1. Introduction
Nutrients, such as nitrogen (N), phosphorus (P), potassium (K), micronutrients, and others, are essential for plant growth, food production and, ultimately, for adequate human nutrition [
1]. It has been estimated that the survival of nearly half of the world’s population depends on the use of agricultural nutrient inputs [
2], whereas lack of access to nutrients in most African countries is a primary cause of low crop yields and food shortages [
3]. Over the past 50 years, China has successfully achieved food self-sufficiency for its rapidly growing population. China is now feeding approximately 22% of the global population with only 7% of the global arable land area. This accomplishment was achieved primarily by increasing the use of chemical fertilizer nutrients, especially N and P. China is now the world’s largest producer, consumer and importer of chemical fertilizers, consuming over 1/3 of the world’s chemical fertilizers and accounting for approximately 90% of the increase in global fertilizer consumption since 1981 [
4]. However, Chinese agriculture uses far more chemical fertilizers per unit of crop production than comparable systems in Europe or North America [
3]. In 2010, Chinese agriculture consumed 28.1 Tg N as synthetic fertilizer, exceeding consumption in North America (11.1 Tg N) and the European Union (10.9 Tg N) combined [
5]. Numerous agronomic and economic studies under both experimental conditions and on farm fields provide conclusive proof that the overuse of chemical fertilizers has become widespread across China. For example, the average amount of N fertilizer used in the major rice producing regions of China is 195 kg ha
−1, which is 47% higher than the recommended rate [
6]. The oversupply of nutrients or an imbalance between nutrients reduces the efficiency of nutrient use. As a consequence, the mean N-use efficiency in crop production in China has decreased drastically from 32% in 1980 to 26% in 2005 and is much lower than the efficiency achieved in many developed countries [
7].
Nutrient losses from agriculture have resulted in serious environmental stress by increasing greenhouse gas (GHG) emissions and by polluting ground and surface water through N leaching [
8]. According to the official report from the Ministry of Environmental Protection of China in 2010, the annual loadings of N and P from the agricultural sector into the nation’s water bodies reached 2.7 and 0.3 Tg, which contributed to approximately 60% of the total N and P loads. The high rate of N fertilizer use has led to large N losses in the form of ammonia (NH
3) volatilization and N leaching into groundwater and lakes [
9]. Furthermore, the manufacture and use of N fertilizers are estimated to have contributed to approximately 30% of agricultural GHG emissions and more than 5% of China’s total GHG emission in 2007 [
10]. To address the country’s widespread water quality and other nutrient-related environmental issues (e.g., soil acidification, N deposition, and climate change), drastic improvements in nutrient management that will allow the Chinese food production industry to simultaneously feed the growing human population and decrease the environmental impacts of food production are one of the great challenges China faces in the 21st century.
In an effort to address these food security and environmental challenges related to agricultural nutrient use, China has implemented wide-ranging nutrient management practices to increase the efficiency of N and P use [
11]. However, most of these nutrient management technologies, programs, and recommendations have not been adopted by farmers. The primary reason for this problem is rooted in the lack of knowledge and information by end users, because the majority of the hundreds of millions of farmers have received limited education about the value and efficient use of plant nutrients [
12]. Hu
et al. [
13] found that, with appropriate N fertilizer application technology, N fertilizer use could be reduced by more than 30% without lowering (and potentially even increasing) rice yields. Cui
et al. [
14] found that using improved nutrient management technologies could reduce N fertilizer use by 40% without lowering maize yields, compared with current farming practices. Therefore, the timely delivery of science-based fertilizer recommendations through education, training and extension services is essential for improving nutrient use efficiency and for reducing the over-application of nutrients [
15].
However, given the importance of agricultural extension services for proper nutrient management, little empirical work has been conducted to examine this area of farm management in China. To the best of our knowledge, the only two exceptions can be found in [
10,
16]. Using data collected on the North China Plain, Huang
et al. [
10] showed that through training and scientist-guided on-farm pilot experiments, N-fertilizer use could be reduced by 22% in maize production without compromising yields. Using data from 813 maize farms, Jia
et al. [
16] found that improved N management training could significantly reduce farmer N fertilizer application by 20%.
