The study location
Fig. 1. Map of Amhara region
Sampling techniques
A face-to-face/in-person/household survey has been used to acquire primary data from 117 stratified selected strata of dairy farm responsible bodies. The sample size was determined using cluster sampling procedures, and its appropriateness was confirmed. In a direct conversation, a well-developed and pre-tested survey of closed-ended questions that can only be answered by selecting from a limited number of options, usually multiple-choice questions with a single-word answer, "yes" or "no" or a rating scale like "from strongly agree to strongly disagree," was used.
Information was gathered from 15 "kebeles" (the town's smallest administrative unit), comprising individual and cooperative members, as well as university-owned dairy farms, who were nominated to participate in the interview process. In the initial stage, dairy farms stratified as farms having 3-5, 6–10, and more than 10 dairy cows were categorized as small, medium, and large dairy farms (Minten et al. 2021). In the second stage, a total of 864 licensed and legally registered dairy producers were discovered in the three milk shed zones of the Amhara region (small = 457, medium = 230, and big = 177). In the sample approach, 11% (50), 15% (35) and 18% (32) farms of small, medium, and large dairy farms were chosen and participated in the study process. The research then involved a total of 117 (13.54% of the population) from the total dairy farm owners, including 113 from individual dairy farms, 2 from cooperatives, and 2 from university research and income-generating dairy farms.
The data gathering took place from 2019 to 2020. The Statistical Package for Social Sciences (SPSS) Ver. 23 and Analysis of Moment Structures (Amos) were used to examine the data (Kline 1998). The questions used to assess dairy performance monitoring (DPM) are based on previous research, and the survey instrument must be validated. The validation is carried out using reliability analysis and the exploratory factor analysis (EFA) method, followed by the confirmatory factor analysis (CFA) process, and finally, structural equation modeling (SEM) is used to track the performance of the dairy farm using the study's outcome variables (Ravindra et al. 2019).
Construct Development and Hypotheses Formulations
According to a review of the literature, multistage monitoring procedures in the dairy sub-sectors in Ethiopia in general, and the Amhara regional state in particular, have declined significantly. There is limited empirical research on dairy monitoring efficiency and difficulties, as well as the region's dairy production system's performance. The effort demands to the development of an empirical framework to assist policymakers in their evaluation of multi-stage management strategies in dairy production system of the region, the following predictors were recruited for the analysis used to SE model. Educational and training (ED) measured from 1=Reading and Writing, to 5 Degree, health care and extension services:(HES): measured in in Likert scale, from 1= strongly disagree to 5=strongly agree, dairy farm facility (DFF):measured in Yes/No, hygienic condition and waste management (HWM): measured in in Likert scare, from 1= strongly disagree to 5=strongly agree, feed and nutrition (FN): measured in in Likert scare, from 1= strongly disagree to 5=strongly agree, mode of transportation (MT): Measured in Yes/No system, reproductive and performances (RP): measured in yes/No, gross revenue (GR): measured as a continuous variable computed daily milk production in litter multiplied by selling price per litter, but it is transformed in to a natural logarithm (ln) to make it compatible to the remaining data measurement. After the development the above constructs and based on the conceptual model presented in (Fig. 1); therefore the research has formulated the following the main hypotheses;
H1: The better the training and education level (ED, HES) of the farm owner has, the better the dairy farm management (FN, HWM, DFF, and MT), H2: The better the training and educational status (ED, HES) of a farm owner has, the better the productivity of the dairy (RP and GR), H3: if the better the dairy farm management practice has improved (FN, HWM, DFF, and MT) of a farm owner, boosted the productivity of the dairy farm performance (RP and GR), H4: The better if there is rigorous training and education (ED, HES) of a farm owner has, the better the productivity of the dairy (RP and GR) through dairy farm management (FN, HWM, DFF, and MT).
