Farmers’ perceptions, adoption and impacts of integrated water management technology under changing climate

Farmers’ perceptions, adoption and impacts of integrated water management technology under changing climate

Hongyun Zheng ^a, Wanglin Ma ^b, David Boansi^c and Victor Owusu ^c

aCollege of Economics & Management, Huazhong Agricultural University, Wuhan, China; ^bDepartment of Global Value Chains and Trade, Faculty of Agribusiness and Commerce, Lincoln University, Christchurch, New Zealand; ^cDepartment of Agricultural Economics, Agribusiness and Extension, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana

ABSTRACT

This study investigates the correlations between Chinese banana farmers’ perceptions of integrated water management technology (IWMT) and their adoption behaviours and examines the impact of IWMT adoption on farm performance. The results revealed that farmers’ IWMT adoption correlates significantly with their percep- tions of whether IWMT adoption can help reduce farm workload, fertilizer and water, the extent to which the technology is easier to operate, and the extent to which the technology can generate higher economic benefits than furrow irrigation technology. IWMT adoption significantly increases banana yields, gross revenue, net returns and irrigation frequency, but does not significantly affect irrigation expenditure.

ARTICLE HISTORY Received 6 February 2023 Accepted 19 March 2023 KEYWORDS

Climate change; food security; integrated water management technology;

banana farmers; China

Introduction

Although the debate on the occurrence of climate change is still inconclusive, increasing events, such as extreme temperatures, frequent droughts and vagaries of precipitation patterns, are evident and inevitable (Chen & Chen, 2018; Kuwayama et al., 2019).

Unexpected and consistent heat waves between June and August 2022 have influenced many European and Asian countries. The highest temperature records in some countries, including the UK and the Netherlands, have been broken. These changes have also endangered the water supply and hydrological cycles of the non-agricultural and agri- cultural sectors. The agricultural sector uses the largest amount of water, accounting for approximately 69% of global water withdrawal (United Nations [UN], 2021). This is significantly higher than water usage in other sectors. Water usage for industrial activities (e.g. energy and power generation) represents only 19% of global water withdrawals, and the remaining 12% is used by municipalities (UN, 2021). In addition, the agricultural sector is more vulnerable and responsive to water supply variability than other sectors because of its overreliance on water for cultivating crops and feeding livestock (Pourzand et al.,

CONTACT Wanglin Ma Wanglin.Ma@lincoln.ac.nz https://doi.org/10.1080/07900627.2023.2196351

© 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any med- ium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

2020; Tong et al., 2022). Water is a unique commodity with no viable substitution. The frequent occurrence of climate change events has amplified the adverse effects of water shortages on global food security from various aspects, such as lowering land productiv- ity, reducing food supply and decreasing farm incomes.

Prior evidence has revealed that water scarcity induced by climate change deteriorates crop yields (Arunrat et al., 2022; Chen & Chen, 2018; Kang et al., 2009; Kuwayama et al., 2019; Senapati et al., 2019). For example, Kuwayama et al. (2019) found that droughts negatively influenced crop yields and farm income in the United States. Specifically, an additional week of drought in dryland counties would reduce 0.1–1.2% of yields of corn and soybean. The situation is not different in China, as rice yields are estimated to decrease by 10–19% by 2050 due to global warming (Chen & Chen, 2018).

In response to global climate change, farmers worldwide have adopted various water management strategies to adapt to the changing climate, tackle water-related hazards and ensure agricultural productivity. These include the adoption of water-saving irrigation systems (Çetin & Kara, 2019; Martínez et al., 2022; Tang et al., 2016), shifting crops to cropland (Salazar-Espinoza et al., 2015), and adopting drought-tolerant varieties (Martey et al., 2020; Simtowe et al., 2019; Zheng et al., 2021). For example, Zheng et al. (2021) showed that adopting drought-tolerant wheat varieties is a practical approach for Chinese wheat farmers to tackle climate change because it increases wheat yield and farm profits.

Moreover, water management can be integrated with the management of other resources (e.g. land, fertilizer and other inputs), referring to integrated water management technologies (IWMT). IWMT, including fertigation (Sidhu et al., 2019; Yang et al., 2020), soil and water conservation practices (Bhatta et al., 2022; Wolka et al., 2018), and bund construction (Abdulai & Huffman, 2014), could improve resource-use efficiency by creat- ing synergies between water and other inputs. Input integration provides IWMTs with substantial advantages over single-purpose technologies in reducing production loss induced by climate change and adapting to water-related challenges.

Previous studies have investigated the nature of IWMT (Bhatta et al., 2022; Rehman et al., 2022; Sampson & Perry, 2019; Sun et al., 2022; Tang et al., 2016), as well as how IWMT influences household well-being and farm performance (Fontes, 2020; Kumar et al., 2020;

Tesfaye et al., 2016; Yang et al., 2020). Tang et al. (2016) showed that awareness of water scarcity and financial status increases farmers’ adoption of farm-based irrigation water- saving techniques in China. Sampson and Perry (2019) analysed peer effects on adopting low-energy precise applications in the United States and found that neighbouring instal- lations significantly increase the probability of adoption. Bhatta et al. (2022) found that off-farm income, access to credit and agricultural micro-irrigation infrastructure are important determinants of Nepali farm households’ decisions to adopt water conserva- tion practices. Regarding the impacts of IWMT adoption, Wolka et al. (2018) found that soil and water conservation technology is economically feasible for improving crop yields and reducing labour opportunity costs in Sub-Saharan Africa. Using plot-level data from Ethiopia, Fontes (2020) observed that adopting soil and water conservation technologies increased adult labour use by 35%.