A major drawback of the above studies is that they do not properly control for potential differences between participants and farmers in the comparison group (non-participants), making it difficult to draw definitive conclusions. To identify the impacts of agricultural extension participation, an evaluation must construct a credible counterfactual outcome; that is, a study must estimate the nutrient management behavior of participants if they had not participated in the agricultural extension programs. Failure to do this will bias the corresponding impact estimates. To fill this gap, we employ a propensity score matching (PSM) method to overcome this unobserved counterfactual problem. We use the PSM model because it can create experimental conditions in which participants and non-participants are randomly assigned, providing an unbiased estimation of the treatment effects, and it can be used to identify a causal link between agricultural extension participation and farmer nutrient management behavior. To the best of our knowledge, this is the first study to use the PSM method to evaluate the impact of agricultural extension participation on farmer nutrient management behavior.
Rice production is selected for this study for two reasons. First, rice is the number one crop in terms of the unit per area yield in China, reaching 6.777 t ha−1 in 2012, which is 1.359 and 1.155 times greater than the unit per area yield for wheat and maize. Second, as discussed above, there is suspected overuse of agricultural nutrients in rice production.
The rest of the paper is organized as follows. The next section presents an analytical framework and methodology, followed by a presentation of the data and descriptive statistics in
Section 3. The empirical results and findings are discussed in
Section 4. The last section concludes with key findings and policy implications.
4. Results and Discussion
In this section, we outline the common steps used to implement the PSM method. First, a probability model for participation in agricultural extension programs is estimated to calculate the probability (or propensity scores) of participation for each observation. In the second step, each participant is matched to a non-participant with a similar propensity score to estimate the ATT.
4.1. Factors That Affect Participation in Agricultural Extension
The factors that affect the decision to participate in agricultural extension programs are estimated using a logit model.
Table 3 presents the results. The last column of
Table 3 indicates changes in the probability of participation in agricultural extension programs given one unit of change in the explanatory variables; these are computed from the means of all of the explanatory variables. The likelihood ratio statistics of −138.024 suggested that the estimated model is statistically significant at the 1% level and that the pseudo-R
2 value indicates that the equation explains 25.39% of the variance in decision-making about whether to participate in an agricultural extension program.
Table 3.
Logit regression estimates of propensity scores for participation in agricultural extension programs.
Table 3.
Logit regression estimates of propensity scores for participation in agricultural extension programs.
Variable | Coefficient | Standard error | Marginal Probability (dy/dx) |
---|
Household characteristics |
Age of household head | −0.0262 ** | 0.0132 | −0.0116 |
Education of household head | 0.0895 *** | 0.0318 | 0.0498 |
Farming experience of household head | 0.0538 | 0.2346 | 0.0023 |
Risk attitude of household head | 0.3291 | 0.1129 | 0.0554 |
Extension contact | 0.0821 ** | 0.0321 | 0.0167 |
Village leader dummy | 0.7214 *** | 0.2211 | 0.1872 |
Household income | 1.1137 | 0.6752 | 0.2901 |
Off-farm income ratio | −1.3840 | 0.8775 | −0.3438 |
Distance to the nearest fertilizer shop | −0.2513 | 0.0667 | −0.0624 |
Farm characteristics |
Farm size | 0.4251 ** | 0.1982 | 0.8520 |
Soil quality | −0.3158 | 0.3298 | −0.0785 |
Village characteristics |
Extent of village agricultural extension participation | 0.0568 *** | 0.0081 | 0.0125 |
Village income | 1.1253 | 0.9932 | 1.0231 |
Village off-farm income ratio | 0.8782 | 0.8531 | 0.3453 |
Constant | −0.4105 | 2.8726 | - |
Log likelihood = −138.024; Pseudo R2 = 0.2539; Prob > chi2 = 0.000; Number of observations = 1250 |
The results indicates that older farmers were less likely to participate in agricultural extension programs, whereas farmers that are more educated have a higher probability of participation. As expected, farmers that have more contact with agricultural extension agents are more likely to participate in agricultural extension programs. Being a village leader and having larger farm size also increased the probability of agricultural extension participation. The higher the proportion of agricultural extension participants in a village, the more likely farmers are to participate in agricultural extension programs.
