Agricultural Water Management 269 (2022) 107645
Available online 11 April 2022
0378-3774/© 2022 Elsevier B.V. All rights reserved.
Review
Yield and water productivity of crops, vegetables and fruits under subsurface drip irrigation: A global meta-analysis
Haidong Wang
a,b, Naijiang Wang
a, Hao Quan
a, Fucang Zhang
a, Junliang Fan
a,*, Hao Feng
a,b,**, Minghui Cheng
a, Zhenqi Liao
a, Xiukang Wang
c, Youzhen Xiang
aaKey Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of the Ministry of Education, Northwest A&F University, Yangling 712100, Shaanxi, China
bInstitute of Water and Soil Conservation, Northwest A&F University, Yangling 712100, Shaanxi, China
cCollege of Life Science, Yan’an University, Yan’an 716000, Shaanxi, China
A R T I C L E I N F O Handling Editor – Xiying Zhang Keywords:
Subsurface drip irrigation Yield
Water productivity Meta-analysis
A B S T R A C T
The rapid population growth and economic development, climate change and irregular rainfall will inevitably intensify the competition of water resources, resulting in the reduction of agricultural irrigation water. In recent years, subsurface drip irrigation (SSDI), as an efficient water-saving irrigation technology, has been widely used in crop production, but its effects on crop yield, irrigation water productivity (IWP) and water productivity (WP) vary with field managements, climatic conditions and soil properties. Here, a global meta-analysis of 984 comparisons from 109 publications was carried out to systematically and quantitatively analyze the responses of yield, IWP and WP of crops, vegetables and fruits to SSDI. The results showed that SSDI significantly increased yield, IWP and WP by 5.39%, 6.75% and 3.97% relative to surface drip irrigation (SDI), respectively. The largest percentage increase in yield was observed in crops (6.42%), followed by vegetables (5.29%) and fruits (3.37%).
SSDI performed best when crops, vegetables and fruits were planted in the open field, under film mulching, in arid regions (<200 mm) and in regions with mean annual temperature ≥12 ℃. Besides, the emitter spacing <
25 cm, emitter discharge rate of 2.5–3.5 L h−1 and buried depth of drip pipe <10 cm were beneficial to obtaining higher increases of yield, IWP and WP. In addition, yield was significantly affected by fertilization rate, and the maximum percentage increase in yield was obtained with 100–200 kg N ha−1, <50 kg P ha−1 and <100 kg K ha−1. Yield, IWP and WP were also significantly affected by soil factors. The percentage changes in yield and IWP in soils with higher bulk density (≥1.4 g cm−3) and in acid soils (pH <7) were significantly higher than those in soils with lower bulk density (<1.4 g cm−3) and in neural and alkaline soils (pH ≥7). In conclusion, SSDI can improve yield and WP, but the application of SSDI should be site-specific.
1. Introduction
Water shortage is a global problem. It is estimated that the global population will be about 9 billion to 10 billion by 2050, and more water will be thus needed (FAO, 2021; Cheng et al., 2021a). The irrigated agriculture is the largest sector of consumptive water use (Intrigliolo et al., 2013; Wang et al., 2021a). How to use the water resources in a sustainable way to ensure food security is a major challenge for present and future generations. Therefore, increasing water productivity through technologies that produce more foods by per drop water is
essential for the sustainable agricultural development (FAO, 2021). Drip irrigation is recognized as a high-efficient water-saving irrigation tech- nology around the world, which has been widely used in arid and semi-arid areas. It can not only improve crop yield and quality, but also increase the water and fertilizer productivity (Wang et al., 2015, 2018, 2021b).
Subsurface drip irrigation (SSDI) is an irrigation technology devel- oped from the surface drip irrigation (SDI). It directly pours water and liquid fertilizer into the root zone for the growth of crops through the irrigation network system in the plough layer. In recent years, many
* Corresponding author.
** Corresponding author. Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of the Ministry of Education, Northwest A&F University, Yangling 712100, Shaanxi, China.
E-mail addresses: [email protected] (J. Fan), [email protected] (H. Feng).
Contents lists available at ScienceDirect
Agricultural Water Management
journal homepage: www.elsevier.com/locate/agwat
https://doi.org/10.1016/j.agwat.2022.107645
Received 29 October 2021; Received in revised form 2 April 2022; Accepted 4 April 2022
Agricultural Water Management 269 (2022) 107645 researchers have studied the effects of SSDI on crop growth. The results
showed that SSDI effectively reduced deep seepage, soil evaporation and weeds growth around the crop, and further improved crop water pro- ductivity (WP). However, the effects of SSDI on crop WP varied with field managements, climatic conditions and soil properties (Badr et al., 2010; Douh et al., 2013; Wang et al., 2017; Çolak et al., 2018; Yao et al., 2021). Besides, the impact of SSDI on yield was controversial among studies (Machado et al., 2003; Dogan et al., 2008; Hassanli et al., 2010;
Al-Mansor et al., 2015; Çolak et al., 2018; Pisciotta et al., 2018; Soliman et al., 2020; Piri and Naserin, 2020). A systematic and quantitative analysis of the responses of yield and WP of various crops to SSDI on a global scale is missing.
