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Yield gap and production constraints of mango (Mangifera indica) cropping systems in Tianyang County, China
Journal of Integrative Agriculture 2019, 18(8): 1726–1736 Available online at www.sciencedirect.com
ScienceDirect
RESEARCH ARTICLE
Yield gap and production constraints of mango (Mangifera indica) cropping systems in Tianyang County, China ZHANG Dong1, 2*, WANG Chong1, 2*, LI Xiao-lin1, 2 1 2
College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, P.R.China Key Laboratory of Plant-Soil Interactions, Minstry of Education, Beijing 100193, P.R.China
Abstract Mango is an important cash crop in the tropics and subtropics. Determining the yield gap of mango and production constraints can potentially promote the sustainable development of the mango industry. In this study, boundary line analysis based on survey data from 103 smallholder farmers and a yield gap model were used to determine the yield gap and production constraints in mango plantations in the northern mountain, central valley and southern mountains regions of Tianyang County, Guangxi, China. The results indicated that the yield of mango in three representing regions of Tianyang County, Northern Mountains, Central Valley and Southern Mountains, was 18.3, 17.0 and 15.4 t ha–1 yr–1, with an explainable yield gap of 10.9, 6.1 and 14.8 t ha–1 yr–1, respectively. Fertilization management, including fertilizer N, P2O5 and K2O application rates, and planting density were the main limiting factors of mango yield in all three regions. In addition, tree age influenced mango yield in the Northern Mountains (11.1%) and Central Valley (11.7%) regions. Irrigation time influenced mango yield in the Northern Mountains (9.9%) and Southern Mountains (12.2%). Based on a scenario analysis, the predicted yield would increase by up to 50%, and fertilizer N use would be reduced by as much as approximately 20%. An improved understanding of production constraints will aid in the development of management strategy measures to increase mango yield. Keywords: smallholder, boundary line analysis, yield gap model, fertilization, planting density
important fruit species worldwide (together with bananas,
1. Introduction Mango (Mangifera indica) is an important tropical cash crop in the world (Jahurul et al. 2015) and one of the five most
oranges, grapes, and apples). Mango is widely planted in the tropics and subtropics (Perez et al. 2016). China is the world’s second largest producer of mango, with a total harvest area of 1.5×105 ha (NBSC 2014). Mango in China is mainly grown in four production areas, which were located in Hainan, Guangxi, Yunnan and Sichuan, respectively (Ma
Received 12 June, 2018 Accepted 3 September, 2018 ZHANG Dong, E-mail: [email protected]; WANG Chong, E-mail: [email protected]; Correspondence LI Xiao-lin, Tel/Fax: +86-10-62731325, E-mail: [email protected] * These authors contributed equally to this study. © 2019 CAAS. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/). doi: 10.1016/S2095-3119(18)62099-4
et al. 2006). However, mango is cultivated by smallholder farmers in most areas where mango is the major source of income, and mango production is limited by the biotic and abiotic management factors among the smallholder farmers (Vandeplas et al. 2010). The average yield of mango in the Guangxi Youjiang Valley production area (the second largest production area in China) was 9.2 t ha–1 yr–1 (BSG 2014). However, the average yield of mango in China is
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8.3 t ha–1 yr–1, which is significantly lower than the average yields in the USA (15.4 t ha–1 yr–1), South Africa (18.4 t ha–1 yr–1) and Brazil (18.4 t ha–1 yr–1) (FAO 2017). Therefore, improving mango yield in China is of great significance to farmer income, farmer economic status and the entire mango industry. Previous studies have indicated that breeding methods and integrated pest management can increase mango yield (Shamili et al. 2012; Khan et al. 2015; Galdino et al. 2016). However, the factors that limit mango yield vary widely and include not only biophysical production constraints, such as soil fertility, plant disease and rootstocks (Zuazo et al. 2006; Dayal et al. 2016), but also abiotic production constraints such as climate change (Normand et al. 2015), exogenous chemical materials (Singh and Bhattacherjee 2005; Malik and Singh 2006; Ali et al. 2017) and salinity (Zuazo et al. 2004). These yield-limiting factors were identified from field experiments performed under controlled environments. Furthermore, the production patterns of different regions and different farmers also affect mango yield (Bie 2004; Mwatawala et al. 2015). However, little information is available on mango production constraints as identified from farm surveys at the regional scale, and the effects of different constraints on yield reduction have not been partitioned. Therefore, there is an urgent need to identify the main production constraints of mango on smallholder farms. Smallholder farmers, which are defined based on the size of their crop land or their income level (Tittonell and Giller 2013), are the main group of mango producers with intensive managements (Zhao et al. 2014), cultivating 600–700 plants per household in Tianyang County, Guangxi, China (FOSBC 2015). There are many limitations to agricultural production among smallholder farmers, including shortages of agricultural production assets, such as land, labour, and knowledge, and access to technologies (Vandeplas et al. 2010). In addition, smallholder farmers play many different roles in rural; they not only act as growers and breeders but also have different social roles, which prevents them from achieving elaborate management in crop production for uncertainty in time and space. Overall, these factors may limit crop production. Due to these various limitations, there is a large gap between the average yields obtained by farmers and the experimental yields obtained under the same environmental conditions. The yield gap is defined as the difference between the actual yield of a farmer and potential yield (Asten et al. 2003); for practical purposes, the potential yield can be defined as the maximum yield achieved by local farmers (van Ittersum et al. 2013). The analysis of yield gaps can aid the identification of production constraints (Bie 2004). The present study aims to: (i) identify the main production constraints on mango yield in a given production area and
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(ii) quantify the mango yield gap associated with each of these constraints. Multiple regression and boundary line analysis were used to explore the interactions between production constraints and yield of mango, and to quantify their contributions to the yield gap.
