I hereby declare that the thesis entitled "Dairy Productivity and Crossbreeding Technology Adoption in Assam: Impact and Determinants" is my original research work carried out in the Department of Humanities and Social Sciences, Indian Institute of Technology Guwahati, India, under the supervision of Dr. Chapter Three: Crossbreeding Technology Diffusion, Cattle Population Composition Change and Milk Production Dynamics in Assam.
List of Tables
129 5.8: Variance Inflation Factor of Explanatory Variables 130 5.9: Labor Use in Different Dairy Activities in Different Farm Sizes and. 140 5.14: Variance Inflation Factor (VIF) of Explanatory Variables: OLS 141 5.15: Milk Production, Sales and Holding Pattern for Consumption Between Samples.
MASB : Mean Absolute Standardized Bias MSP : Minimum Standard Protocol NDDB : National Dairy Development Board NDRI : National Dairy Research Institute. NPBBDD : National Program for Cattle Breeding and Dairy Development NPCBB : National Project on Cattle and Buffalo Breeding.
Abstract
The treatment impact of net dairy income per dairy cattle per day of crossbred cattle adopters increases in the range of Rs. Access to local breeding stock negatively affects both adoption and extent of adoption of AI.
INTRODUCTION
- Background of the study
- Statement of the Problem
- Objectives
- Hypotheses
- Data and Methodology
- Layout of the dissertation
Despite efforts to increase cross-breed adoption under various government programs, the share of cross-bred cattle in the total livestock population in the state is abysmally low. Understanding dairy production and cattle population dynamics in the context of the spread of crossbreeding technology in the state.
REVIEW OF LITERATURE
Crossbreeding Technology and Dairy Productivity
The above discussion thus confirms the arguments to go for the introduction of crossbreeding technology to increase the milk productivity of the cattle and buffalo population in the country. The trend in the growth of the crossbred cattle population is very insignificant compared to the statistics for the same in developed countries (Kaaya et al., 2005).
Impact of Crossbreeding Technology
A detailed discussion on the impact of crossing technology adoption is presented in the next section (section 2.2). Quddus (2012) observes that the adoption of crossbreeding technology contributes wonderfully to the improvement of the living standards of rural residents.
Feeds and Fodder Availability and Crossbreeding Technology
But its adoption on farms is low due to the technology's misalignment in the agricultural system. Here too, the size of the herd on the farm is an important element in the management of a dairy farm.
Determinants of Crossbreeding Technology Adoption
Farmers' age (-), jobs outside agriculture (-) and farmers with children (+) fall under the farmer's personal characteristics, whereas the structural farm factors are gross margin per livestock unit (+), farm size (-), advisory services (+) and cost of artificial intelligence (-) have been found to influence the adoption of artificial intelligence technology in Ireland. There are theoretical and empirical studies that have dealt with several issues underlying the adoption of the technology in the dairy sector.
Summing Up
According to Tyagi and Sohal (1984), economic motivation, milk price and knowledge of technology are significantly related to the adoption of dairy innovation. This necessitates research at a grassroots level into farmers' access to feed and feeding and conservation practices and their views on future dairy farming in the context of the challenge. Some empirical studies also emphasize the characteristics of the technology itself (such as known AI technicians, distance from farm to AI technicians, and cost per AI service per farm and farm per bull) to influence the adoption of crossbreeding technology.
CROSSBREEDING TECHNOLOGY DIFFUSION, COMPOSITIONAL CHANGE OF BOVINE POPULATION AND MILK PRODUCTION
DYNAMICS IN ASSAM
Crossbreeding Technology Diffusion in Assam: A Background
- Cattle Breeding Policy in Assam
- Basics of the Present Cattle Breeding Policy of Assam 1. Policy for Milk Production
- Policy for Draftability
- Infrastructure Development for Crossbreeding Technology
- Initiatives for Semen Production and Expansion of AI Network in Assam
- Semen Production
- Semen Distribution
- Artificial Insemination Network
Recently, under Rastriya Krishi Vikash Yojna (RKVY), the AI network has been expanded to all veterinary institutions (total 1275 centers) in the state. Source: Assam Livestock Development Agency (ALDA), Khanapara, Assam the contents of the state's latest breeding policy that focuses solely on mating Jersey with indigenous cattle. Assam Livestock Development Agency (ALDA), Khanapara, Assam the contents of the state's latest breeding policy that focuses solely on breeding Jersey with indigenous cattle.