Meta-analysis is a quantitative and comprehensive statistical method to study the test results with the same research purpose and systemati- cally explain a group of complex trait factors that may affect the dependent variables to obtain general results (Jiang et al., 2019; Cheng et al., 2021a; Zhang et al., 2021). In recent years, it has been well applied in agricultural studies. For instance, Li et al. (2018) quantified the influences of plastic and straw mulching on yield and WP of potato in China with meta-analysis. Lu et al. (2019), Yu et al. (2020) and Cheng et al. (2021b) applied meta-analysis to study the effects of deficit irri- gation on yield and WP of tomato, wheat and cotton relative to full irrigation. In addition, meta-analysis was also used to analyze the impact of irrigation (Zheng et al., 2019), drip fertigation (Li et al., 2021a), alternate partial root-zone irrigation (Cheng et al., 2021a), aerated irrigation (Du et al., 2018), treated wastewater irrigation (Gao et al.,
2021), organic fertilizer (Wang et al., 2020a), planting method (Wang et al., 2020b) on cereal crops, cotton, vegetables and fruits yield, quality, WP and greenhouse gas emissions. Therefore, a global meta-analysis was conducted to quantify the effects of SSDI on the yield, IWP and WP of crops, vegetables and fruits compared to SDI, and identify the optimal application conditions (field managements, climatic conditions and soil properties) that increase yield, IWP and WP under SSDI systems, so as to further improve our understanding of the responses of crops to SSDI.
2. Materials and methods 2.1. Data collection
Google Scholar (https://xueshu.soogle.top/), Elsevier (Science Direct) (https:// www.sciencedirect.com/search), China National Knowledge Infrastructure (https://www.cnki. net/) and Baidu Scholar (https://xueshu.baidu.com/) were used to collect relevant previous studies from 2001 to 2021 in this study. The key words included “sub- surface drip irrigation” OR “drip irrigation mode” OR “irrigation method” OR “buried depth of drip pipe” AND “water use efficiency or water productivity” AND “yield”. The preferred reporting items for system review and meta-analysis (PRISMA) was used in the present study (Fig. 1). The following criteria were further used to screen these publications: 1) experiments were conducted in the open field and greenhouse (pot/rainproof experiments were excluded); 2) yield, IWP (yield/irrigation water applied (Fern´andez et al., 2020)) or WP
Fig. 1. The PRISMA (preferred reporting items for system review and meta-analysis) used for the collection and meta-analysis of data.
H. Wang et al.
(yield/crop evapotranspiration (ET) or yield/total water applied by irrigation and rainfall) (Fern´andez et al., 2020)) data under SSDI and SDI must be paired; 3) the means, standard deviations (SD) and the number of replications (n) could be directly derived from the publica- tion or could be calculated from the reported data (Tian et al., 2015;
Cheng et al., 2021b). After searching and screening, 109 publications (84 in English and 25 in Chinese) containing 984 paired field observa- tions (439 for yield, 358 for IWP and 187 for WP) across 25 countries were extracted. However, not all publications included yield, IWP and WP at the same time, and the numbers of comparisons for yield, IWP and WP were thus different. The detailed information of 984 comparisons from 109 publications are listed in Appendix A and B.
The agricultural species were classified as crops (wheat, maize, cotton, sorghum, sunflower, bean and green bean), vegetables (tomato, onion, potato, cucumber, cabbage, bell peppers, sugar beet, carrot, okra, parsley, summer squash, eggplant, lettuce, cauliflower and bitter gourd) and fruits (muskmelon, peach, cantaloupe, melon, olive trees, sugar- cane, grape, clementine and pear). The planting conditions included open field and greenhouse. The planting patterns included plastic film mulching and non-plastic film mulching. Drip irrigation parameters included emitter spacing (<25 cm, 25–35 cm, 35–45 cm and ≥45 cm), emitter discharge rate (<1.5 L h−1, 1.5–2.5 L h−1, 2.5–3.5 L h−1 and
≥3.5 L h−1), proportion of subsurface drip irrigation amount to surface drip irrigation amount (<100%, 100% and >100%), and buried depth of drip pipe (<10 cm, 10–20 cm, 20–30 cm, 30–40 cm and 40–45 cm).
