Quantifying Key Internal and External Yield-limiting Factors for Chinese Pear in Smallholder Dominant Areas

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  • 1 Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China

Pear (Pyrus spp.) is the third-largest economic crop in China after apples (Malus pumila Mill.) and citrus (Citrus reticulata Blanco), and it is mainly cultivated by smallholders. Currently, the yield of Chinese pear ranks midlevel globally, with only 17.9 t⋅ha−1⋅year−1, which is lower than that of the United States (36.0 t⋅ha−1⋅year−1). However, the factors limiting pear production dominated by smallholders are unclear. We interviewed 75 smallholders about 18 yield-related indicators for pear-typical planting areas. The boundary line model was used to analyze the contribution of internal factors and dominant external factors affecting yield and to simulate strategies for increasing yield through the scenario analysis. The results showed that the average gap between the average and highest attainable yields for smallholders was 10.5 t⋅ha−1⋅year−1 in Luniao County. Among individual yield-limiting factors, chemical fertilizer nitrogen (N) input (13.3%) was the most significant, followed by the soil-available N content (12.0%) and leaf magnesium content (12.0%). Overall, the contribution of all soil factors (42.7%) was the largest compared with the other factor categories. However, the contribution of internal factors could not be ignored and accounted for 25.3% of the total. A scenario analysis showed that comprehensive strategies considering soil, management, and internal factors achieved the largest yield improvement (14%), as did reducing the fertilizer application rate (66%) compared with only using soil or leaf diagnosis methods. Therefore, integrated methods should be considered when developing pear orchard management measures and include soil, management, and internal factors.

Abstract

Pear (Pyrus spp.) is the third-largest economic crop in China after apples (Malus pumila Mill.) and citrus (Citrus reticulata Blanco), and it is mainly cultivated by smallholders. Currently, the yield of Chinese pear ranks midlevel globally, with only 17.9 t⋅ha−1⋅year−1, which is lower than that of the United States (36.0 t⋅ha−1⋅year−1). However, the factors limiting pear production dominated by smallholders are unclear. We interviewed 75 smallholders about 18 yield-related indicators for pear-typical planting areas. The boundary line model was used to analyze the contribution of internal factors and dominant external factors affecting yield and to simulate strategies for increasing yield through the scenario analysis. The results showed that the average gap between the average and highest attainable yields for smallholders was 10.5 t⋅ha−1⋅year−1 in Luniao County. Among individual yield-limiting factors, chemical fertilizer nitrogen (N) input (13.3%) was the most significant, followed by the soil-available N content (12.0%) and leaf magnesium content (12.0%). Overall, the contribution of all soil factors (42.7%) was the largest compared with the other factor categories. However, the contribution of internal factors could not be ignored and accounted for 25.3% of the total. A scenario analysis showed that comprehensive strategies considering soil, management, and internal factors achieved the largest yield improvement (14%), as did reducing the fertilizer application rate (66%) compared with only using soil or leaf diagnosis methods. Therefore, integrated methods should be considered when developing pear orchard management measures and include soil, management, and internal factors.

Pear (Pyrus spp.) cultivation is among the primary sources of income for farmers in China (Zhu et al., 2017) and has a history spanning more than 3000 years (Song et al., 2014). The cultivation area for pear in China is 1.0 million ha, which is the largest in any country worldwide (FAO, 2019). However, the average yield is 17.9 t⋅ha−1⋅year−1, which is only one-third the average yield of the top ten pear-growing countries of the world, indicating that there is great potential for improvement (FAO, 2019). For example, the average yield in the United States is 36.0 t⋅ha−1⋅year−1 (FAO, 2019). As a labor-intensive industry that is largely managed by farmers, particularly in China, improving the pear yield would be helpful for increasing farmers’ incomes and revitalizing the rural countryside (Li, 2014). Therefore, it is essential to identify the key factors constraining pear yield to close this gap.

Pear growth and yield are affected by external factors (environmental and managerial) and internal factors (the plants themselves) (Maria et al., 2014). Regarding external factors, management mainly includes tree density (Bist and Yadav, 2004), fertilizer input rates (Duarte et al., 2010; Paula et al., 2020), ratio of mineral fertilizer and manure fertilizer (Liu et al., 2013), and disease and pest control (Glenn et al., 1997). Environmental factors are related to the climate and soil. Changes in climatic factors, such as global warming, do not occur frequently and are difficult to control (Feng et al., 2018). One example of a soil factor is the soil nutrient status, which directly affects the absorption and utilization of nutrients by the tree to affect growth and yield (Aruani et al., 2014).