4.2. Treatment Effects of the PSM Methods
The results modeling the impact of agricultural extension participation on farmer nutrient management behavior with KBM, RM and NNM are presented in
Table 4. The three matching methods indicate that participation in agricultural extension programs has a positive impact on farmer nutrient management behavior.
The impact of agricultural extension participation on reducing fertilizer use and inorganic fertilizer use are positive and significant for all the matching algorithms. For the amount of fertilizer used, the ATT ranges from 11 to 24 kg ha−1, implying that on average participants used 11 to 24 kg ha−1 less fertilizer than matched non-participants, and/or the amount of inorganic fertilizer used ranges from 10 to 18 kg ha−1.
Agricultural extension participation also led to clear and significant improvement in organic fertilizer use and soil-testing-based fertilizer use. Farmers that participated in agricultural extension programs improved their percentage of organic fertilizer use by 1.008% to 1.705%. They also had a higher percentage of soil-testing-based fertilizer use than non-participants by an average score of 1.096% and 1.173%, respectively.
However, although agricultural extension participation has an impact on rationalizing farmer nutrient management behavior, this impact is trivial. Based on our study, participating farmers’ total fertilizer use was reduced by only 1.7% to 3.7%, and their inorganic fertilizer use is reduced by only 1.9% to 3.3%. The improved percentage of organic fertilizer use and soil-testing-based fertilizer use due to agricultural extension participation are also small, ranging from 1.008% to 1.173%. The reasons for this are as follows: first, there are many complex barriers to effective knowledge and technology transfer to farmers in China. Most of the more than 200 million farmers in China are poorly educated, are relatively old, and operate very small holdings (an average 0.1–0.5 ha of agricultural land per farm) [
11]; second, China has lacked a wide-reaching and functional extension system. According to one report, there were only 11 technicians providing services for 20,000 farmers in one county; at the township level, the extension personnel, if any, have become fertilizer salesmen or have become engaged in other unrelated activities (e.g., family planning) [
15]; third, the extension system in China generally takes a top-down approach, determining what technologies should be transferred at the central, provincial or county level without the sufficient involvement of local farmers [
13,
36]; fourth, increasing agricultural production and food security have been the primary objectives of the agricultural extension system. Extension officers usually only promote programs intended to increase crop yields, as do most governmental incentives [
37]. However, since the end of the 2000s, government policies have broadened to include, not only food security, but also environmental sustainability. For example, in 2005 the Ministry of Agriculture began a soil- and plant-testing program called the National Soil-Testing and Fertilizer-Recommendation Program (STFR). By 2009, more than 2500 counties were involved and had received 1.5 billion Yuan of financial support from the central government to establish soil-testing laboratories and demonstrate the use of soil-testing and fertilizer recommendations for a diverse range of cropping systems. However, agricultural bureaus lack the knowledge, trained staff, and instruments (e.g., taxes and subsidies, regulatory authority, extension services, education and demonstration, and pollution standards) to implement such a policy with the concurrent goals of environmental sustainability and food security [
11].
Table 4.
Estimates of the average treatment effect on treated (ATT).
Table 4.
Estimates of the average treatment effect on treated (ATT).
Outcome variable | Matching algorithm | Treated | Controls | ATT | T-stat |
---|
The total amount of fertilizer used (kg ha−1) | Kernel-based matching | 648 | 672 | −24 | −1.897 * |
Radius caliper matching | 648 | 665 | −17 | −2.012 ** |
Nearest neighbor matching | 648 | 659 | −11 | −2.134 ** |
The total amount of inorganic fertilizer used (kg ha−1) | Kernel-based matching | 539 | 557 | −18 | −1.954 * |
Radius caliper matching | 539 | 549 | −10 | −1.764 * |
Nearest neighbor matching | 537 | 553 | −16 | 2.894 *** |
The percentage of organic fertilizer used (%) | Kernel-based matching | 10.494 | 9.355 | 1.139 | 1.765 * |
Radius caliper matching | 10.494 | 8.789 | 1.705 | 1.974 ** |
Nearest neighbor matching | 10.329 | 9.321 | 1.008 | 2.023 ** |
The percentage of soil-testing-based fertilizer used (%) | Kernel-based matching | 6.327 | 5.231 | 1.096 | 2.248 ** |
Radius caliper matching | 6.327 | 5.218 | 1.109 | 1.836 * |
Nearest neighbor matching | 6.327 | 5.154 | 1.173 | 2.113 ** |
To gain further understanding of the impact of agricultural extension participation on different groups of participants, we also examined the differential impact of participation by dividing households into different categories based on education level, risk attitude, initial application level and farm size. The stratification was made based on matched samples obtained from the nearest neighbor-matching estimator. (Results are reported in
Table 5,
Table 6,
Table 7 and
Table 8.)