Fertilizer application rates included nitrogen (N) rate (<100 kg ha−1, 100–200 kg ha−1, 200–300 kg ha−1 and ≥300 kg ha−1), phosphorus (P) rate (<50 kg ha−1, 50–100 kg ha−1 and ≥100 kg ha−1) and potassium (K) rate (<100 kg ha−1, 100–200 kg ha−1 and ≥200 kg ha−1). Soil properties included bulk density (<1.4 g cm−3 and ≥1.4 g cm−3), pH (<7 and ≥7) and soil texture (coarse soil, medium soil and fine soil) according to USDA soil texture classification (Soil Survey Staff, 2014).
According to the generalized climate classification, experimental sites with mean annual precipitation <200 mm, 200–400 mm, 400–800 mm and ≥800 mm were considered as arid, semi-arid, semi-humid and humid regions, respectively. Mean annual temperature (<12℃ and
≥12℃) was also included in climate factors.
2.2. Statistical analysis
The natural log of the response ratio (R) was calculated as the effect size (lnR), representing the effects of SSDI on crops, vegetables and fruits yield/IWP/WP compared to SDI (Hedges et al., 1999; Du et al., 2018;
Wang et al., 2021c).
lnR= ln Xt
Xc= lnXt− lnXc (1)
where Xt and Xc are the mean yield/IWP/WP of SSDI and SDI, respectively. The variance of effect size (Var(lnR)) was calculated as follows:
Var(lnR) = SDt2
nt×Xt2+ SDc2
nc×Xc2 (2)
where SDt, SDc, nt and nc are the standard deviations and sample sizes of the treatment group (SSDI) and control group (SDI), respectively.
Because different studies owned different statistical precision, in order to achieve a greater precision, the overall effect size (lnR+), the weight and variance of lnR+, and the 95% confidence intervals of lnR+
(95%CI) were calculated based on the following equations (Hedges et al., 1999; Du et al., 2018; Wang et al., 2021c):
lnR+ =
∑k
i=1
wi lnRi
∑k
i=1
wi
(3)
wi= 1
Var(lnRi) (4)
Var(lnR+ ) = 1
∑k
i=1
wi
(5)
lnR+ (95%CI) = lnR+ ±1.96× ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅
Var(lnR+)
√ (6)
Because the heterogeneity among studies was greater than 50%, a random-effect model was adopted (Duffy et al., 2017). The percentage changes in comparison with surface drip irrigation ((R+− 1)×100%) was used to explain the results. Rosenberg’s Fail-safe N technique (Nfs) was used to identify the publication bias. It is generally believed that, the results are robust when Nfs/(5×n+10) >1 (the n is the number of paired data), despite the possibility of publication bias. In this meta-analysis, the values of Nfs were 153109, 60301 and 13475 for yield, IWP and WP, respectively. It was thus considered that the results were robust in this Meta-analysis. Stata/IC 15.1 and Microsoft Excel were used to conduct the meta-analysis, and GraphPad Prism8 was used to create graphs. As per many previous studies (Du et al., 2018; Wang et al., 2020c; Cheng et al., 2021a; b), error bars represent 95% confi- dence intervals in the figures. If there is an overlap between two error bars, it is considered that there is no significant difference; otherwise it is considered that there is significant difference. Besides, if there is an overlap between the error bar and the Y axis, it is considered that there is no significant difference between the control group and the experi- mental group; otherwise it is considered to be significant.
3. Results
3.1. Overview of the datasets
In this meta-analysis, 439, 358 and 187 side-by-side comparisons of yield, IWP and WP were collected from 109 publications, respectively.
There were 210 comparisons for crops (89 for yield, 66 for IWP and 55 for WP), 707 comparisons for vegetables (313 for yield, 274 for IWP and 120 for WP), and 67 for fruits (37 for yield, 18 for IWP and 12 for WP).
Among the datasets, most of the data came from Asia (70.4% for yield, 75.4% for IWP and 77.5% for WP), followed by Africa (15.5% for yield, 15.6% for IWP and 7.0% for WP), Europe (10.7% for yield, 6.1% for IWP and 12.8% for WP), North America (3.2% for yield, 2.9% for IWP and 2.7% for WP) and South America (0.2% for yield, 0 for IWP and WP) (Fig. 2). The frequency distributions of effect sizes for yield, IWP and WP were found to follow Gaussian normal distributions, indicating that the datasets were homogeneous (Fig. 3) (Shan and Yan, 2013).