Previous studies have mainly focused on farmers’ characteristics (e.g. age and gender) and contextual factors (e.g. infrastructure construction and market access) to investigate farmers’ IWMT adoption (Bhatta et al., 2022; Martínez et al., 2022;

Sampson & Perry, 2019), without paying much attention to the role of farmers’

perceptions of IWMT in their adoption decisions. In addition, IWMT adoption not only influences farm outputs (e.g. crop yields and revenue) but also affects farmers’

irrigation behaviours, such as how many times they irrigate and the corresponding irrigation expenditure. However, evidence of the relationship between IWMT adoption and irrigation frequency and expenditure is lacking. These gaps were addressed in the present study.

This study aims to advance our understanding of the nexus between smallholder farmers’ perceptions, adoption, and impact of IWMT. We aim to answer two signifi- cant research questions: (1) How are farmers’ IWMT adoption decisions correlated with their perceptions of the technology? (2) What are the effects of IWMT adoption on farm performance? This study focuses on fertigation in banana production in China as an IWMT. Fertigation synchronizes water and fertilizer nutrients by adding liquid fertilizers to the irrigation systems (Sandhu et al., 2019). Experimental and empirical studies from Canada and China have proven that fertigation is an effective IWMT that supports sustainable agri-food production and mitigates the negative impacts of climate change on smallholder farming systems (Chai et al., 2020; Yang et al., 2020; Yuan et al., 2021). For example, Yang et al. (2020) found that a drip fertigation system significantly improved the technical efficiency of cherry tomato production in China. However, IWMT is still not widely adopted by farmers because they usually lack the relevant knowledge to better understand this innovative technology (Rehman et al., 2022). Thus, a better understanding of the relationship between farmers’ perceptions, adoption, and impacts of IWMT under a changing climate is essential for designing targeted interventions, programs, and policy instru- ments that motivate farmers to adopt technology for efficient water management and agricultural production.

Our study makes three significant contributions to the literature. First, this study investigates the correlations between farmers’ perceptions of IWMT and their adoption decisions using Pearson correlation coefficient analysis. We capture farmers’ percep- tions of IWMT adoption from eight dimensions: workload reduction, fertilizer reduc- tion, water reduction, water scarcity, technology similarity, operability, economic benefit and others’ attitudes. This allows us to comprehensively understand how farmers’ perceptions of new technology correlate with their actual adoption decisions (Sun et al., 2022). Second, we consider farm performance indicators from both the output and irrigation perspectives. The output indicators include banana yields, gross revenue and net returns, whereas irrigation indicators include irrigation frequency and expenditure. Comprehensive measurements of farm performance indicators help to deepen our understanding of the effects of IWMT adoption. Third, we employ the propensity score matching (PSM) method to address self-selection bias. By doing this, we acknowledge that farmers’ decisions to adopt IWMT are non-random and are influenced by many observed confounding factors (e.g. age, gender, family size and soil quality).

The remainder of this paper is organized as follows. The next section provides the conceptual framework and empirical model. The third section describes the data collec- tion and summary statistics. The fourth section presents and discusses the results. The fifth section concludes the study with policy implications.

Conceptual framework and empirical model Conceptual framework

Figure 1 shows the pathways influencing farmers’ perceptions, adoption and impact of IWMT.

The influencing pathways consist of three channels. The first channel reveals that farmers’

decisions regarding IWMT adoption tend to correlate with their perceptions of technology.

The IWMT allows farmers to irrigate their farmland and apply fertilizer simultaneously, redu- cing the need to apply them separately. Once farmers perceive the farm workload reduction benefits of the IWMT, they may choose to adopt it. Because IWMT improves input use efficiency, it might help reduce water and fertilizer use, contributing to production cost savings. In turn, farmers’ direct perceptions of adopting IWMT help them understand the differences and similarities between the operations and expected benefits from IWMT and traditional irrigation methods such as furrow irrigation. In addition, since farmers make joint and spatial decisions on advanced technology adoption because of peer effects (Martínez et al., 2022; Sampson & Perry, 2019; Zheng et al., 2021), their IWMT adoption decisions are likely to be influenced by peers’ attitudes toward the new technology.

The second channel shows that various exogenous factors could also influence farmers’

IWMT adoption, including farmer characteristics (e.g. age, education, gender and cadre experience), household and farm characteristics (e.g. family size, land size, drought perception and soil type), and location-specific characteristics (e.g. distance to market) (Bhatta et al., 2022; Rehman et al., 2022; Tang et al., 2016; Yang et al., 2020). For example, older farmers may hesitate to adopt the technology because they are more likely to be risk-averse. Farmers cultivating large farms may be more likely to adopt the IWMT to achieve economies of scale and production specialization. In addition, farmers whose land is far from input markets may have fewer incentives to adopt IWMT because of high transaction costs, especially transportation costs (Zheng et al., 2021).

Farmer characteristics Farmer characteristics (e.g., age, education, gender (e.g., age, education, gender

and cadre experience) and cadre experience) Farm and household Farm and household

characteristics characteristics (e.g., family size, land size, (e.g., family size, land size, drought perception and soil drought perception and soil

type) type) Location-specific Location-specific characteristics characteristics (e.g., distance to market and (e.g., distance to market and distance to credit sources) distance to credit sources)

IWMT IWMT adoption adoption

Flexible Flexible irrigation and irrigation and fertilization plan fertilization plan

Improving Improving water efficiency water efficiency Accelerating Accelerating fertilizer uptake fertilizer uptake

Net returns Net returns Banana Banana yield yield Gross Gross revenue revenue Frequency Frequency Expenditure Expenditure Irrigation Irrigation Farmers’ perceptions of IWMT

Farmers’ perceptions of IWMT

Farm Farm outputs outputs

Figure 1. Influencing pathways of perceptions, adoption and impacts of integrated water manage- ment technology (IWMT).