The role of predictor metrics in SEM's dairy management and their impacts
Promoting sustainable dairy development is critical for meeting the ever-increasing demand of emerging countries' rising populations (Dellmuth and Tallberg, 2015). To bridge up, this demand, yet there is lack the necessary technological, organizational, and institutional capabilities (Guadu and Abebaw, 2016). Farmers' training and education to disseminate improved dairy cattle husbandry practices is an important strategy for increasing dairying's competence and, as a result, adoption (JANETRIX 2019). Many international assistance groups and national governments urge large-scale and ongoing rigorous training for farmers in underdeveloped countries for this reason, but there has been no thorough research on whether these programs are beneficial or not (Seble et al. 2020)
A positive and highly significant relationship between dairy farming training and the adoption of higher-quality dairy husbandry practices (Banda et al. 2021). (Misganaw et al. 2016) confirmed that, training and education initiates increased yield and promotes technical efficiency. According to (Shelly M. 2020), training programs have a significant impact on the adoption of new technologies, aid in the achievement of sustainable dairy production, and, as a result, increase gross income and employment in rural areas. On the other hand, a study by emphasizes the value of training, which can help farmers improve their dairy farming skills and generate farm revenues at large (Seble et al. 2020)
However, for developing countries like Ethiopia, where livestock productivity (meat and milk) is low despite the large cattle population, the increasing human population combined with increasing demand for animal-derived food poses a serious challenge. Feed and nutrition management, both in terms of quality and quantity, is one of the most critical causes of the country's low output level (Asredie and Engdaw, 2015). In Ethiopia, the dairy sub-sector is frequently confronted with feed and nutritional constraints, which are often the most pressing issues and a source of concern in livestock development strategies (Tekeba et al. 2014). According to these sources, during the dry season, ruminants' basic diets consist of fibrous crop residues and pasture, both of which have low nutritional value, making dairy production difficult. Inadequate energy, protein, and mineral intake, on the other hand, are linked to sub-optimal dairy cow productivity and reproduction (Sharamo et al. 2021)
An average 35% deficiency in feed supply can be expected in Ethiopia even during normal years, and this figure may rise to 70% during drought years (Derara and Bekuma, 2020). This problem is likely to become more serious as a growing human population demands more land for crop production. The main reasons for dairy feed shortages in Ethiopia are therefore related to shrinking grazing lands as a result of expansion of arable cropping; the low contribution of improved forage as livestock feed (0.25%); and high prices and inaccessibility of concentrates, which further exacerbate the tight situation (Marshall, Salmon et al., 2020)
Dairy health and extension services
However, in comparison to the great national potential, the dairy sub-contribution sector's to the national economy is insufficient. The extensive incidence of a range of viral and parasite infections, which considerably reduce dairy cattle output and productivity due to sickness, mortality, and market volatility, is the primary reason for this mismatch (Gizaw et al. 2021). In Ethiopia, animal health extension services include vaccination, modern (clinical services by professionals and paraprofessionals) and traditional treatments, GIT parasite (deworming) and external parasite (spraying/dipping) controls, disease outbreak investigations and information, herd health advice, and training. Vaccination and contemporary treatments were the most often reported by extension services provider (Bugeza et al. 2017). In general, the quality of animal health care systems is determined by the accessibility, availability, and cost of veterinary services and supplies. Nonetheless, the coverage and access of dairy owners to veterinary services differed significantly across livestock systems, with access being considerably better than other extension services. The most typical problems for health extension services are the relative availability and accessibility of veterinary specialists, basic infrastructure, and other logistics (Gizaw et al. 2019). Most dairy farms' primary purpose is to enhance earnings. Many farmers are inclined to reduce feed expenses because feed accounts for up to half of all costs on a dairy farm, especially when feed prices are high. Feeding for lactation cows, on the other hand, is clearly not a frivolous expense, but rather an investment. Dairy farmers are always looking for feed sources that are less expensive but offer the same results (Baudron et al. 2014)
The high producing dairy cow requires a diet that supplies the nutrient needs for high milk production. Carbohydrates, amino acids, fatty acids, minerals, vitamins, and water are all nutrients required by the lactating dairy cow to meet the demand by the mammary gland to produce milk and milk components. However, in order to develop the cow that will produce a high milk yield, it begins with the nutrition of the calf, lactating cows and heifer (Erickson and Kalscheur, 2020). Dairy cow management interval between drying off and calving, as well as the dry phase, pre-calving period, and calving, is a period of transition. The management, nutrition, and health practices used during the transition period of a cow's lactation cycle will have a significant impact on her productivity and the farm's profitability in the following lactation. (Soberon and Van Amburgh, 2017).
Construction Validity and Reliability
The latent variables were generated after the hypothesized model displayed in (Fig. 2) was tested and validated to determine how well the model matched the observed data. Feed and nutrition (FN), dairy farm facility (DFF), education level (ED), dairy cow health and extension service (HES), dairy farm hygiene and waste management (HWM), reproductive performance (RP), and gross revenue are the latent variables in the model (GR). Each of the latent variables was measured in the model using a minimum of four and a maximum of seven observed variables. Analyzing Moment Structures was used to run and evaluate the model (AMOS-ver. 21 program). In (Figure1) it estimates were made using the maximum likelihood estimate over other estimation approaches (Kline 2011). To examine the model's validity in terms of both model fit (MF) and Construct Validity (CV), the model was evaluated using (Hair, Halle et al. 2006). In (Table 1) the model's summary results, this included the Factor loading (FL) and Standardized (SFL), the mean (Ӯ), standard deviation (SD) of each latent variable, Cronbach-alpha (α), chi-square (2), p-value, the Tucker-Lewis coefficient (TLI), the comparative fit index (CFI), and Root Mean Square Error of Approximation (RMSEA)
It is a measure of the internal consistency coefficient, or how closely related a collection of items are as a group, and it discloses the equivalence, homogeneity, and correlation of the statements to assess scale reliability. As it is presented below in (Table 1), the result showed that all the latent variables have exhibited a value 0.80 or above which is acceptable (Mor, Bhardwaj et al. 2019). However, during the test one of the latent variables, Reproductive Performance (RP), has exhibited low value (α = 0.310) and when two of the items were deleted the α value was improved to α = 0.842. Initially its Cronbach’s alpha value was low as 0.684, but after revising and one of the items was discarded items with low correlation, the reliability of the Cronbach’s become improved with the better value to α =0.801. Furthermore, for the sake of additional testing, the average variance extracted (AVE) and Construct Reliabilities (CR) were computed and given in the same table. All of the latent variables have CR values greater than the desirable ≥ 0.5 limit. Moreover, all of the latent variables have an AVE greater than the cutoff value of 0.5. Both of these tests showed that the model is well fitted to the data.