Pear is a perennial plant that can store nutrients, thereby making internal factors a crucial aspect of pear growth. The ‘Nanguo’ pear yield gradually increases as the trees age from 1 to 10 years old because the trees can absorb nutrients over time (Liu et al., 2016). In addition, the tree leaves are very sensitive to surpluses or deficiencies in mineral nutrients, which have essential roles in structural components and energy transformation (Dar et al., 2015). The elements contained in leaves are directly related to yield and include N, phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and others (Awasthi et al., 1998; Babu and Yadav, 2005; Kumar et al., 2007).

Various methods have been applied to assess the effects of different indicators on yield, such as field trials (Gomand et al., 2018) and crop growth models (De Melo-Abreu et al., 2015). However, these methods do not consider the actual production situation of farmers (Cao et al., 2019). The boundary line model is a more comprehensive method that has been widely applied to assess yield-limiting factors of wheat (Cao et al., 2019; Li et al., 2020), maize (Chen et al., 2018), apple (Zhang et al., 2019a), mango (Zhang et al., 2019b), and other crops (Mahbubeh et al., 2021). However, these studies did not evaluate the effect of internal factors on yield.

Until now, the factors limiting the yield of Chinese pear are unclear. Using the boundary line model, this study aimed to 1) quantify the effects of internal factors on and identify the overall key limiting factors for pear yield at smallholder farms in China and 2) explore the best agronomy practice strategies for yield improvement.

Materials and Methods

Site description.

The research site was in Luniao County (lat. 30°27′N–30°28′N, long. 119°43′E–119°46′E), Zhejiang Province, which is a typical pear planting area of smallholders with 0.7 ha per capita arable area and more than 30 years of planting history. The mean annual temperature and precipitation are 16.0 °C and 1350 mm, respectively; these are typical climatic conditions for sand pear growth in south China. The major pear variety grown in this county is ‘Cuiguan’, a predominant cultivar of sand pear.

Data collection and processing.

Data were collected and processed from 10 Aug. 2020 to 25 Jan. 2021. Seventy-five smallholders were randomly selected from the county’s planting list; their basic information is shown in Table 1. We investigated the yield, soil properties, farmer agronomy practices, tree age, tree density, and leaf nutrient content. The pear yield, tree density, tree age, and management practices were collected via a face-to-face questionnaire. Pear tree agronomy management mainly includes fertilization, insecticides and fungicides, pruning branches, pollination, flower thinning, and fruit thinning. Farmers use the same operations for pruning branches, thinning flowers, and thinning fruits; therefore, we chose the fertilization (mineral fertilizer N, P2O5, and K2O input) and spraying situation (pest and disease control time) as the main management factors. None of the farmers thinned flowers and fruits twice; therefore, these factors were not considered during our analysis. Soil properties included soil organic matter, available N, available P, available K, soluble Ca, and soluble Mg. In the leaf, we measured the total N, P, K, Ca, and Mg contents. Soil and leaf samples were collected from the farms during the pear harvest period (Fig. 1). The soil sample was a mixed sample of the 0- to 40-cm layer from three plots collected from near the drip line of the vertical projection of the canopy, as described by Wu et al. (2010). Twenty leaves were collected from sites corresponding to the soil samples and taken from the middle of new shoots at the periphery of the trunk (five leaves in each of the four directions: south, east, north, and west).

Fig. 1.
Fig. 1.

The distribution of sampling points of farmers (n = 75). The solid boundary line represents the location of Luniao County. The black dot represents the sampling location.

Citation: HortScience 56, 11; 10.21273/HORTSCI16115-21

Table 1.

Characteristics of the investigated smallholders.

Table 1.

Soil properties were analyzed using methods established by Bao (2000) as follows: the soil organic matter content was measured via the external heating potassium dichromate volumetric method; available N was determined using the diffusion method; available P was extracted with HCl-NH4F and measured via the molybdate blue method; available K was determined via flame photometry after extraction with 1.0 M NH4OAC; and Ca and Mg were measured using inductively coupled plasma optical emission spectrometry (Varian 710 ES; Agilent, Santa Clara, CA). Leaf N, P, and K were measured using the diffusion method, molybdate blue method, and flame photometry method, respectively, after dissolution in H2SO4-H2O2 (Bao, 2000). The leaf Ca and Mg were dissolved in 0.5 M HNO3 and measured using inductively coupled plasma optical emission spectrometry (Zhou et al., 2014).

