Role of agricultural extension in learning for uptake and intensification of less-practiced dairy climate-smart practices in Kenya

Figures & data

Table 1. Description of the dairy climate-smart practices.

Practice	Description and measure
Fodder Diversification (FD)	Cultivate and use diverse high-yield fodder; 1 = yes, 0 = no
Drought Tolerant (DT)	Cultivate and use fodder crops that can withstand drought; 1 = yes, 0 = no
Leguminous Fodder (LF)	Production and use of high nitrogen fixing and high protein content leguminous fodder; 1 = yes, 0 = no
Treating Crop Residues (TCR)	Treating crop residues before feeding them to cows to enhance digestibility, unlock nutrients and improve nutrient content; 1 = yes, 0 = no
High Energy Concentrates (HEC)	Feeding milking cows with high energy concentrate; 1 = yes, 0 = no
Multi-nutrient Blocks (MB)	Feeding milking cows with nutrient-fortified blocks/feeds; 1 = yes, 0 = no
Total Mixed Rations (TMR)	Preparing dairy feeds at home that are nutrient-balanced while incorporating locally available materials; 1 = yes, 0 = no
Hay (HAY)	Baling harvested and cured fodder crops to preserve them for use during a shortage; 1 = yes, 0 = no
Silage (SIL)	Ensiling fodder crops to preserve them for use during a shortage; 1 = yes, 0 = no
Biogas (BIO)	Using cow dung to prepare biogas for household use and application of slurry to fodder crops; 1 = yes, 0 = no
Covering Manure (CM)	Heaping or putting manure in a covered manure pit or heap; 1 = yes, 0 = no
Composting (COM)	Composting manure and using it for fodder production; 1 = yes, 0 = no
Disease Prevention (DP)	Reduce disease burden through prevention techniques, including vaccination and farm biosecurity measures; 1 = yes, 0 = no
Disease Control (DC)	Manage diseases and parasites through timely treatment and appropriate use of animal drugs, and chemicals; 1 = yes, 0 = no
Artificial Insemination (AI)	Use of AI to get high-yielding breeds; 1 = yes, 0 = no
Adaptable Breeds (AB)	Rearing breeds adaptable to climatic conditions and farm characteristics; 1 = yes, 0 = no
Culling (CUL)	Replacing less productive animals; 1 = yes, 0 = no

Table 2. Adoption level of dairy climate-smart practices (Primary data, 2022).

Dairy climate-smart practice	Mean	Std. dev
Fodder Diversification (FD)	0.42	0.49
Drought Tolerant (DT)	0.17^∗	0.38
Leguminous Fodder (LF)	0.09^∗	0.29
High Energy Concentrates (HEC)	0.58	0.49
Multi-nutrient Blocks (MB)	0.09^∗	0.29
Treating Crop Residues (TCR)	0.08^∗	0.27
Total Mixed Rations (TMR)	0.02^∗	0.15
Hay (HAY)	0.31	0.46
Silage (SIL)	0.19^∗	0.40
Biogas (BIO)	0.03^∗	0.16
Covering Manure (CM)	0.15^∗	0.35
Composting (COM)	0.26^∗	0.44
Disease Prevention (DP)	0.55	0.50
Disease Control (DC)	0.94	0.23
Artificial Insemination (AI)	0.60	0.49
Adaptable Breeds (AB)	0.25^∗	0.44
Culling (CUL)	0.05^∗	0.21

^{∗ ∗}Represents the mean values of the DCSPs adopted by less than 30% of the respondents.

Table 3. Description of the less-adopted dairy climate-smart practices.

