail to account for potential selection bias. This paper fills this important gap.

Climate Change Influence On Ontario Corn Farms’ Income.

Our study quantifies the impact of climate change on the income of corn farms in Ontario, at the 2068 horizon, under several warming scenarios. It is articulated around a discrete- time dynamic model of corn farm income with an annual time-step, corresponding to one agricultural cycle from planting to harvest. At each period, we compute the income of a farm given the corn yield, which is highly dependent on weather variables: temperature and rainfall. We also provide a reproducible forecast of the yearly distribution of corn yield for the regions around ten cities in Ontario, located where most of the corn growing activity takes place in the province. The price of corn futures at harvest time is taken into account and we fit our model by using 49 years of county-level historical climate and corn yield data. We then conduct out-of-sample Monte-Carlo simulations in order to obtain the farm income forecasts under a given climate change scenario, from 0 ° C to + 4 ° C.