The data were analyzed with RStudio 3.5 software for Windows (The R Project for Statistical Computing, Vienna, Austria). Yield-limiting factors and the contribution rate of different indices were analyzed using the boundary lines model with Microsoft Office 2016 (Redmond, WA), and figures were drawn using Sigmaplot 12.5 (Systat Software, Bengaluru, India).

Data analysis methods.

The approach to building boundary lines during this study was based on the methods described by Chen et al. (2018) and included the following main steps: 1) establishing scatter plots of pear yield and yield-related limiting factors; 2) identifying upper boundary points for every limiting factor; and 3) fitting a curve using the upper boundary points.

The total gap was considered the difference between the highest attainable yield and farmers’ actual yield; this includes the explainable gap, which is caused by individual limiting factors, and the unexplainable gap, which is caused by other unknown factors (Cao et al., 2019). The explainable gap represented the total gap in this study. Moreover, according to von Liebig’s law of the minimum (von Liebig, 1840), the most significant limiting factor was used to explain the yield gap. The average contribution proportion of different factors was calculated assuming that the total percentage of all factors was 100%.

A scenario analysis was used to predict the best yield-increasing strategy. Current yield improvement strategies based on fertilization include soil testing and fertilization technology, as well as leaf nutrition diagnosis. Crop yield is affected by various factors, such as soil factors, management factors, and leaf factors. Therefore, we assumed that a comprehensive improvement strategy that considers multiple factors would be the best strategy for improving production. Accordingly, we set three scenarios: Si (soil test and fertilization technology); Sii (leaf nutrient diagnosis technology); and Siii (comprehensive management technology). For Si, the best fertilizer application rate was calculated using the pear nutrient absorption regulation and soil nutrient content. We calculated the increasing yield by summing the current yield with the farmers’ previous yield for which the fertilizer rate and soil content were within reasonable ranges. For Sii, we hypothesized that the increasing fertilizer ratio was the sum of every leaf nutrient content ratio from the current average value to the optimum value. Increasing the yield ratio followed the same rule as Si, for which the leaf contents were within reasonable intervals. For Siii, we selected two main limiting factors of soil, management, and internal factors according to the boundary line model; the increasing fertilizer and yield ratio was the sum of the limiting factors according to the reasonable range and current average value.

Results

Pear yield gap and basic management.

The average yield of pear was 16.5 t⋅ha−1⋅year−1, and the yield gap was 10.5 t⋅ha−1⋅year−1 (Table 1). This translated to an income gap of $19,384.60 per ha−1⋅year−1.

The basic results of soil factor analysis (soil organic matter, available N, available P, available K, soluble Ca, and soluble Mg), agronomy factor analysis (mineral fertilizer N inputs; mineral fertilizer P2O5 inputs; mineral fertilizer K2O inputs; and the ratio of manure fertilizer, density, and pest and disease time), and internal factor analysis (tree age, leaf N content, leaf P content, leaf K content, leaf Ca content, and leaf Mg content) are shown in Table 2.

Table 2.

Pear yield and the yield-limiting factors.

Table 2.

Positive correlations were found between soil N and P, P and K, and Ca and Mg; between the input rates of P2O5 and K2O; and between leaf Ca and Mg (Fig. 2). Overall, the fertilizer application rate used as management practice was not significantly correlated with the soil nutrient content, although it showed a positive correlation trend (Fig. 2). The correlation between the soil nutrient content and leaf mineral element content was weak (Fig. 2).

Fig. 2.
Fig. 2.

The correlation analysis of different production restriction factors (n = 75). The contents of soil organic matter (SOM), soil-available nitrogen (SN), soil-available phosphorus (SP), soil-available potassium (SK), soluble calcium (SCa), and soluble magnesium (SMg) are shown. MN, MP, and MK represent the application rates of N, P2O5, and K2O, respectively. LN, LP, LK, LCa, and LMg represent the nutrient contents of leaf N, P, K, Ca, and Mg, respectively.

Citation: HortScience 56, 11; 10.21273/HORTSCI16115-21

Yield-limiting factors and their contributions to the yield gap.

The relationships between the pear yield and 18 yield constraint factors were evaluated using the boundary line model (Fig. 3A–C).

Fig. 3.
Fig. 3.
Fig. 3.