The third channel demonstrates how IWMT adoption influences farmers’ irrigation investments and farm output. First, IWMT adoption optimizes water transportation and application, which increases water utilization efficiency. Fertigation reduces water waste through pipeline transport and applies water to plant roots (Jayakumar et al., 2017). The unique water transmission and application in fertigation practice contribute immensely to improving water utilization efficiency, which has been widely proven by field experiments (Chatzimichael et al., 2019; Luo & Li, 2018). In comparison, traditional furrow irrigation systems are often associated with irrigation water evaporation during soil transmission.

Second, IWMT adoption helps reduce fertilizer and nutrition loss and accelerates fertilizer uptake during crop production. Fertigation enables the efficient utilization and precise application of fertilizers and nutrients based on soil conditions. Notably, fertilizer is applied to soils with wetted volumes, thus improving the fertilizer utilization efficiency. For example, in a field experiment conducted in India, Sidhu et al. (2019) reported that subsurface drip fertigation saves about 20% nitrogen fertilizer in rice and wheat production. Third, IWMT adoption enables farmers to design flexible plans, ensuring efficient and effective irrigation and fertilization. Fertigation allows for timely water supply in different cultivation stages and frequent fertilizer supply to avoid extremely high or low nutrient concentrations in the soil (Sandhu et al., 2019). Increased flexibility in irrigation and fertilization frequency helps farmers avoid unexpected climate shocks, such as extreme droughts (Rehman et al., 2022).

In the present study, we employed Pearson correlation coefficient (PCC) analysis to explore the correlations between farmers’ perceptions of IWMT and their adoption decisions. In addition, we used a PSM model to examine the impact of IWMT adoption on farm outputs, irrigation frequency and expenditure. The following section provides a detailed discussion of the PSM model.

Empirical model

When analysing the effects of IWMT adoption on farm performance, an endogeneity problem should be addressed. Farmers self-select themselves to adopt IWMT, which leads to a selection bias that needs to be corrected. Previous studies relied on several strategies to address the self-selection problem. These include the PSM method (Ma et al., 2022a), inverse probability weighted regression adjustment (IPWRA) estimator (Chigusiwa et al., 2022; Zheng & Ma, 2021), two-stage residual inclusion (2SRI) approach (Ma et al., 2022b;

Zhang et al., 2022; Zheng et al., 2023), and endogenous switching regression (ESR) model (Ahmed & Mesfin, 2017; Liu et al., 2021). Among these, the effective estimations of the 2SRI and ESR models rely on valid instrumental variables. A valid instrumental variable should only affect IWMT adoption, not farm output and irrigation outcome variables.

Failure to identify valid instrumental variables generates biased estimates (Bowden et al., 2016). Nevertheless, it is always challenging to find valid instrumental variables, especially in this study, where we analysed five outcome variables.

Because we could not simultaneously find valid instrumental variables that meet the exclusion restriction requirements for the five outcome variables, we employed the non- parametric PSM approach in the empirical analysis. In addition, we estimate the IPWRA model as a robustness check. The advantage of the PSM and IPWRA models is that apart from controlling for observable characteristics, they are nonparametric approaches that do not require an identification restriction during the estimation process (Chesterman

et al., 2019; Donkor & Owusu, 2019; Yuan et al., 2021). The basic idea of PSM is to match the observations of IWMT adopters and non-adopters based on the predicted propensi- ties to adopt a superior technology (Asfaw et al., 2012; Wooldridge, 2002).

Using PSM to estimate the treatment effects of IWMT adoption on the relevant outcome variables in this study proceeds in two steps: In the first step, we use the logit model to generate propensity scores. Using matched observations of IWMT adopters and non- adopters, the average treatment effect on the treated (ATT) on the relevant outcome variable is calculated in the second step. With this approach, the propensity score, p Xð Þ, defined as the conditional probability of receiving treatment given pre-treated characteristics ð Þ, is esti-X mated as:

p Xð Þ ¼Pr½IWMT¼1jX� ¼E IWMT½ jX�;p Xð Þ ¼F h Xf ð Þi g (1) where Ff g� can be a normal or logistic commutative distribution (the latter in the present study); and X is a vector of pretreatment characteristics (e.g. age and education of household heads, family size, land size and soil type).

The PSM estimator of the ATT shows the difference between the outcomes of the treated (IWMT adopters) and counterparts (if they have not adopted). Using the estimated propensity scores, we then compute the average treatment effects (ATT) of IWMT adop- tion on the outcome variables as:

ATT ¼E Hf 1i H0ijIWMTi¼1g

¼E Hf 1ijIWMTi¼1;p Xð Þi g E Hf 1ijIWMTi ¼0;p Xð ÞjIWMTi i¼1g (2) where H1i and H0i are the potential outcomes of IWMT adoption and counterparts, respectively, IWMTi denotes the treatment variable, and Xi denotes the pre-treatment characteristics or observable covariates.

Estimating propensity scores requires a particular matching method. Different matching techniques, including nearest-neighbour matching, stratification matching, radius matching and kernel-based matching, have been applied in the literature (Chesterman et al., 2019;

Kumar et al., 2020). In this study, we apply nearest-neighbour and kernel-based matching methods to match the samples and estimate the treatment effects of IWMT adoption. The nearest neighbour matching technique can pick a comparison group by matching each adopter with its closest neighbour with similar observed characteristics, which can be done either with replacement or without replacement. The kernel-based matching algorithm can use more non-adopters for each IWMT adopter and reduce the variance, even though, in some cases, the bias is also increased. In addition, we undertake matching quality before and after matching to ensure that the balancing property is satisfied (Ma et al., 2022a; Mojo et al., 2017) and the Rosenbaum bound to conduct a sensitivity analysis (Rosenbaum, 2014; Schmidt et al., 2020).