3.2. Agricultural species, planting conditions and patterns
Generally, SSDI significantly increased the yield, IWP and WP of crops, vegetables and fruits, with the percentage increases of yield by 5.39% (95%CI: 5.10–5.69%), IWP by 6.75% (95%CI: 6.13–7.36%) and WP by 3.97% (95%CI: 3.22–4.74%) across 439, 358 and 187 compari- sons compared to SDI, respectively (Fig. 4). The largest percentage in- crease in yield was observed in crops (6.42%), while the smallest percentage increase in yield occurred in fruits (3.37%) (Fig. 5a). The largest percentage increases in IWP and WP were found in vegetables (7.20%) and fruits (26.06%), respectively. However, the difference in IWP among agricultural species was not significant (Fig. 5b, c).
SSDI also significantly increased yield and WP no matter crops, vegetables and fruits were planted in the open field or greenhouse (Fig. 5d, and f), but the difference in IWP between SSDI and SDI was not significant when the agricultural species were planted in the green- house, because the 95% confidence internal overlapped zero (Fig. 5e).
The percentage changes in yield, IWP and WP (7.19%, 10.09% and 8.55%) of crops, vegetables and fruits planted in the open field were
Agricultural Water Management 269 (2022) 107645
significantly larger than those in the greenhouse.
The overall mean of planting patterns was slightly lower than that of planting conditions, but SSDI still had significantly positive effects in increasing yield, IWP and WP, with the percentage increases by 5.40%, 6.72% and 3.77%, respectively (Fig. 5g, h and i). When the agricultural species were planted under film mulching, the percentage increases in yield and WP were significantly greater than those without film mulching. As for IWP, although a greater IWP was observed under film mulching, there was no significant difference between the two planting patterns (Fig. 5h).
3.3. Irrigation and fertilization factors 1) Irrigation factors
Overall, SSDI increased yield, IWP and WP compared to SDI, regardless of drip irrigation parameters (Fig. 6). With the increase of emitter spacing, the percentage increases in yield and IWP decreased. When the emitter spacing was ≥45 cm, the difference in IWP between SSDI and SDI was not significant (Fig. 6b). However,
when the emitter spacing was less than 25 cm, there were notable percentage increases in yield (18.75%), IWP (17.89%) and WP (20.15%).
The percentage changes in yield, IWP and WP increased first and then decreased with the increase of emitter discharge rate (Fig. 6d, e and f), and there were quadratic parabola relationships between the effect size of yield, IWP and WP and emitter discharge rate (Fig. 7d, e and f). When the emitter discharge rate was 2.5–3.5 L h−1, SSDI improved yield, IWP and WP most (15.47%, 95%CI: 13.05–17.94%;
20.47%, 95%CI: 17.33–23.70%; and 29.21%, 95%CI:
24.83–33.75%, respectively).
When the irrigation amount of SSDI was higher (>100%) or lower (<100%) than that of SDI, there was no significant difference in yield and WP between the two irrigation methods (Fig. 6g, i). When the irrigation amount was the same (100%), the percentage changes in yield, IWP and WP were 5.45% (95%CI: 5.14–5.77%), 7.39% (95%
CI: 6.73–8.04%) and 4.41% (95%CI:.3.59–5.24%), respectively (Fig. 6g, h and i).
The changes of buried depth group were opposite to emitter Fig. 2.The geographical distribution of experimental sites used in this meta-analysis.
Fig. 3.Frequency distributions of effect sizes for yield (a), IWP (b) and WP (c). The red lines are the fitted Gaussian (normal) distributions curves of effect size and N is sample size. P is the significance level.
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discharge group. The percentage changes in yield decreased first and then increased with the increase of buried depth of drip pipe. For yield and IWP, the buried depth <10 cm produced the largest per- centage increases (14.60% and 10.90%, respectively) (Fig. 6j, k) and the buried depth of 20–30 cm showed the least increases (1.85% and 1.22%, respectively). However, WP was increased most by SSDI when the buried depth was 20–30 cm, and the difference in WP between the buried depth of 20–30 cm and <10 cm was not sig- nificant (Fig. 6i). A quadratic parabola relationship existed between
the effect size (lnR) of yield, IWP and WP and buried depth of drip pipe (Fig. 7g, h and i). Overall, the percentage changes of overall mean in yield, IWP and WP was 5.60% (95%CI: 5.26–5.95%), 6.00%
(95%CI: 5.37–6.62%) and 2.86% (95%CI: 2.09–3.64%) (Fig. 6j, k and l), respectively.
2) Fertilization factors
Compared with SDI, SSDI significantly increased yield, IWP and WP under different N fertilization rates, with percentage increases of 8.22%, 6.92% and 2.00% across 308, 268 and 113 comparisons, respectively.