The relationship between pear yield and soil factors (A), agronomy practice factors (B), and internal factors (C). The soil factors include soil organic matter, available nitrogen (N), available phosphorus (P), available potassium (K), soluble calcium (Ca), and magnesium (Mg). The agronomy factors include mineral fertilizer N inputs, mineral fertilizer P2O5 inputs, mineral fertilizer K2O input, the ratio of manure fertilizer, density, and the frequency of controlling pests and disease. The internal factors include tree age, leaf N content, leaf P content, leaf K content, leaf Ca content, and leaf Mg content. The black dots represent the yield that farmers can obtain. The black triangle represents the boundary point. The solid black line represents the boundary line.

Citation: HortScience 56, 11; 10.21273/HORTSCI16115-21

Overall, the chemical N fertilizer input rate was the most common yield constraint (accounting for 16.4%), followed by the soil-available N content (15.5%) and pest and disease control time (13.0%) (Fig. 4). The leaf Mg content (12.0%) was the most important leaf factor. The two least significant yield-limiting factors were the P2O5 input rate (1.6%) and tree age (2.8%).

Fig. 4.
Fig. 4.

The yield gap of different yield-limiting factors at the regional level (n = 75). The box boundaries indicate the upper and lower quartiles. The whisker caps indicate the 95th and 5th percentiles. The dotted line represents the average. Ca = calcium; K = potassium; Mg = magnesium; N = nitrogen; P = phosphorus.

Citation: HortScience 56, 11; 10.21273/HORTSCI16115-21

From the perspective of factors at the farmer level (Fig. 5), soil factors were the most significant index affecting pear yield, accounting for 40.0%. The management N input rate was the dominant individual factor (16.0%), which was also the most important factor according to the farmers themselves. Internal factors also significantly affected yield, accounting for 25.3%, with leaf Mg content (10.7%) being the most important yield constraint among them.

Fig. 5.
Fig. 5.

The proportion of dominant factors that influence the pear yield at the farmer level (n = 75). The proportion of each factor indicates the percentage of the number of farmers. Tis factor is highest than any other factors divided by the total number of interviewed farmers. Ca = calcium; K = potassium; Mg = magnesium; N = nitrogen; P = phosphorus.

Citation: HortScience 56, 11; 10.21273/HORTSCI16115-21

Scenario analysis.

Based on the defined yield-limiting factors, we set three different scenarios for increasing yield (Table 3). For Si, soil testing and fertilization technology were applied. Using this strategy, the yield was projected to increase by 6% and the fertilizer use rate decreased by 78% because the fertilizer input rates were required to decrease by nearly 2100 kg⋅ha−1 (N+P2O5+K2O). For Sii, the leaf diagnosis was applied; the application of fertilizer increased by 27% because the average nutrient value was lower than the normal value, and the yield increased by 5%. For Siii, after comprehensively optimizing the soil nutrients, management practices, and internal factors combined with boundary lines, fertilizer use rates decreased by 66% and the yield increased by 14%, which was the largest increase compared with the other scenarios.

Table 3.

Scenario analysis of different management methods use for fertilizer application and yield.

Table 3.

Discussion

Pear yield gap for smallholders in China.

Based on our survey results, the yield of pears in this area ranges from 3.6 to 27 t⋅ha−1⋅year−1, revealing variations in yield between smallholders. These gaps may be related to differences in cultivation techniques attributable to the gender, age, and education level of the farmers (Zhang et al., 2019a). In addition, the local average pear yield is 16.5 t⋅ha−1⋅year−1, which is lower than the average of high-yield countries such as the United States and Switzerland (FAO, 2019). This difference may be because of a shortage of labor, lack of knowledge (Tittonell et al., 2013; Vandeplas et al., 2010), or smaller planting area per capita (Wang et al., 2016), resulting in a lack of scientific management and standardized production capacity. Therefore, changing the overall industry model and improving farmer management techniques are important.

Yield-related individual limiting factors of pear.