Performance of Crossbreeding Technology Diffusion
- Composition of Bovine Population
- Growth of Bovine Population
- Distribution of Bovine Population
Moreover, a significant proportion of cattle and buffaloes remain dry as more than 40 percent of adult females are not in. According to the 19th Livestock Census, 2012 of Assam, the composition of the total cattle population in the state is 90.22 percent of the population . Again, the proportion of indigenous breed dairy cattle population is around 11 percent (highest in Nalbari district and lowest in Jorhat district).
Crossbreeding and Milk Production Dynamics
- Trend in Bovine Milk Production and Productivity in Assam
- Cattle Crossbreeding and Milk Production Linkage
- Decomposition of the Factors Contributing to the Growth of Milk Production
The increase in buffalo milk production in the period under review is relatively higher than the milk production of indigenous breeds. The increase in domestic production of bovine milk in the state is not evident due to a relatively constant rate of yield over the years (see Table 3.18). During the period under review, the effect of yield is dominant (57 percent) in increasing the production of bovine milk in the state.
Summing up
A perusal of the table shows that over time productivity has played an important role in increasing the production of bovine milk in the state. From table 3.22, it is summarized that the production of bovine milk has increased in Assam due to increase in productivity as there is an increase in the proportion of high yielding crossbred cattle in the recent period in Assam. It has been found that the crossbred cattle population contributes significantly to the total milk production in the state.
FIELD STUDY LOCATION, SAMPLE AND SAMPLE PROFILE
The Purpose of Field Study
Keeping these requirements in mind, a field survey was conducted during December 2015 to March 2016 in few selected districts of the state. Thus, the overall objective of the field study is to understand impact indicators that subsequently adopt the technology and investigate the factors that influence adoption and the extent of adoption (for farmers who have continued to adopt) of AI technology (to increase the share of crossbred cattle). ). The field study also attempts to identify the constraints that farmers perceive to inhibit the adoption of AI technology.
Sampling and Sample Selection
The District Veterinary Officer (DVO) and the Veterinarian who is in charge of the Block Veterinary Dispensary. A total of 245 respondents (135 adopters and 108 non-adopters) selected in this way were interviewed to generate primary data to meet the objectives of the study. The interview schedule was prepared in consultation with the study supervisor and finalized after several rounds of pilot surveys in each sample district.
INDIA
Map, not to scale
ASSAM
Broad Profile of the Sample Districts
- Barpeta District
- Sonitpur District
The population of Barpeta district shares 5.43 percent of the total population of the state. The percentage share of poultry population of Sonitpur district is 6.03 percent of the total poultry population in the state. Like Sonitpur district, 99.13 percent of the total cattle population is reared in the rural areas of the district.
Composition of the Sample
The total livestock population in Karbi Anglong district stands at 8,22,729, with a share of 4.31 percent of the state's total livestock population. Among the districts in Assam, Karbi Anglong has the highest share of pig population at 9.92 percent in the total pig population of the state. The highest share of pig population in the district is attributed to the dominance of the tribal population of the district who specialize in pig farming.
Bajali CDB
Manager, Veterinary Doctor and Veterinary Field Assistant (VFA) that the distribution of sample villages in Mandia CDB is almost evenly distributed between adopter and non-adopter groups. Thus, the same proportion of sample households was surveyed in all three villages of this block (see Table 4.2). The two CDBs surveyed are Biswanath and Baghmora, which cover nearly 51 and 49 percent of the total surveyed sample households in the district respectively.
Biswanath CDB
Of the three districts under study, Sonitpur is incorporated as one of the sample districts in the study which falls as middle category district in terms of AI coverage of breeding cattle population (5.97 percent). Lumbajong and Rongkhang CDBs are surveyed and account for 57.5 and 42.5 percent of adopters and 45 and 55 percent of non-adopters, respectively. Thus, a total of 52 percent of households are surveyed in Lumbajong CDB and 48 percent in Rongkhang CDB.
Lumbajong CDB
- Family Size
- Educational Profile of the Sample Farmers
- Housing Condition and Access to Assets and Amenities
- Household Access to Institutional Credit and Savings
- Agrarian Structure of the Sample Farmers
Farmers who are matriculated, but in secondary education, make up 23.2 per cent. of the total literate farmers. According to the different age cohort of the sample farmers as represented by table 4.9 indicates that only 2.04 per cent. It appears from table 4.10 that around 33 per cent of the total sampled farmers indicated dairying as their primary livelihood activity.