Data collection and summary statistics Data collection

We obtained data for this study through a household survey in rural areas of three Chinese provinces (Hainan, Yunnan and Guangdong) from July to October 2019.

These are the three major banana-producing provinces in China. Notably, in 2020,

the banana outputs in Hainan, Yunnan and Guangdong provinces were 1.13, 1.98, and 4.79 million tons, respectively, accounting for 69% of the national banana production in China (NBS, 2021). However, agricultural production in these three survey provinces has been challenged by frequent natural disasters such as droughts, floods, geological disasters and typhoons, and banana production is no exception. For example, the areas affected by drought, floods, geological disasters and typhoons in Guangdong, Hainan and Yunnan provinces were 40,300, 1,024,200, and 84,000 ha, respectively, which comprise more than 98% of the total area affected by farm crops in 2020 (NBS, 2021). Because banana production relies on sufficient water supply, it is severely affected by natural disasters. Improved water management patterns, such as the IWMT, have great potential to enhance the sustainability of banana production in the context of climate change. Thus, it is valuable to descriptively and empirically explore these associations to enrich our understanding.

A stratified sampling approach was used to select respondents. Stratified sampling has substantial advantages over other probability sampling methods because it can ensure sample diversity and lower the overall variance in the population. In addition, it suits our study, as we interviewed respondents face to face through door-to-door visits, facilitating us to obtain a representative sample from the local banana farmer populations.

In the first stage, we purposively selected Hainan, Yunnan and Guangdong provinces because of their significant banana production. The second stage involved randomly selecting three to five countries in each province. The third and fourth stages involved randomly selecting two towns in each county and one to two villages in each town.

Around 10–20 rural households that cultivated bananas during the 2018/19 cropping season were chosen in each village. The final sample included 626 households after excluding observations with missing values for the key variables.

We designed a structured questionnaire to interview rural respondents in person.

Farmers were required to report detailed information on the inputs (e.g. expenditure on fertilizers and irrigation) and output (e.g. banana yields and market price) of banana production. The collected information allowed us to construct farm output variables (banana yields, gross revenue, and net returns) and irrigation variables (irrigation fre- quency and expenditure) to serve as dependent variables in our analysis. Net returns are the difference between gross revenue and investment costs, measured at yuan/mu. The investment costs comprise expenditures on seedlings, fertilizers, pesticides, machinery, irrigation and hired labour.

In the water management module of our questionnaire, we asked farmers whether they had adopted IWMT (i.e., fertigation) for banana cultivation. We used the collected information to create a binary variable that reflected banana farmers’ IWMT adoption status (1 for IWMT adopters and 0 for non-adopters).

A series of questions was also prepared to collect information on banana farmers’

perceptions of the IWMT. The questionnaire also gathered information on farmers’

characteristics (e.g. age and gender of household heads), household characteristics (e.g. family size), farm characteristics (e.g. soil type and land size), and location- specific characteristics (e.g. distance to market, credit sources and geographical location dummies).

Summary statistics

Table 1 presents the summary statistics of the selected variables. The upper part shows that farmers obtained 31,800 kg/mu (1 mu = 1/15 ha) of banana on average. The gross revenue and net returns of banana production were 71,000 and 39,300 yuan/mu, respectively. Farmers, on average, irrigated bananas 34 times and spent 530 yuan/mu on irrigation. Approximately 52%

of farmers have adopted IWMT during banana production. The lower part of Table 1 presents summary statistics of the control variables. On average, the sampled household heads were 48 years old and had received 8.08 years of education. Approximately 83% of the respondents were male, and 23% were or had been village cadres. About six members lived in the family. On average, farmers cultivated 29.33 mu (1 mu = 1/15 hectares) of land for banana production. The average distance to the input market and credit sources was 5.72 and 5.89 km, respectively.

Table 2 presents the mean differences in the variables between IWMT adopters and non-adopters. On average, IWMT adopters obtained higher banana yields, gross revenue, and net returns than non-adopters. However, only the mean differences in banana yield and gross revenue were statistically significant. In addition, IWMT adopters irrigated bananas more frequently than non-adopters. Interestingly, the difference in irrigation expenditure between IWMT adopters and non-adopters is not statistically significant, implying that IWMT may not be relatively expensive compared with traditional irrigation technologies. Nevertheless, these observations cannot be used to speculate on the impact of IWMT adoption on farm performance because simple mean comparisons do Table 1. Variable definition and summary statistics.

Variables Definitions Mean (SD)

Outcome variables

Banana yields Yields of banana production (1000 kg/mu)^a 3.18 (2.83)

Gross revenue Gross revenue of banana production (1000 yuan/mu)^b 7.10 (7.21) Net returns Gross revenue minus investment costs of banana production (1000 yuan/

mu)

3.93 (7.24) Irrigation frequency Frequency of irrigation during banana production (×100) 0.34 (0.45)

Irrigation expenditure Expenditure on irrigation (100 yuan/mu) 0.53 (1.40)

Treatment variable

IWMT adoption 1 = Adopters of integrated water management technology (IWMT);

0 = otherwise

0.52 (0.50) Control variables

Age Age of household head (years) 48.20 (9.90)

Gender 1 = Male; 0 = otherwise 0.83 (0.37)

Education Educational level (years) 8.08 (3.12)

Cadre experience 1 = Is or has been a village cadre; 0 = otherwise 0.23 (0.42)

Family size Number of family members (persons) 5.98 (2.37)

Land size Total orchard size for banana production (mu) 29.33 (72.93)

Drought perception Perceived drought frequency compared with five years ago:

1 = More frequent; 2 = Almost; 3 = Less frequent

2.10 (0.61)

Clay soil 1 = Orchard mainly has clay soil; 0 = otherwise 0.50 (0.50)

Loam soil 1 = Orchard mainly has loam soil; 0 = otherwise 0.29 (0.45)

Sandy soil 1 = Orchard mainly has sandy soil; 0 = otherwise 0.21 (0.41)

Distance to market Distance to the nearest input market (km) 5.72 (8.46)

Distance to credit sources

Distance to the nearest formal (e.g. bank and financial institutions) and informal (e.g. relatives or financial groups) credit source (km)

5.89 (8.33)

Hainan 1 = Located in Hainan province; 0 = otherwise 0.37 (0.48)

Yunnan 1 = Located in Yunnan province; 0 = otherwise 0.29 (0.46)

Guangdong 1 = Located in Guangdong province; 0 = otherwise 0.34 (0.47)

Observations 626

Note: ^a1 mu = 1/15 ha.

bYuan is Chinese currency (US$1 = 6.90 yuan in 2019).