As observed in
Table 5, the impact of participation on total fertilizer use and inorganic fertilizer use decrease with educational level, while the relationship between participation and organic fertilizer use and soil-testing-based fertilizer use are positive. This is consistent with the expectation that better educated farmers are more adept at acquiring and processing information from various sources, and then adopting and implementing recommendations and solutions relevant to their specific problems [
38].
Table 5.
Differential impact by education level.
Table 5.
Differential impact by education level.
Category | Outcome variable | ATT | T-stat |
---|
Low (0–6 years) | The total amount of fertilizer used (kg ha−1) | −11.21 | −1.978 ** |
The total amount of inorganic fertilizer used (kg ha−1) | −9.87 | −2.321 ** |
The percentage of organic fertilizer used (%) | 1.012 | 1.856 * |
The percentage of soil-testing-based fertilizer used (%) | 0.098 | 1.985 ** |
Middle (6–9 years) | The total amount of fertilizer used (kg ha−1) | −13.45 | −1.765 * |
The total amount of inorganic fertilizer used (kg ha−1) | −10.34 | −1.995 ** |
The percentage of organic fertilizer used (%) | 1.213 | 2.012 ** |
The percentage of soil-testing-based fertilizer used (%) | 1.011 | 1.764 * |
High (more than 9 years) | The total amount of fertilizer used (kg ha−1) | −15.76 | −2.679 *** |
The total amount of inorganic fertilizer used (kg ha−1) | −13.25 | −2.114 ** |
The percentage of organic fertilizer used (%) | 1.432 | 2.789 *** |
The percentage of soil-testing-based fertilizer used (%) | 1.163 | 2.065 ** |
Table 6 presents results for the causal impacts of participation on nutrient management behavior for different categories of risk attitude. The results generally reveal that the participation of agricultural extension exerts a positive and statistically significant impact on nutrient management behavior among the risk-loving farmers and risk-neutrality farmers, but insignificant effects on the risk-aversion farmers. It may be that risk aversion leads farmers to want to avoid the possibility of applying too little fertilizer, and are less concerned about applying too much fertilizer. Given that farmers in China, like rural households in many developing countries, have limited access to formal insurance and credit markets, they are generally risk-averse and more risk aversion can lead to more intensive fertilizer use, providing crop insurance would be a beneficiary policy to help alleviate farmers’ fertilizer use.
Table 6.
Differential impact by risk attitude.
Table 6.
Differential impact by risk attitude.
Category | Outcome variable | ATT | T-stat |
---|
Risk aversion | The total amount of fertilizer used (kg ha−1) | −9.34 | −1.045 |
The total amount of inorganic fertilizer used (kg ha−1) | −8.47 | −1.326 |
The percentage of organic fertilizer used (%) | 0.078 | 1.543 |
The percentage of soil-testing-based fertilizer used (%) | 0.094 | 1.456 |
Risk neutrality | The total amount of fertilizer used (kg ha−1) | −12.64 | −2.065 ** |
The total amount of inorganic fertilizer used (kg ha−1) | −11.78 | −1.978 ** |
The percentage of organic fertilizer used (%) | 1.117 | 2.114 ** |
The percentage of soil-testing-based fertilizer used (%) | 1.014 | 2.064 ** |
Risk loving | The total amount of fertilizer used (kg ha−1) | −16.56 | −2.896 *** |
The total amount of inorganic fertilizer used (kg ha−1) | −13.43 | −2.015 ** |
The percentage of organic fertilizer used (%) | 1.332 | 1.986 ** |
The percentage of soil-testing-based fertilizer used (%) | 1.432 | 2.234 ** |
The relationship between participation and initial application level are shown in
Table 7. The results generally reveal that within the different initial application level groups, the impacts of participation on nutrient management behavior are all very trivial. The reason for this result may be that farmers in China had been overusing fertilizer in the past and they are becoming too used to relying on chemical fertilizer. As a result, farmers become locked into “unsustainable” agricultural systems once fertilizers are adopted. As Tisdell [
39] demonstrates, when chemical agricultural systems are adopted, agricultural yields or returns become dependent on them despite the very high costs, and thus impose an “economic barrier” to switching to organic systems. In short, agricultural practices tend to become “inclined towards” such systems once they are adopted despite being unsustainable.