MILK PRODUCTIVITY AND THE UNDERLYING ISSUES OF MILK PRODUCTION
Farm Type and Distribution of Sample Farms
Farms of different herd size categories (small, medium and large) were post-stratified on the basis of milk SAUs using cumulative frequency square root technique. It can be seen from Table 1 that out of the total number of sample households, 92, 82 and 71 sample farmers were administered questionnaires from Barpeta, Sonitpur and Karbi Anglong district respectively. Due to lower intensity of milk production in Karbi Anglong district, the number of sample farmers (both adopters and non-adopters) selected for survey is relatively less (71 households) as compared to Barpeta and Karbi Anglong district.
Milk Productivity, Lactation and Reproduction Characteristics of Cattle and Input-Output Relation in Milk Production
- Milk Productivity of Crossbred and Indigenous Cattle
- Functional Relationship between Input and Output in Milk Production
It shows that one percent increase in the value of concentrate fed to the animal leads to 0.40 and 0.13 percent increase in the value of milk production per day per standard animal unit of crossbred and indigenous cattle respectively. A one percent increase in the value of dry feed fed to the animal leads to a 0.19 and 0.25 percent increase in the value of milk production for crossbred and indigenous cattle respectively. It is not found that the value of green fodder fed per milk SAU is statistically significant in influencing productivity of crossbred cattle and it is negatively associated with the productivity of indigenous cattle (see Table 5.4).
Economics of Dairying and Determinants of Profitability in Dairying 1. Economics of Dairying
- Factors Determining Profitability (Net returns/milch SAU) in Milk Production A multiple regression equation of the following form is fitted to identify the factors that
Based on the actual value of operating costs and returns from milk sales, the economics of milk production are presented in table 5.6. This implies that an increase of one hectare in land holding leads to a 37.86 percent increase in the profitability of dairy farming. Based on the results of the multiple regressions, it is found that an increase in the actual value of fodder and fodder of one rupee equivalent fed to the animal increases the profitability by 1.95 percent.
Crossbreeding of Cattle and Employment in Dairying
Differences in labor use between adopter and non-adoptive farmers (of crossbred cattle) by farm size and farm operations are shown in Table 5.10. However, in milk marketing there is no significant increase in the use of labor for SAU milk after the adoption of crossbreeding technology. There is also no clear pattern in the increase in labor use with increasing farm size after the adoption of crossbreeding technology.
Labour Use (Adopter)
Differences in the pattern of labor use between adopters and non-adopters did not appear to be much visible.
Labour use (Non-adopter)
Milk Marketing, Market Accessibility and Determinants of Marketed Surplus 1. Marketing of milk
- Access to market
- Determinants of marketed surplus of milk
On the other hand, distance to market is expected to have a negative effect on the marketed surplus of milk. The negative relationship between family size and marketed surplus of milk shows that higher the size of the family lower is the marketed surplus. Family size matters and it has a negative but statistically non-significant influence on the marketed surplus of milk in the present study.
Summing Up
The chapter also highlighted the available sources of milk sales for the sample farmers generating marketed surplus and their prevailing prices of milk for each source. The chapter finally focused on identifying .. market and non-market factors that determine the marketed surplus of milk. Other factors such as herd size, price received for the milk sold and membership of DCD are positive and highly significant in influencing marketed surplus.
CROSSBRED CATTLE ADOPTION AND TREATMENT IMPACT ON FARMERS’ WELLBEING
Conceptual Framework and Estimation Technique
- Conceptual Framework: Technology Adoption and Household Wellbeing
- Propensity Score Matching: Estimation Technique
Following Becerril and Abdulai (2010), Gitonga et al. 2014), the adoption decision of a new technology, such as crossbred cattle with high milk production, is modeled in an arbitrary utility framework. Another econometric method that addresses the problem of selection bias, using cross-sectional data in a non-experimental causal framework and ignoring functional or distributive form assumptions or exogeneity of covariates, is the propensity score matching (PSM) method (Gitonga et al., 2013). (Heckman et al., 1997; Jalan and Ravallion, 2001). This indicates that households with the same values of covariates have a positive probability of participating or not (Heckman et al., 1999).
Variable Choice and Estimation of Propensity Score 1. Descriptive Statistics
- Estimation of Propensity Scores
- Covariate Balancing Test
Table 6.5 again shows that there is a positive relationship between the education of the household head and the adoption of high-yielding cross-bred livestock. Farm duration which significantly influences the adoption of crossbred livestock has a negative association with the dependent variable. Membership in DCS and becoming a beneficiary of the government's dairy product development program have a stronger influence on the adoption of crossbred livestock.
Estimation of Treatment Impact