The maximum yield increase occurred when N fertilization rate was 100–200 kg ha−1 (10.20%), followed by ≥300 kg ha−1 (7.81%), 200–300 kg ha−1 (6.54%) and <100 kg ha−1 (3.55%). Moreover, yield under the N fertilization rate of 100–200 kg ha−1 was significantly higher than under the other three fertilization rates (Fig. 8a). The maximum percentage increase in IWP occurred when N fertilization rate was ≥300 kg ha−1 (15.77%), followed by 200–300 kg ha−1 (15.10%), 100–200 kg ha−1 (5.86%) and <100 kg ha−1 (3.47%) (Fig. 8b). As for WP, the maximum value occurred when N fertilization rate was
≥300 kg ha−1 (14.53%), followed by 200–300 kg ha−1 (6.15%),
<100 kg ha−1 (2.40%) and 100–200 kg ha−1 (− 0.91%) (Fig. 8c).
Unlike N fertilization rates, the percentage changes of yield decreased with the increase of P and K fertilization rates (Fig. 8d, g).
However, the changes of WP decreased first and then increased with the increase of P fertilization rate (Fig. 8f). As for IWP, the percentage changes increased first and then decreased with the increase of P fertilization rate and increased with the increase of K fertilization rate (Fig. 8e, h). When P fertilization rate was <50 kg ha−1, the percentage changes in yield, IWP and WP were 14.25%, 13.30% and 9.48%, respectively. When P fertilization rate was 50–100 kg ha−1, the Fig. 4. Effects of subsurface drip irrigation on yield, IWP and WP. Mean effect
and 95% confidence interval are shown. Sample size (n) is listed beside the every bar. Error bars represent 95% confidence intervals. The same as below.
Fig. 5.The effects of SSDI on yield, IWP and WP as a percentage change of the control for different agricultural species (a, b and c), planting conditions (d, e and f), and planting patterns (g, h and i).
Agricultural Water Management 269 (2022) 107645
percentage changes in yield, IWP and WP were 8.35%, 20.08% and 2.71%, respectively. When P fertilization rate was ≥100 kg ha−1, the percentage changes in yield, IWP and WP were 5.52%, 5.10% and 5.09%, respectively (Fig. 8d, e and f). Besides, when the P fertilization rates were 50–100 kg ha−1 and ≥100 kg ha−1, the difference in WP between SSDI and SDI was not significant. When the K fertilization rate was <100 kg ha−1, the yield was increased most by SSDI, and the IWP and WP were increased least. When the K fertilization interval was 100–200 kg ha−1, the percentage changes in yield, IWP and WP were 11.40%, 10.00% and 7.02%, respectively. When the K fertilization rate was ≥200 kg ha−1, the percentage changes in yield, IWP and WP were 8.95%, 14.38% and 12.81%, respectively (Fig. 8g, h and i).
3.4. Soil factors
Overall, SSDI had a positively effect on yield, IWP and WP regardless of soil bulk densities. The percentage increases were greater in soils with higher bulk density (≥1.4 g cm−3) (7.44% for yield, 6.73% for IWP and 10.50% for WP, respectively). When the soil bulk density was
<1.4 g cm−3, the percentage changes in yield, IWP and WP were 3.07%, 1.29% and 2.10%, respectively (Fig. 9a, b and c).
In both acidic and alkaline soils, SSDI significantly increased yield and IWP compared with SDI (Fig. 9d and e). The percentage increases of
yield and IWP in soils with pH <7 were 15.05% (95%CI:
12.37–17.79%) and 26.89% (95%CI: 23.23–30.66%) respectively, which were significantly higher than those in soils with pH ≥7 (10.51%, 95%CI: 9.90%− 11.12% and 18.02%, 95%CI: 16.59%− 19.47%) (Fig. 9d, e). However, WP in soils with pH <7 was decreased by SSDI with a percentage changes of − 33.90% (95%CI: − 38.77 to − 28.63%), which was significantly lower than that in soils with pH ≥7 (9.31%, 95%CI: 7.40–11.25%) (Fig. 9f). Overall, the mean effect size of SSDI on yield, IWP and WP was increased by 10.74% (95%CI: 10.15–11.33%), 19.29% (95%CI: 17.96–20.64%) and 6.58% (95%CI: 4.77–8.42%), respectively (Fig. 9d, e and f).