SD, standard deviation.

not account for the observed differences. Regarding the control variables examined in this study, we found significant differences in the age and gender of the household heads, drought perception, soil type, distance to the market, credit sources, and geographical dummies between IWMT adopters and non-adopters. These observed differences high- light the importance of accounting for control variables when estimating the effects of IWMT adoption on irrigation investment and farm performance.

Results and discussions

Perceptions of IWMT and their correlations with adoption decisions

Table A1 in Appendix A presents the definitions of IWMT perception variables and their summary statistics. Briefly, 79% of farmers perceived that IWMT adoption reduced the farm workload associated with irrigation and fertilization applications. Approximately 68% of farmers perceived that IWMT adoption could reduce fertilizer use, and 55%

perceived that IWMT adoption could reduce water use. To improve our understanding, we further asked farmers’ perceptions of how much fertilizer and water can be saved when using fertigation as an IWMT in banana production. The results in Figure 2 show that the majority of farmers (over 80%) perceived that IWMT adoption reduced fertilizers by less than 40% (Figure 2a) and water by a similar level (Figure 2b).

The mean values of the variables representing technology similarity, operability, economic benefits and others’ attitudes were 2.63, 3.54, 3.75 and 3.70, respectively (see Table A1 in Appendix A). We further provide an intuitive understanding in Figure 3, where the distributions of the answers related to the four variables are graphically

Table 2. Mean differences of variables between integrated water management technology (IWMT) adopters and non-adopters.

Variables IWMT adopters Non-adopters Mean differences

Outcome variables

Banana yields 3.60 (3.34) 2.73 (2.06) 0.87***

Gross revenue 7.66 (6.78) 6.49 (7.60) 1.17**

Net returns 4.19 (6.96) 3.64 (7.53) 0.55

Irrigation frequency 0.49 (0.53) 0.18 (0.24) 0.31***

Irrigation expenditure 0.48 (1.23) 0.57 (1.57) −0.08

Control variables

Age 47.36 (10.12) 49.10 (9.59) −1.74**

Gender 0.81 (0.39) 0.86 (0.35) −0.05*

Education 8.15 (3.16) 8.01 (3.08) 0.14

Cadre experience 0.22 (0.41) 0.24 (0.43) −0.02

Family size 6.07 (2.43) 5.87 (2.29) 0.20

Land size 30.42 (75.11) 28.16 (70.63) 2.26

Drought perception 2.15 (0.69) 2.04 (0.50) 0.12**

Clay soil 0.60 (0.49) 0.40 (0.49) 0.20***

Loam soil 0.20 (0.41) 0.37 (0.49) −0.17***

Sandy soil 0.20 (0.40) 0.23 (0.42) −0.03

Distance to market 3.41 (3.21) 8.17 (11.19) −4.77***

Distance to credit sources 4.51 (4.22) 7.37 (10.97) −2.86***

Hainan 0.51 (0.50) 0.21 (0.41) 0.31***

Yunnan 0.10 (0.29) 0.51 (0.50) −0.42***

Guangdong 0.39 (0.49) 0.28 (0.45) 0.11***

Observations 323 303

Note: ***p < 0.01, **p < 0.05 and *p < 0.10.

Standard deviation (SD) is presented in parentheses.

demonstrated. Figure 3(a) depicts the answers to the question ‘To what extent do you perceive there exists a difference between IWMT and furrow irrigation technology?’

corresponding to the technology-similarity variable. It shows that only 0.64% of farmers perceived that the IWMT and furrow irrigation technologies were the same, and around 47% of them perceived that those two irrigation technologies were somewhat different or totally different. Figure 3(b) illustrates the answers to the question ‘To what extent do you perceive that IWMT is easier to operate in practice than furrow irrigation?’ which corre- sponds to the operability variable. This shows that only 3.19% of the farmers perceived IWMT to be very difficult to operate. Figure 3(c) answers the question ‘To what extent do you perceive that IWMT adoption generates higher economic benefits than furrow irrigation technology?’ corresponds to the economic benefit variable. We found that around 68.85% of farmers perceived that IWMT adoption could generate higher (56.07%) or much higher (12.78%) economic benefits than furrow irrigation technology.

Finally, Figure 3(d) answers the question ‘To what extent do you perceive that other farmers’ attitudes towards IWMT adoption are supportive?’ which corresponds to the attitude variable of the others. Around 68% of farmers reported that other farmers’

attitudes toward IWMT adoption are supportive (37.22%) or very supportive (20.45%), indicating that farmers widely recognize IWMT adoption.

Table 3 presents the results of the PCC analysis, reporting the correlations between the eight selected perception variables and farmers’ decisions to adopt IWMT. At least four interesting findings were identified. First, IWMT adoption was positively correlated with workload reduction, fertilizer reduction, and water reduction. The findings suggest that farmers adopt IWMT because of their perceived advantages. Second, the perceived operability and economic benefits of IWMT are also positively correlated with a higher probability of adopting IWMT. This indicates that farmers are more likely to adopt IWMT if the technology is easy to operate and could bring economic benefits (e.g. less fertilizer input or higher benefits), regardless of whether they have experienced water scarcity.