Smallholders have the most important role in crop production in China (Cao et al., 2019). Under specific hydrological and climatic conditions, unsuitable management and soil conditions have a notable impact on yield (Grassini et al., 2011). Many studies reported that fertilizer application rates of most farmers of cash crops are higher than the recommended value in China, particularly for N fertilizer (Weinbaum et al., 1992; Zhang et al., 2018), which greatly constrains the vegetative growth of trees and the yield (Ma et al., 2020; Ndabamenye et al., 2013). A study of apples by Zhang et al. (2018) indicated that the amount of basal fertilizer N of smallholders was among the top three yield-limiting factors in China, which is similar to the results of our study. Large-scale application of N fertilizer occurs because of the farmers’ lack of scientific fertilization knowledge (Lobell et al., 2009). Many farmers are not familiar with the nutrient marks on fertilizer bags, such as “20–16–9,” and they cannot calculate the nutrient demand of crops (Zhang et al., 2016). Moreover, most farmers fertilize the field surface rather than using deep fertilization techniques because they have limited time to devote to agricultural activities (Lu et al., 2008; Vandeplas et al., 2010). Our research found that the optimal amount of N fertilizer calculated by the boundary line was higher than the recommended amount because the nutrient use efficiency was lower. Therefore, translating scientific knowledge into farming action, such as using the Science and Technology Backyard mode (Zhang et al., 2016), while developing innovation technologies to reduce labor time should greatly improve yield.

Soil is a mineral reserve that stores various nutrients necessary for normal growth; therefore, soil fertility parameters are closely correlated with one another (Rahman et al., 2011). This is similar to our findings that soil organic matter, soil N, soil P, soil K, soil Ca, and soil Mg are strongly correlated with each other. Additionally, in our study, the leaf Ca and Mg contents were positively correlated (Fig. 2), which is consistent with the finding of Song et al. (2011). However, Wang et al. (2019) found that the leaf N content was negatively correlated with the leaf Ca and Mg contents but positively correlated with the leaf K content, which was in contrast to our results. This may be because the crop variety and research sites were different. Overall, different limiting factors are not isolated, but they are closely related. Therefore, to formulate strategies to optimize yield, changes in yield caused by synergy and antagonism between elements should be further investigated.

Overall yield-limiting factors of pears.

From the perspective of soil, agronomy practice, and internal factors, our results showed that soil factors have the most significant effect on the yield of pear orchards, which is in agreement with the results of Sussy et al. (2020). It is likely that soil is a direct source of nutrients for trees and directly affects the growth and development of crops (Song, 2017). In addition, internal factors, such as the elemental content of leaves, are closely related to fruit crop yield. This may be because proteins and fats, among other macromolecules, are synthesized in the leaves (Dar et al., 2015). Therefore, the effects of internal factors on pear yield are important, and appropriate measures should be taken to ensure an appropriate nutrient content in the leaves.

In terms of soil nutrient concentration and leaf mineral element content, we found a weak correlation between soil and leaf variables (Fig. 1). Khattak and Hussain (2007) concluded that the relationship between orchard soil nutrients and tree nutrient levels is very complicated. When the soil nutrient content was high, the corresponding nutrient content of fruit trees was not always high (Khattak and Hussain, 2007). However, Wang et al. (2019) suggested that there is a definite correlation between the soil nutrient content and leaves. Further research is necessary to determine the correlation between soil and leaves and to formulate more reasonable fertilization management strategies.

Method and strategy for yield improvement.

Soil analysis can reveal the availability of soil nutrients; furthermore, soil testing and fertilization technology can be used to develop a fertilization strategy, which is helpful for reducing the impact of unbalanced fertilization and increasing efficiency (Hu and Zhou, 2011). Additionally, pear is a perennial crop in which nutrients are stored in the trunk (Cheng and Raba, 2009); therefore, diagnosis technology for trees, such as leaf nutrient diagnosis technology (Diagnosis and Recommendation Integrated System), is widely used to determine the status of tree nutrient deficiency (Beaufils, 1973). Combining these methods may be an effective strategy for improving fruit yield (Du, 1977).

The boundary line model is a comprehensive method that can identify key limiting factors and combine the various factors of soil, management, and leaf nutrients, which is helpful for developing strategies to increase yield. According to our results, if optimized strategies are applied to all Chinese pear orchards (1.0 million ha), then fertilizer use can be decreased by 1.3 million tons while increasing yield by 2.0 million tons. Our research revealed the contribution of internal factors to yield but did not consider the interactions among factors. Therefore, the development of soil-, leaf-, and management-based models that consider the interaction of different factors may help to achieve even higher crop yields.