Table 7.
Differential impact by initial application level.
Table 7.
Differential impact by initial application level.
Category | Outcome variable | ATT | T-stat |
---|
Low | The total amount of fertilizer used (kg ha−1) | −7.38 | −1.972 ** |
The total amount of inorganic fertilizer used (kg ha−1) | −5.34 | −1.718 * |
The percentage of organic fertilizer used (%) | 0.076 | 2.002 ** |
The percentage of soil-testing-based fertilizer used (%) | 0.087 | 1.684 * |
Middle | The total amount of fertilizer used (kg ha−1) | −7.35 | −2.124 ** |
The total amount of inorganic fertilizer used (kg ha−1) | −6.93 | −1.765 * |
The percentage of organic fertilizer used (%) | 0.098 | 2.321 ** |
The percentage of soil-testing-based fertilizer used (%) | 1.012 | 1.804 * |
High | The total amount of fertilizer used (kg ha−1) | −10.23 | −1.865 * |
The total amount of inorganic fertilizer used (kg ha−1) | −7.45 | −2.327 ** |
The percentage of organic fertilizer used (%) | 0.096 | 1.911 * |
The percentage of soil-testing-based fertilizer used (%) | 1.112 | 2.132 ** |
Results from the causal impacts of participation on nutrient management behavior for different categories of farm size are presented in
Table 8. It is significant to note that agricultural extension participation exerts a positive and statistically significant impact on nutrient management behavior among the medium and large farmers, but insignificant effects on the small-scale farmers. This result is consistent with Zhou
et al. [
40], who found an inverse relationship between farm size and fertilizer intensity in a study in Hebei Province, indicating that smaller farms are more likely to have high intensities. The reason for the insignificant effects of the small-scale farmers may be that farmers with less farm land will find it more difficult to spread the risks across family plots and, thus, could possibly use fertilizer more intensively to stabilize the crop yields.
Table 8.
Differential impact by farm size.
Table 8.
Differential impact by farm size.
Category | Outcome variable | ATT | T-stat |
---|
Small | The total amount of fertilizer used (kg ha−1) | −10.21 | −1.214 |
The total amount of inorganic fertilizer used (kg ha−1) | −9.32 | −1.431 |
The percentage of organic fertilizer used (%) | 0.092 | 1.614 |
The percentage of soil-testing-based fertilizer used (%) | 0.086 | 1.542 |
Medium | The total amount of fertilizer used (kg ha−1) | −12.56 | −2.124 ** |
The total amount of inorganic fertilizer used (kg ha−1) | −11.45 | −2.247 ** |
The percentage of organic fertilizer used (%) | 1.034 | 1.986 ** |
The percentage of soil-testing-based fertilizer used (%) | 1.112 | 2.578 *** |
Large | The total amount of fertilizer used (kg ha−1) | −15.98 | −2.797 *** |
The total amount of inorganic fertilizer used (kg ha−1) | −14.43 | −2.015 ** |
The percentage of organic fertilizer used (%) | 1.332 | 2.028 ** |
The percentage of soil-testing-based fertilizer used (%) | 1.213 | 2.456 ** |
4.3. Assessing the Quality of the Matching Process
The matching process is checked to determine whether it balances the distribution of the relevant covariates in both the treatment and control groups using different methods. The results of the covariate-balancing tests are presented in
Table 9 and
Table 10.
First, the propensity score test indicates a significant reduction in bias after matching, and most importantly, there are no significant differences in matched non-participants and participants for any of the covariates (
Table 9).
Table 9.
Tests for selection bias after matching.
Table 9.
Tests for selection bias after matching.