Third, others’ attitudes toward IWMT are also positively associated with farmers’ IWMT

(a) Perceptions of fertilizer-saving levels (b) Perceptions of water-saving levels 43.87

38.44

16.75

0.71 0.24 0

10 20 30 40 50

% of respondents

44.96 36.31

16.43

2.31 0

10 20 30 40 50

% of respondents

Figure 2. Farmers’ perceptions of fertilizer and water-saving levels through integrated water manage- ment technology (IWMT) adoption: (a) perceptions of fertilizer-saving levels; and (b) perceptions of water-saving levels.

(a) Technology similarity (b) Operability

s e d u t i t t a

’ s r e h t O ) d ( t

i f e n e b c i m o n o c E ) c ( 13.74

33.23 29.55

22.84

0.64 0

10 20 30 40

% of respondents

3.19 15.5

22.68 41.69

16.93

0 10 20 30 40 50

% of respondents

2.08 2.88 26.2

56.07

12.78

0 10 20 30 40 50 60

% of respondents

1.44 5.11

35.78 37.22

20.45

0 10 20 30 40

% of respondents

Figure 3. Farmers’ perceptions of the integrated water management technology (IWMT) adoption with respect to technology similarity, operability, economic benefit and others’ attitudes: (a) technol- ogy similarity; (b) operability; (c) economic benefit; and (d) others’ attitudes.

Table 3. Pearson correlation coefficients between percep- tions variables and integrated water management tech- nology (IWMT) adoption.

Variables IWMT adoption p-value

Workload reduction 0.243*** 0.000

Fertilizer reduction 0.255*** 0.000

Water reduction 0.103** 0.010

Water scarcity 0.014 0.727

Technology similarity 0.029 0.465

Operability 0.514*** 0.000

Economic benefit 0.250*** 0.000

Others’ attitude 0.589*** 0.000

Note: ***p < 0.01 and **p < 0.05.

adoption. This can be partially explained by ‘peer effects theory’ (Zheng et al., 2021).

Finally, farmers’ experiences of water scarcity and perceptions of technology similarity are positively associated with their IWMT adoption decisions, but the correlation coefficients are not statistically significant.

Although the findings in this section help us understand why some farmers choose to adopt and others do not adopt IWMT in banana production in China, they provide only a narrow descriptive picture. Notably, it must be pointed out that the perception variables may be influenced by unobserved factors (e.g. farmers’ motivations and innate abilities), making them potentially endogenous to some extent. For this reason, we only employed the PCC method to verify how these perception variables are linked to banana farmers’

decisions on IWMT adoption.

Empirical results

Matching quality check and sensitivity analysis

We checked the validity of the PSM estimates based on the common support provided by the estimated propensity score. Good matching quality requires suffi- cient overlap in the characteristics of IWMT adopters and non-adopters (Bachke, 2019). Figure A1 in Appendix A shows the distributions and common support results, revealing a satisfactory overlap between the scores of the treated and control groups. These findings satisfy the common support hypothesis. In addition, a covariate balancing test was performed to assess the matching quality of the nearest-neighbour matching and kernel matching algorithms. The results (see Table A2 in Appendix A) show that the mean bias reduced from 31.7 before matching to approximately 5.1–6.1 after matching. The results show that the p-value of the likelihood ratio (LR) is statistically significant before matching but becomes insignif- icant after matching. The covariance balance test was based on the nearest neighbours with one to three NNM (i.e., nearest-neighbour matching). The results (see Table A3 in Appendix A) show that the bias for almost all covariates (except family size) is reduced to an insignificant level, suggesting a strong data balance after matching (Zhang et al., 2020). These findings verify a high degree of similarity in the covariates between IWMT adopters and non-adopters after matching, confirm- ing the good matching quality.

We performed a sensitivity analysis using Rosenbaum bounds to determine the critical value at which one may contest the significance of the causal inference of the impact of IWMT adoption on the outcome variables. The reported gamma values ranging between 1 and 2 (see Table A4 in Appendix A) suggest that the estimated treatment effects on the five outcome variables are free from hidden bias and cannot be changed, even by large unobserved heterogeneity (Fentie & Beyene, 2019; Melesse & Bulte, 2015). These findings suggest that the matching techniques used in this study are satisfactory.

Treatment effects of IWMT adoption

The treatment effects of IWMT adoption on outcome indicators are presented in Table 4. The results reveal that IWMT adoption tends to increase banana yields by 19.38–20.78%, gross revenue by 22.43–23.54% and net returns by 33.56–33.60%.

Fertigation enhances banana yields through a plausible increase in water supply at

critical stages of growth, finally increasing fertilizer use efficiency and water produc- tivity. An increment in yields at a reduced volume/quantity of fertilizer and water indirectly implies a potential increase in gross revenue but a decrease in cost, thereby leading to increased net returns. The observed positive impact of the technology on banana yields is in line with documented evidence in China (Li et al., 2019, 2021; Liao et al., 2019; Lv et al., 2019). For example, in a study by Li et al. (2021) using Chinese data, drip fertigation increased crop yields by 12%, water productivity by 26.4% and nitrogen use efficiency by 34.3%. However, the impact of the practice varied among crops, with the highest yield impact recorded for potatoes (40.3%) and the lowest for wheat (6%).

In addition, the results in Table 4 reveal that IWMT adoption motivates banana farmers to irrigate orchards more frequently. Our estimates also show that IWMT adoption significantly increases irrigation frequency by 80.74–86.26%. However, we find that IWMT adoption does not significantly impact irrigation expenditures. Overall, our findings suggest that IWMT adoption motivates banana farmers to irrigate more, but their expen- diture on irrigation is relatively low.