Conclusion

We found that smallholder farmers have large yield gaps between the actual yield and high yield of Chinese pear. Soil, agronomy, and internal factors are important yield-limiting factors. From the individual factor perspective, the amount of N fertilizer input (agronomy factor) is the most critical factor limiting yield, followed by the soil-available N content (a soil factor) and leaf Mg content (an internal factor). Overall, soil factors were the dominant yield-limiting factor group. Additionally, the impact of internal factors was significant. Research based on a scenario analysis showed that comprehensive management methods that consider soil, agronomy, and internal factors will greatly increase yields. Our findings indicate that further studies aimed at pear yield improvement should consider internal factors, and that comprehensive management measures are vital for further development of the pear industry. Furthermore, the influence of synergy and antagonism between different factors on the yield should be determined to achieve further improvement.

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  • Maria, C.A., Pablo, D.R. & Norma, E.B. 2014 Influence of soil properties on yield and fruit maturity at harvest of ‘Williams’ pear Chilean J. Agr. Res. 74 4 460 467 https://doi.org/10.4067/S0718-58392014000400013

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  • Ndabamenye, T., Asten, P.J.A.V., Blomme, G., Vanlauwe, B., Uzayisenga, B., Annandale, J.G. & Barnard, R.O. 2013 Nutrient imbalance and yield limiting factors of low input East African highland banana (Musa spp. AAA-EA) cropping systems Field Crops Res. 147 68 78 https://doi.org/6878.10.1016/j.fcr.2013.04.001

    • Search Google Scholar
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  • Rahman, M.H., Holmes, A.W., McCurran, A.G. & Saunders, S.J. 2011 Impact of management systems on soil properties and their relationships to kiwifruit quality Commun. Soil Sci. Plant Anal. 42 332 357 https://doi.org/10.1080/00103624.2011.538884

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  • Song, R. 2017 Analysis of soil nutrients and tree nutrient abundance and deficiency status of main pear orchards in the Yellow River Basin Nanjing Agricultural University Nanjing, China (in Chinese)

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  • Song, Y., Fan, L., Chen, H., Zhang, M.Y., Ma, Q.Q., Zhang, S.L. & Wu, J. 2014 Identifying genetic diversity and a preliminary core collection of Pyrus pyrifolia, cultivars by a genome-wide set of SSR markers Scientia Hort. 167 5 16 https://doi.org/10.1016/j.scienta.2013.12.005

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  • Sussy, M., Dahlin, A.S., Onyango, M.C., Oluoch, K.W. & Marstrop, H. 2020 Soil and management-related factors contributing to maize yield gaps in western Kenya Food Energy Secur. 9 1 1 17 https://doi.org/10.1002/fes3.189

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  • Tittonell, P. & Giller, K.E. 2013 When yield gaps are poverty traps: The paradigm of ecological intensification in African smallholder agriculture Field Crops Res. 143 76 90 https://doi.org/10.1016/j.fcr.2012.10.007

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  • Vandeplas, I., Vanlauwe, B., Driessens, L., Merckx, R. & Deckers, J. 2010 Reducing labour and input costs in soybean by smallholder farmers in south-western Kenya Field Crops Research 117 70 80 https://doi.org/10.1016/j.fcr.2010.02.002

    • Search Google Scholar
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  • von Liebig, J.F. 1840 Chemistry in its Application to Agriculture and Physiology Taylor and Walton London, UK

  • Wang, N.N., He, H.H., Lacroix, C., Morris, C., Liu, Z.D. & Ma, F.W. 2019 Soil fertility, leaf nutrients and their relationship in kiwifruit orchards of China’s central Shaanxi province Soil Sci. Plant Nutr. 65 4 369 376 https://doi.org/10.1080/00380768.2019.1624481

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  • Wang, N., Joost, W. & Zhang, F.S. 2016 Towards sustainable intensification of apple production in China - Yield gaps and nutrient use efficiency in apple farming systems J. Integrative Agr. 15 716 725 https://doi.org/10.1016/S2095-3119(15)61099-1

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  • Weinbaum, S.A., Johnson, R.S. & Theodore, M.D. 1992 Causes and consequences of overfertilization in orchards HortTechnology 2 1 112 121 https://doi.org/10.21273/horttech.2.1.112b

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  • Wu, H.Y., Kong, Y., Yao, Y.C., Bi, N.N., Qi, L.P. & Fu, Z.G. 2010 Effects of intercropping aromatic plants on soil microbial quantity and soil nutrients in pear orchard Zhongguo Nong Ye Ke Xue 43 1 140 150 (in Chinese)

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  • Zhang, D., Wang, C., Li, X.L., Yang, X.S., Zhao, L.B. & Xia, S.J. 2019a Correlation of production constraints with the yield gap of apple cropping systems in Luochuan County, China J. Integr. Agr. 18 8 1714 1725 https://doi.org/10.1016/s2095-3119(18)62098-2