Practice	Description and measure
Drought Tolerant (DT)	Cultivate and use fodder crops that can withstand drought; 1 = yes, 0 = no
Leguminous Fodder (LF)	Production and use of high nitrogen fixing and high protein content leguminous fodder; 1 = yes, 0 = no
Treating Crop Residues (TCR)	Treating crop residues before feeding them to cows to enhance digestibility, unlock nutrients and improve nutrient content; 1 = yes, 0 = no
Multi-nutrient Blocks (MB)	Feeding milking cows with nutrient-fortified blocks/feeds; 1 = yes, 0 = no
Total Mixed Rations (TMR)	Preparing dairy feeds at home that are nutrient-balanced while incorporating locally available materials; 1 = yes, 0 = no
Silage (SIL)	Ensiling fodder crops to preserve them for use during a shortage; 1 = yes, 0 = no
Biogas (BIO)	Using cow dung to prepare biogas for household use and application of slurry to fodder crops; 1 = yes, 0 = no
Covering Manure (CM)	Heaping or putting manure in a covered manure pit or heap; 1 = yes, 0 = no
Composting (COM)	Composting manure and using it for fodder production; 1 = yes, 0 = no
Adaptable Breeds (AB)	Rearing breeds adaptable to climatic conditions and farm characteristics; 1 = yes, 0 = no
Culling (CUL)	Replacing less productive animals; 1 = yes, 0 = no

Table 4. Description and measurement of explanatory variables.

Variable	Variable description and measure	Expected sign
		Learning	Adoption	Intensity
Resilience	If a farmer perceived DCSPs to enhance resilience of dairy production; 1 = yes, 0 = no		+	+
Record	If a farmer keeps dairy records; 1 = yes, 0 = no		+	+
Climate change knowledge	Level of knowledge about climate change measured as the aggregate scores of knowledge about climate change causes, effects, and extreme events divided by the total sum of scores; proportion		+	+
Extension visits	Number of visits by an extension agent; continuous		+	+
Risk attitude	Given that a farmer can afford insurance premiums, what is their insurance response to a disease anticipated in the next two years with the potential to result in the death of all their dairy cows (Risk averse – insure, and risk loving – not insure); 1 = risk averse, 0 = risk loving		+
Ease of DCSPs extension	If a farmer perceived that it was easy to access DCSPs extension services; 1 = yes, 0 = no	+
Household size	Number of members in a household; continuous	+	+	+
Sex	Sex of the household head; 1 = male, 0 = female	+ −	+ −	+ −
Education	Education level of the household head as measured by the years of schooling; continuous	−	+	+
Experience	Experience in dairy farming measured as years in dairy farming; continuous	+	+	+
Agricultural group	If a farmer belongs to an agricultural group; 1 = yes, 0 = no	+	+	+
Herd size	Measured as the tropical livestock unit; continuous	+	+	+
Log on-farm income	Log household on-farm income measured as the log of aggregate on-farm income from all the enterprises practiced on the farm; continuous	+	+	+
Milk market participation	Proportion of milk produced that is sold: proportion	+	+	+
Primary occupation	If farming is the primary occupation of the household; 1 = yes, 0 = no	+	+	+
Wakulima Mukurueini Dairy Limited (WL)	Farmer being from WL milk shed; 1 = yes, 0 = no	+ −	+ −	+ −
Happy Cow Limited (HC)	Farmer being from HC milk shed; 1 = yes, 0 = no	+ −	+ −	+ −
New Kenya Cooperative Creameries-Sotik (NKCCS)	Farmer being from NKCCS milk shed; 1 = yes, 0 = no	+ −	+ −	+ −

Table 5. Descriptive statistics of adopters and non-adopters of dairy climate-smart agriculture practices (Primary data, 2022).