Robustness check

As a robustness check, we also estimated the treatment effects of IWMT adoption using an IPWRA estimator. Similar to the PSM model, the IPWRA estimator is a nonparametric approach that addresses observed selection bias (Nikam et al., 2022; Zheng & Ma, 2021).

The empirical results (see Table A5 in Appendix A) show that IWMT adoption significantly increased banana yields by 19.57%, gross revenue by 24.84%, net returns by 5.47% and irrigation frequency by 77.26%. The estimated treatment effects of the IPWRA estimator are close to those estimated by the PSM model presented in Table 4. These findings verify the robustness of the PSM results.

Table 4. Average treatment effects of integrated water management technology (IWMT) adoption on outcome variables: propensity score matching (PSM) estimator.

Mean outcomes

Outcome variables Actual Counterfactual ATT % Change

Nearest-neighbour matching (NNM) (1–3 matching)

Banana yields 3.604 2.984 0.620 (0.252)** 20.78

Gross revenue 7.648 6.247 1.401 (0.524)*** 22.43

Net returns 4.195 3.141 1.054 (0.563)* 33.56

Irrigation frequency 0.489 0.262 0.226 (0.045)*** 86.26

Irrigation expenditure 0.490 0.365 0.126 (0.182) 34.52

Kernel matching

Banana yields 3.604 3.019 0.585 (0.218)*** 19.38

Gross revenue 7.648 6.190 1.457 (0.522)*** 23.54

Net returns 4.195 3.140 1.055 (0.523)** 33.60

Irrigation frequency 0.489 0.270 0.218 (0.041)*** 80.74

Irrigation expenditure 0.490 0.412 0.078 (0.187) 18.93

Note: ***p < 0.01, **p < 0.05 and *p < 0.10.

ATT, average treatment effects on the treated.

100 bootstrapped standard errors are presented in parentheses.

Heterogeneous analysis

Farmers’ decisions to adopt IWMT may be affected by their personal characteristics (e.g. age and gender) and household resource endowments (e.g. available labour force and land size). For example, males usually take more responsibility for support- ing household livelihoods; thus, they are more likely to adopt improved technologies to improve farm performance and maximize farm income. Meanwhile, off-farm parti- cipation is also preferred by males as it brings considerable off-farm income. While rural females also endeavour to adopt income-increasing technologies, they have fewer off-farm work opportunities and need to take on more intra-household respon- sibilities, such as cooking and looking after children and elders. Furthermore, the treatment effects of IWMT adoption for small-scale farmers may not be cost-effective, as farm size limits the achievement of economies of scale and scope. Given this, we conduct a heterogeneous analysis by gender of the household head and land size.

Specifically, the samples were split by the sex of the household head and the three tertiles of land size. For simplicity, we only employ NNM (1–3 matching) to generate the results. Table 5 presents the results.

Table 5. Average treatment effects by gender of household head and land size: nearest-neighbour matching (NNM) (1–3 matching).

Mean outcomes

Groups Actual Counterfactual ATT % Change

By gender of household head

Male Banana yields 3.319 3.054 0.265 (0.215) 8.68

Gross revenue 7.288 6.055 1.233 (0.563)** 20.36

Net returns 3.862 3.111 0.751 (0.701) 24.14

Irrigation frequency 0.483 0.310 0.177 (0.041)*** 57.10

Irrigation expenditure 0.522 0.394 0.128 (0.192) 32.49

Female Banana yields 4.744 3.097 1.645 (0.873)* 53.12

Gross revenue 9.065 5.809 3.256 (1.744)* 56.05

Net returns 5.523 2.309 3.214 (1.855)* 139.19

Irrigation frequency 0.517 0.290 0.227 (0.122)* 78.28

Irrigation expenditure 0.368 0.536 −0.168 (0.434) −31.34

By land size Tertile 1

(0.5–5 mu)

Banana yields 4.396 3.170 1.226 (0.493)** 38.68

Gross revenue 8.389 5.886 2.504 (1.291)* 42.54

Net returns 5.004 2.673 2.331 (1.591) 87.21

Irrigation frequency 0.462 0.207 0.255 (0.067)*** 123.19

Irrigation expenditure 0.543 0.034 0.509 (0.157)*** 1497.06

Tertile 2 (5.5–15 mu)

Banana yields 3.335 3.021 0.315(0.314) 10.43

Gross revenue 6.956 5.714 1.242 (0.788) 21.74

Net returns 3.668 2.968 0.701 (0.926) 23.62

Irrigation frequency 0.552 0.320 0.232 (0.072)*** 72.50

Irrigation expenditure 0.443 0.902 −0.459 (0.534) −50.89

Tertile 3 (16–1000 mu)

Banana yields 3.087 2.338 0.749 (0.306)** 32.04

Gross revenue 7.992 5.962 2.029 (0.961)** 34.03

Net returns 4.286 2.484 1.802 (1.281) 72.54

Irrigation frequency 0.452 0.304 0.149 (0.153) 49.01

Irrigation expenditure 0.437 0.145 0.022 (0.591) 15.17

Note: ***p < 0.01, **p < 0.05 and *p < 0.10.

ATT, average treatment effects on the treated.

100 bootstrapped standard errors are presented in parentheses.

The treatment effects of IWMT adoption vary across the genders of household heads.

IWMT adoption significantly increases gross revenue and irrigation frequency for house- holds headed by males or females. IWMT adoption also significantly increases banana yields and net returns for households headed by females but not for those headed by males. The treatment effects on irrigation expenditure are insignificant for both male-headed and female-headed households, and the findings align with the results in Table 4. Apropos land size, we find heterogeneous effects among households with different land sizes. IWMT adoption significantly increases banana yields, gross revenue, irrigation frequency, and expenditure for small-sized households at tertile 1 (0.5–5 mu). In comparison, it only significantly increased the irrigation frequency for medium-sized households in tertile 2 (5.5–15 mu). For those in tertile 3 (16–1000 mu), IWMT adoption helped them obtain a higher level of banana yields and gross revenue.

