Mechanization in land preparation and irrigation water productivity: insights from rice production

Figures & data

Figure 1. Top 12 water productivity provinces and their total water supply in China, 2019.

Table 1. Variable definitions and descriptive statistics.

Variables	Definitions	Mean	SD
Dependent variable
IWP	Rice yield per unit volume of irrigation water (kg/m^3)	0.60	0.52
MLP adoption	1 if a household used machines for rice production land preparation; 0 otherwise	0.71	0.45
Control variables
Age	Age of HH (years)	61.19	10.84
Gender	1 if the HH is male; 0 otherwise	0.75	0.43
Education	Educational level of HH (years)	6.91	3.96
Health status	1 if the HH reports him/her is in good physical condition; 0 otherwise	0.88	0.33
Household size	Number of people residing in a rural household	3.26	1.76
Elderly ratio	Ratio of the number of residents aged more than 64 years to household size	0.28	0.31
Asset ownership	1 if the rural household owns a car and/or an air purifier; 0 otherwise	0.50	0.50
Negative shock	1 if the rural household experienced negative shocks (i.e. member death and/or health deterioration); 0 otherwise	0.10	0.30
Farm size	Total area of the major plot committed to rice production (mu)^a	7.74	35.69
Soil fertility	1 if the HH perceives the cultivated land is fertile; 0 otherwise	0.48	0.50
Irrigation access	1 if the major rice plot has the access to irrigation; 0 otherwise	0.98	0.13
Negative shocks	1 if the major rice plot experienced negative shocks (e.g. flood and drought) in 2020; 0 otherwise	0.26	0.44
Pest infestation	1 if the major rice plot experienced pest infestation in 2020; 0 otherwise	0.08	0.27
Traffic condition	Distance from the major rice plot to the nearest cement road (km)	0.25	0.47
Northern Jiangsu	1 if a household is located in northern Jiangsu; 0 otherwise	0.42	0.50
Central Jiangsu	1 if a household is located in central Jiangsu; 0 otherwise	0.40	0.49
Southern Jiangsu	1 if a household is located in southern Jiangsu; 0 otherwise	0.18	0.39
City-level MLP ratio (IV)	Proportion of people using machines for rice land preparation (excluding the respondent) in the same city	0.64	0.20
Sample size		595

^a1 mu = 1/15 ha.

HH, household head; SD, standard deviation.

Table 2. Mean differences in the variables between mechanization in land preparation (MLP) adopters and non-adopters.

Variables	MLP		Mean difference	t-value
	Adopters	Non-adopters
IWP	0.615	0.564	0.051 (0.047)	1.073
Age	60.758	62.260	−1.502 (0.977)	−1.537*
Gender	0.756	0.728	0.028 (0.039)	0.703
Education	7.095	6.462	0.632 (0.357)	1.772**
Health status	0.886	0.855	0.031 (0.030)	1.038
Household size	3.358	3.029	0.329 (0.158)	2.081**
Elderly ratio	0.261	0.311	−0.050 (0.028)	−1.795**
Asset ownership	0.543	0.405	0.138 (0.045)	3.077***
Negative shock	0.088	0.133	−0.045 (0.027)	−1.666**
Farm size	8.372	6.182	2.190 (3.223)	0.680
Soil fertility	0.502	0.410	0.092 (0.045)	2.044**
Irrigation access	0.986	0.971	0.015 (0.012)	1.207
Negative shocks	0.301	0.150	0.151 (0.039)	3.860***
Pest infestation	0.104	0.017	0.087 (0.024)	3.603***
Traffic condition	0.257	0.215	0.043 (0.042)	1.009
Northern Jiangsu	0.393	0.497	−0.104 (0.044)	−2.332**
Central Jiangsu	0.453	0.260	0.192 (0.044)	4.422***
Southern Jiangsu	0.154	0.243	−0.089 (0.035)	−2.570***
City-level MLP ratio (IV)	0.678	0.550	0.128 (0.017)	7.375***
Observations	422	173

Irrigation water productivity (IWP) is measured in kg/m³; ***< 0.01, **< 0.05 and *< 0.10.

Table 3. Determinants of mechanization in land preparation (MLP) adoption and its impact on irrigation water productivity (IWP): endogenous treatment regression (ETR) model estimates.

Variables	MLP adoption	IWP
MLP adoption		0.247 (0.077)***
Age	−0.015 (0.008)*	0.001 (0.002)
Gender	0.100 (0.095)	0.021 (0.078)
Education	0.004 (0.020)	−0.003 (0.004)
Health status	0.150 (0.113)	0.007 (0.087)
Household size	0.048 (0.039)	−0.005 (0.009)
Elderly ratio	0.041 (0.259)	0.162 (0.078)**
Asset ownership	0.094 (0.101)	0.073 (0.034)**
Negative shock	−0.192 (0.067)***	−0.121 (0.061)**
Farm size	0.002 (0.002)	0.001 (0.000)*
Soil fertility	0.310 (0.148)**	−0.000 (0.036)
Irrigation access	0.510 (0.280)*	−0.016 (0.090)
Negative shocks	0.386 (0.206)*	−0.060 (0.052)
Pest infestation	0.780 (0.322)**	0.022 (0.080)
Traffic condition	−0.008 (0.089)	0.053 (0.066)
Northern Jiangsu	−0.058 (0.103)	0.188 (0.099)*
Central Jiangsu	0.376 (0.091)***	0.025 (0.095)
City-level MLP ratio (IV)	1.887 (0.310)***
Constant	−1.016 (0.422)**	0.243 (0.129)*
${\rho }_{\mathit{\epsilon \mu }}$	−0.248 (0.076)***
Variance inflation factor (VIF) test		Mean VIF = 1.35
Log-likelihood	−750.523
Wald χ^2 (d.f. = 16)	51.24, p = 0.000
Wald test of exogeneity	χ^2(1) = 9.83; Prob>χ^2 = 0.002
Observations	595	595

IWP is measured in kg/m³; city-level clustered standard errors are shown in parentheses; the reference region is the southern Jiangsu; ***< 0.01, **< 0.05 and *< 0.10.

Table 4. Disaggregated analysis: how the mechanization in land preparation (MLP) is accessed.

Category	Predicted IWP	95% Confidence interval
MLP non-adopters	0.436	[0.419, 0.452]
Via only household-owned machines	0.686	[0.659, 0.712]
Via only outsourcing machinery services	0.664	[0.652, 0.675]
Via both household-owned machines and outsourcing services	0.660	[0.616, 0.704]

Irrigation water productivity (IWP) is measured in kg/m³.

Figure 2. Impact of mechanization in land preparation (MLP) adoption on irrigation water productivity (IWP) at the selected quantiles: instrumental variable-based quantile regression (IVQR) model estimates.

Data availability statement

The data that support the findings of this study are available from Wanglin Ma upon reasonable request.