In the soil texture group, the overall mean of yield, IWP and WP were 5.41%, 6.71% and 3.76%, respectively. The maximum percentage in- crease of yield occurred in fine soils (Fig. 9g). However, the highest percentage changes in IWP and WP were obtained in medium soils (Fig. 9g, h and i). When the soil texture was fine soil, the percentage increases of yield, IWP and WP were 11.01% (95%CI: 10.06–11.97%), 3.13% (95%CI: 0.98–5.33%) and 3.90% (95%CI: 1.86–5.98%), respec- tively. For the medium soil, the percentage increases of yield, IWP and WP were 0.51% (95%CI: 0.03–1.00%), 9.68% (95%CI: 8.22–11.17%) and 12.32% (95%CI: 10.35–14.32%), respectively. For the coarse soil, the percentage increases of yield, IWP and WP were 7.31% (95%CI:
6.92–7.71%), 6.35% (95%CI: 5.64–7.06%) and 1.65% (95%CI:
Fig. 6.The effects of SSDI on yield, IWP and WP as a percentage change of the control for different emitter spacing (a, b and c), emitter discharge (d, e and f), irrigation proportion (g, h and i) and buried depth of drip pipe (j, k and l).
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0.75–2.56%), respectively (Fig. 9g, h and i).
3.5. Climate factors
It can be seen from the overall mean, SSDI significantly increased yield and WP compared with SDI in both the annual average precipi- tation and temperature groups (Fig. 10a, c d and f). However, in semi- arid and semi-humid regions, there was no difference in IWP between the two irrigation methods due to the 95% confidence internal over- lapped zero (Fig. 10b). Generally, the percentage changes of yield, IWP and WP decreased first and then increased with the increase of annual average precipitation. The maximum yield, IWP and WP increase occurred in the humid region (8.86%, 8.63% and 30.66%, respectively), but the differences between humid and arid regions (6.17%, 8.00% and 24.92%, respectively) were not significant (Fig. 10a, b and c). In semi- arid regions, the percentage increases of yield, IWP and WP were 5.42% (95%CI: 3.60–7.28%), − 1.28% (95%CI: − 3.68 to − 1.19%) and 7.84% (95%CI: 4.20–11.60%), respectively. In semi-humid regions, SSDI increased yield, IWP and WP by 1.81% (95%CI: 1.12–2.50%),
− 0.03% (95%CI: − 1.01 to 0.95%) and 2.28% (95%CI: 1.36–3.21%), respectively (Fig. 10a, b and c).
The percentage changes of yield and IWP were positively correlated to annual average temperature. The percentage increases of yield and IWP in regions with annual average temperature <12℃ were 4.94%
(95%CI: 3.46–6.44%) and 4.18% (95%CI: 1.16–7.30%) respectively, which were significantly lower than those with annual average tem- perature ≥12℃ (14.69%, 95%CI: 11.50–17.97% and 10.41%, 95%CI:
7.14–13.78%) (Fig. 10d, e). As for WP, there was no significant
difference between annual average temperature <12℃ (15.80%, 95%
CI: 13.05–18.62%) and ≥12℃ (15.65%, 95%CI: 12.18–19.22%) (Fig. 10f). Overall, the mean effect sizes of SSDI on yield, IWP and WP were increased by 6.84% (95%CI: 5.49–8.20%), 7.19% (95%CI:
4.96–9.47%) and 15.74% (95%CI: 13.58–17.95%), respectively (Fig. 10d, e and f).
4. Discussion
4.1. Agricultural specie, planting condition and pattern effects on yield, IWP and WP
Compared with SDI, SSDI significantly increased crops, vegetables and fruits yield, IWP and WP through the comparison of dataset from 109 publications. SSDI and SDI show different locations and sizes of wetting body and water state (Liu and Li, 2009; Liu et al., 2021).
Compared with SDI, SSDI maintained a relatively drier soil surface and a favorable water condition in the root zone so as to reduce the surface water losses and promote the growth of roots, which improved the ab- sorption and utilization of water and nutrients, and further improved yield, IWP and WP (Kong et al., 2010; Martínez and Reca, 2014; Mali et al., 2017; Yang et al., 2019; Piri and Naserin, 2020; Hamad et al., 2021). Besides, agricultural species groups responded differently to SSDI. The increase of fruit yield was smallest, which was significantly lower than those of crops and vegetables. This may be due to that fruit trees had deeper and wider root systems than vegetables and crops, resulting in small differences in their responses to SDI and SSDI. Vege- tables and crops are shallow-rooted crops and are more sensitive to Fig. 7.Relationships between the effect size lnR of yield, IWP and WP and the emitter spacing, emitter discharge rate and buried depth of drip pipe (a-i).
Agricultural Water Management 269 (2022) 107645
Fig. 8. The effects of SSDI on yield, IWP and WP as a percentage change of the control for different N fertilization rate (a, b and c), P fertilization rate (d, e and f) and K fertilization rate (g, h and i).
Fig. 9. The effects of SSDI on yield, IWP and WP as a percentage change of the control for different soil bulk densities (a, b and c), soil pH (d, e and f) and soil textures (g, h and i).
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water changes (Jefferies and Heilbronn, 1991; Liao et al., 2016; Wang et al., 2019).