Conclusions and policy implications Conclusions

Promoting the adoption of water management technologies that improve farm input use efficiency effectively reduces water usage. However, water is not the only natural resource and should not be considered in isolation. Therefore, it is imperative to integrate water management with other resources (e.g. land or fertilizers), which would effectively ensure the resilience of cropping systems to mitigate the multiple risks arising from climate change. As a representative IWMT, fertigation (which involves the incorporation of fertilizer within irrigation water by a drip system) has the potential to ensure the timely availability of sufficient fertilizer and water to plants. By this, the practice could increase water productivity and fertilizer use efficiency and address the water scarcity issues associated with changing climate.

Using fertigation in China’s banana production as a case of IWMT, this study used the PCC analysis to investigate the nexus between banana farmers’ perceptions of IWMT and their adoption behaviours. We then employed a PSM approach to empirically examine the impact of IWMT adoption on banana yields, gross revenue, net returns, irrigation fre- quency, and expenditure in China. An IPWRA estimator was also employed to estimate the treatment effects as a robustness check. Further, we conduct heterogeneous analysis by gender of household head and land size.

The PCC results showed that banana farmers’ IWMT adoption is significantly correlated with their perceptions of whether IWMT adoption can help reduce farm workload, fertilizers, and water, and the extent to which IWMT is easier to operate in practice and generates higher economic benefits than furrow irrigation technology. In addition, a robust positive effect of IWMT adoption was found on banana yields, gross revenue, net returns, and irrigation frequency based on the PSM and IPWRA estimations. The estimated treatment effects reveal that IWMT adoption significantly increases banana yields by 19.38–20.78%, gross revenue by 22.43–24.84% and net returns by 33.56–35.47%.

Because the IWMT has the potential to enhance fertilizer use efficiency and increase water productivity, it was found that IWMT adoption increases the frequency of irrigation on farms by 77.26–86.26%. The practice, however, had no significant effect on irrigation expenditure. These findings provide empirical evidence that IWMT adoption could

enhance farm performance and increase irrigation frequency in banana production in China. In addition, the treatment effects of IWMT adoption vary between households headed by males and females and with different land sizes.

The influence of IWMT adoption on farm performance may change over time. However, this study relied on cross-sectional data from a household survey of banana farmers in China, limiting our capability to explore the potential dynamic patterns of the influences of IWMT adoption. It would be valuable for future studies to employ panel data to explore the long-run effects of IWMT adoption and help enrich our understanding of this interesting field. Further, this study focuses on banana production and uses data from three major banana-producing provinces. It would provide meaningful insights into the adoption and impacts of IWMT adoption if future studies could extend this study by exploring data from other crops and regions. We focus on fertigation only in this study due to the data limitation.

Future research can compare the costs and benefits of other IWMTs or consider farmers’

multiple adoptions of different IWMTs. We believe these works would provide more valu- able insights into how to tackle water shortages induced by climate change.

Policy implications

The findings from this study suggest that IWMT plays an essential role in increasing banana yields, gross revenue, and net returns. Since increases in crop yields and farm income are crucial requisites for food and nutrition security, our findings emphasize the essence of encouraging farmers to adopt IWMT in their banana production. The IWMT also enables farmers to irrigate bananas more times without increasing irrigation expenditure.

The findings seem to suggest farmers would increase irrigation frequency by adopting IWMT. However, the increasing irrigation frequency may lead to water overuse, threaten- ing water use sustainability. Thus, in practice, the government should avoid emphasizing the economic benefits of IWMT adoption in its efforts to diffuse innovative technology.

Instead, they should provide example-based guidelines and brochures to illustrate the benefits and ways of the IWMT application. For example, they can provide evidence that compares the workload, fertilizer and water inputs between IWMT and traditional irriga- tion practices. For example, they can report some case and cost–benefit analysis results.

Since we found that very few farmers perceive the IWMT and furrow irrigation technology are totally the same, publicity should be focused on the detailed process of IWMT, aiming to remove the psychological barriers when adopting new technology and increase the familiarity of farmers with the technology. Moreover, farmers also need to be mentored as they transfer irrigation practices from traditional ones to fertigation, though most sampled farmers report that it is easy or very easy to operate the IWMT.

Our study also showed that IWMT adoption is affected by some observed factors, such as the age and gender of household heads, land size, and distance to credit access. Thus, targeted strategies are needed for farmers who are elderly and female, cultivating small farm sizes, and living far away from credit sources. Practical interventions include provid- ing technology subsidies for those who may face barriers to adopting IWMT and improv- ing the accessibility and affordability of rural finance services. Relying on the widespread application of information and communication technologies, the government could develop digital finance products and provide digital inclusive finance services, and both should be targeted at IWMT adopters.

Acknowledgement

The authors acknowledge the Asian Development Bank Institute (ADBI) Virtual Conference on

“Water Resource Management for Achieving Food Security in Asia Under Climate Change” held on 26–27 October 2022.

Disclosure statement

No potential conflict of interest was reported by the authors.

Funding

Hongyun Zheng acknowledges the financial support from the Fundamental Research Funds for the Central Universities [grant number 2662022JGQD006] and National Social Sciences Foundation of China [grant number 18ZDA072].

ORCID

Hongyun Zheng http://orcid.org/0000-0002-8205-6563 Wanglin Ma http://orcid.org/0000-0001-7847-8459 Victor Owusu http://orcid.org/0000-0002-5277-1128

Data availability

The data that support the findings of this study are available from Hongyun Zheng upon request.

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