Variable | Matched sample | Bias | T-testp-value |
---|
Treated (N = 396) | Control (N = 854) | % Bias | % Bias reduction |
---|
Household characteristics |
Age of household head | 46.21 | 49.98 | −36.35 | 46.21 | 0.521 |
Education of household head | 2.89 | 2.56 | 38.79 | 5.71% | 0.358 |
Farming experience of household head | 3.57 | 3.54 | 3.50 | 25.00% | 0.172 |
Risk attitude of household head | 1.49 | 1.44 | 6.32 | 54.55% | 0.616 |
Extension contact | 3.25 | 2.65 | 20.66 | 77.62% | 0.216 |
Village leader dummy | 0.22 | 0.13 | 33.57 | 3.01% | 0.238 |
Household income | 10.83 | 10.78 | 5.74 | −38.78% | 0.228 |
Off-farm income ratio | 55.98 | 56.04 | −20.48 | 14.97% | 0.172 |
Distance to the nearest fertilizer shop | 2.97 | 4.86 | −9.80 | 36.03% | 0.425 |
Farm characteristics |
Farm size | 0.34 | 0.31 | 15.86 | 22.81% | 0.106 |
Soil quality | 2.18 | 2.14 | 1.62 | 85.19% | 0.273 |
Village characteristics |
Extent of village agricultural extension participation | 36.32 | 26.61 | 54.55 | 3.48% | 0.345 |
Village income | 10.64 | 10.56 | 2.99 | 27.27% | 0.772 |
Village off-farm income ratio | 39.87 | 38.95 | 6.34 | 59.47% | 0.298 |
Second, there is a substantial reduction in bias as a consequence of matching. The estimates indicate that the standardized mean bias before matching is 28.71%, whereas the standardized mean bias after matching is reduced to between 6.79% and 13.65%. The percentage reductions in the absolute bias are 65.62%, 76.35% and 52.46% with KBM, RM and NNM matching methods, respectively. Because the percentage reduction in bias by all three matching methods is greater than 20%, a value recommended by Rosenbaum and Rubin [
32] as a sufficiently large enough reduction in standardized bias, it is determined that the matching substantially reduced the selection bias. Similarly, the pseudo-R
2 of the estimated logit model was high before matching and low afterwards for all matching algorithms. The
p-value of the likelihood ratio test was always rejected after matching, whereas it was never rejected at any significance level before matching, suggesting that there is no systematic difference in the distribution of covariates between participants and non-participants after matching (
Table 10).
Table 10.
Statistical tests to evaluate the matching.
Table 10.
Statistical tests to evaluate the matching.
Matching algorithm | Mean bias | % |bias| reduction | Pseudo-R2 | p-value of LR |
---|
Before matching | After matching | Unmatched | Matched | Unmatched | Matched |
---|
Kernel-based matching | 28.71 | 9.87 | 65.62 | 0.2539 | 0.0923 | 0.000 | 0.432 |
Radius caliper matching | 28.71 | 6.79 | 76.35 | 0.2539 | 0.0897 | 0.000 | 0.654 |
Nearest neighbor matching | 28.71 | 13.65 | 52.46 | 0.2539 | 0.1125 | 0.000 | 0.786 |
4.4. Testing for Hidden Bias with Sensitivity Analysis
Might endogeneity drive our results? As noted above, the effectiveness of our matching estimators in controlling for selection bias are dependent on the untestable identifying assumption that we are able to observe confounding variables that simultaneously affect farmers’ decisions to participate in agricultural extension programs and to adopt or not to adopt the nutrient management practices that serve as our outcome variables. That is, we essentially assume that endogeneity is not a problem [
17]. We calculate Rosenbaum bounds to check the sensitivity of our results with the failure of this assumption. Given that the sensitivity analysis of insignificant effects is not meaningful, the Rosenbaum bounds were calculated only for the treatment effects that are significantly different from zero [
41]. As Duvendack and Palmer-Jones [
42], and DiPrete and Gangl [
43] noted, if the critical value is less than two, one may assert that the likelihood of such unobserved characteristic is relatively high; therefore, the estimated impact is rather sensitive to the existence of unobservables. As shown in
Table 11, in our results, the lowest critical value of
γ is 2.08, whereas the largest critical value of
γ is 4.59. Therefore, our sensitivity tests suggest that even large amounts of unobserved heterogeneity would not alter the inference of the estimated effects. In other words, endogeneity is unlikely to drive our results.