Variable	Non-adopters (A)	Adopters (B)	Difference (A–B)	t-Value	Significance
DCSP enhance resilience	0.80 (0.40)	0.89 (0.31)	−0.09	−2.98	***
Keeping dairy records	0.25 (0.44)	0.48 (0.50)	−0.22	−5.26	***
Knowledge of climate change	0.62 (0.19)	0.69 (0.17)	−0.07	−4.42	***
Number of extension visits	2.52 (0.46)	4.57 (0.51)	−2.04	−2.29	**
Risk attitude	0.60 (0.49)	0.69 (0.46)	−0.10	−2.20	**
Ease of accessing DCSP’s extension	0.61 (0.04)	0.69 (0.02)	−0.09	−2.05	**
Household size	4.50 (0.15)	4.50 (0.09)	−0.01	−0.05
Sex	0.82 (0.03)	0.83 (0.02)	−0.01	−0.22
Education	10.00 (3.63)	10.56 (4.10)	−0.60	−1.73	**
Experience	15.91 (12.34)	18.51 (13.39)	−2.60	−2.26	**
Agricultural group	0.74 (0.03)	0.84 (0.02)	−0.10	−2.80	***
Herd size	3.82 (2.56)	4.06 (2.76)	−0.25	−1.03
Log on-farm income	11.75 (0.87)	11.89 (0.96)	−0.14	0.02	**
Milk market participation	0.48 (0.03)	0.54 (0.02)	−0.06	−1.97	**
Primary occupation	0.76 (0.03)	0.74 (0.02)	0.02	0.56
Wakulima Mukurueini Dairy Limited (WL)	0.38 (0.49)	0.40 (0.49)	−0.02	−0.45
Happy Cow Limited (HC)	0.32 (0.47)	0.28 (0.45)	0.04	1.05
New Kenya Cooperative Creameries-Sotik (NKCCS)	0.30 (0.46)	0.32 (0.47)	−0.02	−0.55

Notes: ** and *** indicate significance at 5% and 1% level respectively; figures in parentheses are robust standard errors.

Table 6. Marginal effect estimates of the triple hurdle model for the determinants of learning, adoption and adoption intensity of DCSPs (Primary data, 2022).

	Learning	Adoption	Intensity
	dy/dx (Probit)	dy/dx (Probit)	dy/dx (Tobit)
DCSP enhance resilience		0.210 (0.199)	0.090 (0.023)***
Keeping dairy records		0.593 (0.168)***	0.063 (0.031)**
Knowledge of climate change		1.465 (0.618)**	0.300 (0.097)***
Number of extension visits		0.047 (0.025)*	0.007 (0.003)**
Risk attitude		0.396 (0.157)**
Ease of accessing DCSP’s extension services	0.123 (0.049)**
Household size	−0.060(0.036)*	−0.012 (0.047)	0.015 (0.006)***
Sex	0.022(0.191)	0.034 (0.208)	−0.008 (0.031)
Education	−0.023(0.019)	−0.002 (0.023)	0.009 (0.003)***
Experience	−0.013(0.005)**	0.024 (0.007)***	0.003 (0.001)***
Agricultural group	−0.036(0.176)	0.272 (0.193)	−0.026 (0.042)
Herd size	0.025(0.029)	−0.005 (0.039)	0.015 (0.006)**
Log on-farm income	−0.168(0.081)**	0.003 (0.092)	−0.002 (0.014)
Milk market participation	00.422(0.230)*	−0.129 (0.270)	0.064 (0.038)*
Primary occupation	0.000(0.163)	−0.122 (0.203)	−0.060 (0.025)**
Wakulima Mukurueini Dairy Limited milkshed	−0.084(0.177)	0.167 (0.214)	0.083 (0.032)**
Happy Cow Limited milkshed	0.505(0.190)***	−0.150 (0.209)	−0.028 (0.027)
Inverse mills ratio		−0.640 (1.177)	0.340 (0.156)**
Constant	3.012(0.876)***	−1.322 (1.015)	−0.549 (0.201)***
Number of observations	471	328	310
lnsig_3	−1.723(0.104)***
Rho_12	−0.712 (0.978)
Rho_13	0.026 (0.071)
Rho_23	−0.347 (0.176)**
Wald chi2(44)	150.17
Prob > chi2	0.000

Notes: *, **, *** indicate significance at 10%, 5% and 1% level respectively; figures in parentheses are robust standard errors.

Data availability statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.