Table 1. Exploratory Factor Analysis (EFA) of dairy farm monitoring practices in SEM analysis
1
|
Measurable variables
|
Latent Variables
|
FL*
|
SFL
|
AVE
|
CR
|
Ӯ
|
SD.
|
|
water
|
<---
|
DFF
|
1.000
|
0.884
|
0.832
|
0.972
|
1.581
|
0.414
|
|
Solar
|
<---
|
DFF
|
0.866
|
0.765
|
|
ECC
|
<---
|
DFF
|
0.835
|
0.738
|
|
Toilet
|
<---
|
DFF
|
0.976
|
0.857
|
|
Iron roof
|
<---
|
DFF
|
0.910
|
0.816
|
|
Brick Wall
|
<---
|
DFF
|
0.898
|
0.793
|
|
Filed Floor
|
<---
|
DFF
|
0.887
|
0.772
|
2
|
Farm hygiene & Waste Management (HWM)
|
|
|
|
|
|
|
|
|
Frequently wash your hands during milking
|
<---
|
HWM
|
1.000
|
0.922
|
0.567
|
0.864
|
4.177
|
0.957
|
|
The cow tit is frequently washed during milking
|
<---
|
HWM
|
0.702
|
0.863
|
|
Milkers have frequent checking
|
<---
|
HWM
|
0.650
|
0.329
|
|
The milk equipment usually clean and sterilized
|
<---
|
HWM
|
0.720
|
0.473
|
|
There is proper waste avoiding system in the farm
|
<---
|
HWM
|
0.635
|
0.624
|
3
|
Feed and Nutrition (FN)
|
|
|
|
|
|
|
|
|
frequently used Green feed
|
<---
|
FN
|
1.000
|
0.799
|
0.675
|
0.865
|
4.785
|
0.958
|
|
frequently used Cope residue
|
<---
|
FN
|
0.974
|
0.962
|
|
frequently used commercially formulated feed
|
<---
|
FN
|
0.048
|
0.036
|
|
frequently feed industrial feed
|
<---
|
FN
|
0.866
|
0.842
|
4
|
Reproductive Performance (RP)
|
|
|
|
|
|
|
|
|
Heat Trained
|
<---
|
RP
|
1.000
|
0.965
|
0.73
|
0.912
|
1.888
|
0.2704
|
|
AI Record
|
<---
|
RP
|
0.991
|
0.793
|
|
AI practice
|
<---
|
RP
|
0.785
|
0.694
|
|
AI Pay
|
<---
|
RP
|
0.568
|
0.577
|
5
|
Health and Extension Service (HES)
|
|
|
|
|
|
|
|
|
The extensive services supported you
|
<---
|
HES
|
1.000
|
0.954
|
0.549
|
0.875
|
4.045
|
0.995
|
|
Frequently consulted the veter.
|
<---
|
HES
|
0.710
|
0.874
|
|
The vet clinic provide appropriate services
|
<---
|
HES
|
0.824
|
0.817
|
|
The animals are regularly vaccinated
|
<---
|
HES
|
0.702
|
0.879
|
|
The vaccine service is heaper
|
<---
|
HES
|
0.578
|
0.672
|
|
The extension service provides
|
<---
|
HES
|
0.533
|
0.541
|
6
|
Mode of Transport (MT)
|
|
|
|
|
|
|
|
|
transport by Ox Cart
|
<---
|
MT
|
1.000
|
0.843
|
0.645
|
0.872
|
1.858
|
0.806
|
|
Transport by foot
|
<---
|
MT
|
0.966
|
0.948
|
|
transport by bicycle
|
<---
|
MT
|
0.570
|
0.643
|
|
Transport by Vehicle
|
<---
|
MT
|
0.568
|
0.505
|
|
Continuous Variable
|
|
|
|
|
|
|
|
7
|
Farm owners education in years
|
|
ED
|
|
|
|
|
.953
|
1.310
|
8
|
Gross Revenue (LN Transformed)
|
|
GR
|
|
|
|
|
.367
|
7.900
|