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  • Zhang, D., Wang, C. & Li, X.L. 2019b Yield gap and production constraints of mango (Mangifera indica) cropping systems in Tianyang County, China J. Integr. Agr. 18 8 1726 1736 https://doi.org/10.1016/s2095-3119(18)62099-4

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  • Zhang, W.F., Cao, G.X., Li, X.L., Zhang, H.Y., Wang, C., Liu, Q., Chen, X.P., Cui, Z.L., Shen, J.B., Jiang, R.F., Mi, G.H., Miao, Y.X., Zhang, F.S. & Dou, Z.X. 2016 Closing yield gaps in China by empowering smallholder farmers Nature 537 671 674 https://doi.org/10.1038/nature19368

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  • Zhang, Y.C., Lei, H.M., Zhao, W.G., Shen, Y.J. & Xiao, D.P. 2018 Comparison of the water budget for the typical cropland and pear orchard ecosystems in the north China plain Agr. Water Mgt. 198 53 64 https://doi.org/10.1016/j.agwat.2017.12.027

    • Search Google Scholar
    • Export Citation
  • Zhou, G.F., Peng, S.A., Liu, Y.Z., Wei, Q.J., Han, J. & Islam, M.Z. 2014 The physiological and nutritional responses of seven different citrus rootstock seedlings to boron deficiency Trees (Berl.) 28 295 307 https://doi.org/10.1007/s00468-013-0949-y

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    • Export Citation
  • Zhu, L.W., Tang, X.M., Lin, T.Y., Zhou, S.S., Liu, P., Ye, Z.F., Wang, D.S. & Wu, Z.Y. 2017 First report of Fusarium root rot in Asian pear caused by Fusarium solani in China Plant Dis. 101 1 252 https://doi.org/10.1094/pdis-07-16-0998-pdn

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Contributor Notes

This research was financed by the Science and Technology Planning Project of Ningbo City, China (202002N3106) and the university–enterprise cooperation project of Luniao County, China (2020330004002089).

H.F. and L.W. conceived and designed the study, analyzed data and drafted the manuscript; Z.M., Y.H., F.L., K.C., W.P., S.T., and X.Z. performed the experiments; Q.M. revised the manuscript.

L.W. is the corresponding author. E-mail: finm@zju.edu.cn.

  • View in gallery

    The distribution of sampling points of farmers (n = 75). The solid boundary line represents the location of Luniao County. The black dot represents the sampling location.

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    The correlation analysis of different production restriction factors (n = 75). The contents of soil organic matter (SOM), soil-available nitrogen (SN), soil-available phosphorus (SP), soil-available potassium (SK), soluble calcium (SCa), and soluble magnesium (SMg) are shown. MN, MP, and MK represent the application rates of N, P2O5, and K2O, respectively. LN, LP, LK, LCa, and LMg represent the nutrient contents of leaf N, P, K, Ca, and Mg, respectively.

  • View in gallery View in gallery

    The relationship between pear yield and soil factors (A), agronomy practice factors (B), and internal factors (C). The soil factors include soil organic matter, available nitrogen (N), available phosphorus (P), available potassium (K), soluble calcium (Ca), and magnesium (Mg). The agronomy factors include mineral fertilizer N inputs, mineral fertilizer P2O5 inputs, mineral fertilizer K2O input, the ratio of manure fertilizer, density, and the frequency of controlling pests and disease. The internal factors include tree age, leaf N content, leaf P content, leaf K content, leaf Ca content, and leaf Mg content. The black dots represent the yield that farmers can obtain. The black triangle represents the boundary point. The solid black line represents the boundary line.

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    The yield gap of different yield-limiting factors at the regional level (n = 75). The box boundaries indicate the upper and lower quartiles. The whisker caps indicate the 95th and 5th percentiles. The dotted line represents the average. Ca = calcium; K = potassium; Mg = magnesium; N = nitrogen; P = phosphorus.

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    The proportion of dominant factors that influence the pear yield at the farmer level (n = 75). The proportion of each factor indicates the percentage of the number of farmers. Tis factor is highest than any other factors divided by the total number of interviewed farmers. Ca = calcium; K = potassium; Mg = magnesium; N = nitrogen; P = phosphorus.