Compared with crops, vegetables and fruits planted in the green- house, the percentage increases of yield, IWP and WP were higher when they were planted in the open field. The reason for this may be that the high temperature and humidity in the greenhouse weakened the dif- ference between the two irrigation methods. Plastic film mulching can reduce soil water evaporation, increase soil temperature and maintain higher soil moisture, and further increase yield compared with non- plastic film mulching (Fan et al., 2014; Wang et al., 2020c; Yu et al., 2018; Zheng et al., 2021). The percentage changes of crops, vegetables and fruits yield and WP under film mulching were larger than those without film mulching.
4.2. Irrigation and fertilization factors effects on yield, IWP and WP Reasonable emitter spacing and discharge rate can effectively improve crop water productivity, which are two important parameters of drip fertigation technology (Li, 2006; Ren et al., 2017; Li et al., 2021b). Emitter spacing and discharge rate can affect crop growth, yield and quality by affecting soil moisture uniformity and water infiltration process (Wang et al., 2005, 2010, 2021d). In this meta-analysis, with the increase of emitter spacing, the percentage changes in yield and IWP decreased. With the increase of emitter discharge rate, the percentage changes in yield, IWP and WP increased first and then decreased. When the emitter spacing was less than 25 cm and emitter discharge rate was 2.5–3.5 L h−1, yield, IWP and WP were increased more. This may be because the width of wetting volume on both sides of the lateral was small when the emitter spacing was small, and water mainly accumu- lated in the root zone (El-Hafedh et al., 2001). Moreover, previous studies have showed that there was a clear relationship between the shape of the wetting soil zone and the emitter discharge rate, and the horizontal wetting area increased and the wetted soil depth decreased with the increase of emitter discharge rate (Khan et al., 1996). Beyond the root depth, large emitter spacing, too large or small discharge rate would result in a water loss. Besides, when the irrigation amount of SSDI was higher (>100%) than that of SDI, there was no significant difference in yield and WP between SSDI and SDI. However, when the irrigation amount was the same (100%), SSDI significantly increased yield, IWP and WP. The reason for this difference may be that the sample size of
>100% group was relatively small, and only five studies showed that the irrigation amount of SSDI was higher than that of SDI among the 109 publications. Therefore, the result needs to be further verified.
In the subsurface drip irrigation system, the selection of drip pipe installation depth is the key (Patel and Rajput, 2007; Li et al., 2015;
Bozkurt and Mansuroglu, 2018; Vadar et al., 2019; Hamad et al., 2021).
Irrigation depth determines the initial infiltration position of water, and affects the range and content distribution of water in soils (Gu, 2017).
Moreover, different crops have different requirements for the depth of drip irrigation pipe under different soil property conditions. Some re- searchers have found that crop yields decreased as the buried depth of drip pipe increased. Marouelli and Silva (2002) found the increase in the buried depth of drip pipe from 20 cm to 40 cm led to a 23.4% tomato yield reduction. Bozkurt and Mansuroglu (2018) also reported that green bean production was higher when drip pipes were located at 0.1 m than 0.2 m depth. However, Patel and Rajput (2009) showed that the onion yield and IWP increased first and then decreased as the drip pipe depth increased from 5 cm to 30 cm, and the maximum onion yield was obtained at 10 cm depth. Mansour and Gyuricza (2013) revealed that the potato yield increased with the increase of the buried depth of drip pipe from 15 cm to 30 cm. Yield and IWP were better improved when the drip pipe depth was <10 cm, which meant that the drip irrigation pipe should be buried shallowly, which was consistent with the finding of Li et al. (1996). When the buried depth of drip pipe was shallow, the surface soil was wet and the moisture content was high. However, when the buried depth increased gradually, the resistance of water to shallow layer also increased, which gradually reduced the moisture content of surface soil. When the buried depth exceeded a certain depth, the moisture content of surface soil was low (Patel and Rajput, 2007).
Nutrients absorbed by crops mainly come from soil residues and additional fertilization. Generally, within a certain range of fertilizer inputs, crop yield increased with the increase of fertilizer applied, but the yield decreased when the fertilization rate exceeded a certain threshold (Xing et al., 2015; Gu et al., 2017; Wang et al., 2018). Our study found the maximum yield increase occurred when N fertilization rate was 100–200 kg ha−1. The increase of yield decreased with the increase of P and K fertilization rates. This may be due to that the advantage of SSDI was more obvious in the case of low fertilization rate, and the yield of SSDI was significantly higher than that of SDI. In the case of high fertilization rate, the difference between the two irrigation methods was weakened due to the effect of soil fertility (Badr et al., 2012; Mali et al., 2017).