Table 11.
Sensitivity analysis with Rosenbaum bounds.
Table 11.
Sensitivity analysis with Rosenbaum bounds.
Matching algorithm | Outcome Variable | Critical level of hidden bias(
γ) |
---|
Kernel-based matching | The total amount of fertilizer used (kg ha−1) | 2.08–2.12 |
The total amount of inorganic fertilizer used (kg ha−1) | 3.15–3.23 |
The percentage of organic fertilizer used (%) | 2.65–2.72 |
The percentage of soil-testing-based fertilizer used (%) | 2.78–2.86 |
Radius caliper matching | The total amount of fertilizer used (kg ha−1) | 2.21–2.32 |
The total amount of inorganic fertilizer used (kg ha−1) | 3.04–3.11 |
The percentage of organic fertilizer used (%) | 2.26–2.34 |
The percentage of soil-testing-based fertilizer used (%) | 3.21–3.56 |
Nearest neighbor matching | The total amount of fertilizer used (kg ha−1) | 2.48–2.65 |
The total amount of inorganic fertilizer used (kg ha−1) | 4.32–4.59 |
The percentage of organic fertilizer used (%) | 3.15–3.26 |
The percentage of soil-testing-based fertilizer used (%) | 2.48–2.52 |
5. Conclusions
Agricultural nutrients play a critical role in food production and human nutrition and health in China. However, the oversupply of nutrients has resulted in serious environmental problems. Managing agricultural nutrients to provide a safe and secure food supply while protecting the environment remains one of the great challenges in 21st-century China. Providing knowledge and information through agricultural extension services to farmers is essential for nutrient management. Therefore, this study examined participation in agricultural extension programs on farmer nutrient management behavior based on a nearly nationally representative household survey in seven provinces of rural China. Given the non-experimental nature of the data used in the analysis, the causal impact of agricultural extension participation is estimated by utilizing a PSM method. This helps in estimating the true effect of agricultural extension participation by controlling for the role of selection bias problems.
Three main conclusions can be drawn from the results of this study. First, the group of farmers that participated in agricultural extension programs has systematically different characteristics than the group of farmers that did not participate. These differences represent sources of variation between the two groups that the estimation of an OLS model, including a dummy variable for participation, cannot take into account. Second, the empirical results from the PSM analysis show that agricultural extension participation has a positive impact on rationalizing farmer nutrient management behavior; however, the impact is trivial. Compared with non-participating farmers, the reduced ratio of total fertilizer use and inorganic fertilizer use by participating farmers are only 1.7% to 3.7%, and the improved ratio of organic fertilizer use and soil-testing-based fertilizer use are only 1.008% to 1.173%. Third, we found interesting results from differential impacts of participation, based on education level, risk attitude, initial application level and farm size. The causal impacts of participation on nutrient management behavior tend to be higher for more educated, risk-loving and larger-scale farmers.
This study has important policy implications. First, for agricultural extension to have a long-term and more significant impact on farmer nutrient management behavior, more training efforts or other methods, such as the participatory approach to farming education during the entire crop season, are needed. However, how to implement improved nutrient management practices on hundreds of millions of Chinese farms through extension is a major challenge to the agricultural extension system. The development of more effective methods for delivering information to farmers is essential. Working through Farmer Professional Associations and using Farmer Field Schools are obvious steps forward. Meanwhile, given that the causal impacts of participation on nutrient management behavior are higher for more educated, risk-loving and larger-scale farmers, targeting these farmers with agricultural extension programs will have great demonstration effects on other farmers. Second, a shift in the focus of national policies from merely food security to an integrated approach that emphasizes food security, the efficient use of resources, and environmentally sound production and consumption are highly desirable. Third, governmental support of agriculture should be redirected. We recommend abandoning indirect fertilizer subsidies and increasing direct support to farmers who adopt environmentally friendly nutrient management practices.
While this study has made significant advancements in knowledge about the impact of agricultural extension participation on farmer nutrient management behavior, it nevertheless has its limitations. That is, the effect of agricultural extension on the change in farmer nutrient management behavior may be seen a long time after the extension program. However, due to the lack of panel data, we can only rely on a cross-sectional data set to evaluate this impact, which may bias the estimated results of this study.