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    • Search Google Scholar
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    • Search Google Scholar
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  • Rahman, M.H., Holmes, A.W., McCurran, A.G. & Saunders, S.J. 2011 Impact of management systems on soil properties and their relationships to kiwifruit quality Commun. Soil Sci. Plant Anal. 42 332 357 https://doi.org/10.1080/00103624.2011.538884

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  • Song, Y., Fan, L., Chen, H., Zhang, M.Y., Ma, Q.Q., Zhang, S.L. & Wu, J. 2014 Identifying genetic diversity and a preliminary core collection of Pyrus pyrifolia, cultivars by a genome-wide set of SSR markers Scientia Hort. 167 5 16 https://doi.org/10.1016/j.scienta.2013.12.005

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  • Sussy, M., Dahlin, A.S., Onyango, M.C., Oluoch, K.W. & Marstrop, H. 2020 Soil and management-related factors contributing to maize yield gaps in western Kenya Food Energy Secur. 9 1 1 17 https://doi.org/10.1002/fes3.189

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  • Tittonell, P. & Giller, K.E. 2013 When yield gaps are poverty traps: The paradigm of ecological intensification in African smallholder agriculture Field Crops Res. 143 76 90 https://doi.org/10.1016/j.fcr.2012.10.007

    • Search Google Scholar
    • Export Citation
  • Vandeplas, I., Vanlauwe, B., Driessens, L., Merckx, R. & Deckers, J. 2010 Reducing labour and input costs in soybean by smallholder farmers in south-western Kenya Field Crops Research 117 70 80 https://doi.org/10.1016/j.fcr.2010.02.002

    • Search Google Scholar
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  • von Liebig, J.F. 1840 Chemistry in its Application to Agriculture and Physiology Taylor and Walton London, UK

  • Wang, N.N., He, H.H., Lacroix, C., Morris, C., Liu, Z.D. & Ma, F.W. 2019 Soil fertility, leaf nutrients and their relationship in kiwifruit orchards of China’s central Shaanxi province Soil Sci. Plant Nutr. 65 4 369 376 https://doi.org/10.1080/00380768.2019.1624481

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  • Wang, N., Joost, W. & Zhang, F.S. 2016 Towards sustainable intensification of apple production in China - Yield gaps and nutrient use efficiency in apple farming systems J. Integrative Agr. 15 716 725 https://doi.org/10.1016/S2095-3119(15)61099-1

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  • Wu, H.Y., Kong, Y., Yao, Y.C., Bi, N.N., Qi, L.P. & Fu, Z.G. 2010 Effects of intercropping aromatic plants on soil microbial quantity and soil nutrients in pear orchard Zhongguo Nong Ye Ke Xue 43 1 140 150 (in Chinese)

    • Search Google Scholar
    • Export Citation
  • Zhang, D., Wang, C., Li, X.L., Yang, X.S., Zhao, L.B. & Xia, S.J. 2019a Correlation of production constraints with the yield gap of apple cropping systems in Luochuan County, China J. Integr. Agr. 18 8 1714 1725 https://doi.org/10.1016/s2095-3119(18)62098-2

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  • Zhang, W.F., Cao, G.X., Li, X.L., Zhang, H.Y., Wang, C., Liu, Q., Chen, X.P., Cui, Z.L., Shen, J.B., Jiang, R.F., Mi, G.H., Miao, Y.X., Zhang, F.S. & Dou, Z.X. 2016 Closing yield gaps in China by empowering smallholder farmers Nature 537 671 674 https://doi.org/10.1038/nature19368

    • Search Google Scholar
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  • Zhang, Y.C., Lei, H.M., Zhao, W.G., Shen, Y.J. & Xiao, D.P. 2018 Comparison of the water budget for the typical cropland and pear orchard ecosystems in the north China plain Agr. Water Mgt. 198 53 64 https://doi.org/10.1016/j.agwat.2017.12.027

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  • Zhou, G.F., Peng, S.A., Liu, Y.Z., Wei, Q.J., Han, J. & Islam, M.Z. 2014 The physiological and nutritional responses of seven different citrus rootstock seedlings to boron deficiency Trees (Berl.) 28 295 307 https://doi.org/10.1007/s00468-013-0949-y

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  • Zhu, L.W., Tang, X.M., Lin, T.Y., Zhou, S.S., Liu, P., Ye, Z.F., Wang, D.S. & Wu, Z.Y. 2017 First report of Fusarium root rot in Asian pear caused by Fusarium solani in China Plant Dis. 101 1 252 https://doi.org/10.1094/pdis-07-16-0998-pdn

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