4.3. Soil factors effects on yield, IWP and WP
Soil bulk density had a direct effect on the infiltration of water, and Fig. 10.The effects of SSDI on yield, IWP and WP as a percentage change of the control for different climate regions (a, b and c) and annual average temperatures (d, e and f).
Agricultural Water Management 269 (2022) 107645 the infiltration capacity of soils decreased with the increase of soil bulk
density (Li et al., 2009). We found the increase of yield, IWP and WP in soils with bulk density ≥1.4 g cm− 3 was significantly higher than that for <1.4 g cm− 3. This result could be explained by that soils with high soil bulk density were more compacted, which inhibited the water from surface drip irrigation seeping into the root zone. However, SSDI could directly transport water to the root zone of crops, which was more conducive to the absorption and utilization of water by roots and reduced soil evaporation.
The greater percentage increases in yield and IWP in soils with pH
<7 can be attributed to that the alkaline soil increased ammonia volatilization, resulting in a large amount of nitrogen loss (Carrijo et al., 2017; Du et al., 2018). Moreover, alkaline soils usually have high salt content, which has a negative impact on crop growth.
The maximum yield increase was obtained in fine soils, followed by coarse soils and medium soils. This was probably because the fine- textured soils had higher clay content with poor soil aeration and the coarse soil owned higher sandy content with poor capacity of holding water and nutrients (Groffman and Tiedje, 1991; Bollmann and Conrad, 2004; Du et al., 2018; Cheng et al., 2021b), which was not conducive to surface drip irrigation and widen the gap between the two irrigation methods. However, the difference in yield between the two drip irri- gation methods was small in medium soils. On the contrary, SSDI significantly increased WP compared with SDI in medium soils. This may be due to the effective reduction of water consumption under SSDI in medium soils.
4.4. Climate factors effects on yield, IWP and WP
In this meta-analysis, we found that the increases of yield, IWP and WP under SSDI in arid regions were higher than those in semi-arid and semi-humid regions. The percentage increases in yield and IWP in re- gions with annual average temperature ≥12℃ were significantly greater than those with annual average temperature <12℃. The possible reason may be that the arid regions with annual average tem- perature ≥12℃ owned higher evaporation, and SSDI effectively reduced the loss of surface water, thereby providing more water for crop growth. Besides, we also found the maximum yield, IWP and WP in- creases under SSDI occurred in humid regions, but the differences be- tween humid and arid regions were not significant. It may be related to that less rainfall occurred during the growing season despite of the high annual rainfall.
4.5. Limitations
The responses of yield, IWP and WP of crops, vegetables and fruits under SSDI were evaluated in this meta-analysis. Nevertheless, other factors such as the frequency of drip irrigation and fertilization, seasonal temperature and precipitation also had influences on the results. Future studies need to consider more factors affecting the application of SSDI.
The data of yield, IWP and WP were not sufficient for the irrigation proportion group >100% and soil pH <7, so the results need to be further verified. Besides, we only considered the responses of crops, vegetables and fruits under SSDI in this meta-analysis in recent 20 years, but the alfalfa and other grasses and studies before 2001 were not considered.
5. Conclusions
A global meta-analysis of the literature was conducted to investigate the responses of yield, irrigation water productivity and water produc- tivity to subsurface drip irrigation compared to surface drip irrigation.
Overall, subsurface drip irrigation significantly increased the yields of crops, vegetables and fruits, irrigation water productivity and water productivity. Subsurface drip irrigation performed best when the emitter spacing was less than 25 cm, the emitter discharge rate varied
2.5–3.5 L h−1, and the buried depth of drip pipe was less than 10 cm.
The improvement of yield was significantly affected by fertilization rate, soil and climate factors. The nitrogen rate of 100–200 kg ha−1, phos- phorus rate <50 kg ha−1, potassium rate <100 kg ha−1, mean annual temperature ≥12℃, higher bulk density (≥1.4 g cm−3) and acidic soil (pH<7) were more beneficial to the increase of yield under subsurface drip irrigation. These results highlighted the potential of subsurface drip irrigation in improving yield and water productivity of crops, vegetables and fruits. However, considering the high installation and maintenance costs as well as the blockage of emitters, the application of subsurface drip irrigation should be comprehensively weighed on the basis of yield, economic benefit, environmental benefit and water productivity. The rational application of subsurface drip irrigation is of great significance to the protection of water resources and the sustainable development of agriculture.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
This work was jointly supported by the National Natural Science Foundation of China (51879224), Key Research and Development Pro- gram of Shaanxi Province (2022NY-063), the Chinese Universities Sci- entific Fund (2452020018).
Appendix A. Supporting information
Supplementary data associated with this article can be found in the online version at doi:10.1016/j.agwat.2022.107645.
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