2. Materials and methods 2.1. Site description The study region was located in Tianyang County in central Youjiang River Valley, which is a main mango production area in China. Mango has been planted within the valley and on the mountains on either side for almost 30 years. The area lies approximately between latitudes 23°24´N and 23°58´N and between longitudes 106°36´E and 107°20´E at an altitude of 250–1 200 m above sea level. The mean annual temperature is 21.8–22.1°C, and the mean annual precipitation is 1 053–1 100 mm. The region has a typical subtropical monsoon climate. According to the difference of mean annual temperature and annual rainfall, the whole county was divided into three regions (hereafter referred to as the Northern Mountains, Central Valley and Southern Mountains). Farm surveys were carried out from July 16th to August 12th in 2015 among these three regions of Tianyang, and the study sites were distributed over the entire county.
2.2. Data collection and processing The mango household samples for survey were obtained randomly and participated in in-person questionnaire-based surveys with each household as a sample. Approximately 40 households were randomly selected from the major townships and villages (Fermont et al. 2009; Wang et al. 2015) with a minimum representation of 30 households in each region (Northern Mountains, Central Valley and Southern Mountains). Eliminating the household samples with incomplete production information and outliers, the complete and effective data from a total of 103 households (35 households in the Northern Mountains, 34 households in the Southern Mountains and 34 households in the Central Valley) were finally obtained during the survey for subsequent analysis. Structured interviews were used to collect data on the main production constraints. The following information was obtained by farmer recall: mango yield (t ha–1 yr–1, actual harvest yield in the past three years by recall from the farmer); fertilizer N, P2O5 and K2O application rates (kg ha–1, pure nutrient content calculated based on the amount of fertilizer input); farmer age (yr); education (yr); work experience (yr); labour (no., the number of people involved in mango production ); density (plants ha–1, calculated by row×column distance per tree); tree age (yr); plant height
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(m); variety quantity (no., all varieties in the orchard of the household); planting scale (plants per household); irrigation frequency (number, times of irrigation in a year, and 0 represents no irrigation performed by farmers). The yield, tree age, plant height, density, fertilization and irrigation management were the mean values of all the orchards in a household, who cultivated the same mango variety. The information was first recorded on the survey forms and then entered into a computer.
2.3. Statistical analysis Means of mango yield and production constraints were compared among regions using analysis of variance and means comparisons, which were performed with SPSS (IBM Statistics 20) for Windows and Microsoft Office 2010. All of the statistical results obtained in this study were considered significant at P<0.05. Boundary line analysis (Webb 1972), which has been used to quantify yield constraints and evaluate yield improvement potential (Shatar and McBratney 2004; Wairegi et al. 2010), was carried out to evaluate the relationship between mango yield gap and production constraints as well as the contributions of individual constraints to mango yield reduction. The followings were steps used to develop a boundary line: (i) Listing mango yield and corresponding production constraints. Scatter plots were conducted for a range of production constraints and mango yields. (ii) Identifying of the upper boundary points. The upper boundary points were built from scatter plots with the boundary line development system (BOLIDES) proposed by Schnug et al. (1996). (iii) Fitting of the curve to the upper boundary points. The regression lines were fitted separately for each region. For the rate of fertilizer P2O5 and K2O and education, the sigmoidal regression lines were fitted separately using the model developed by Fermont (2009): Yatt Y p= (1) 1+(Kexp(–Rx)) Where, Yatt is the highest yield obtained from the on-farm surveys in the surveyed region. Yp is the maximum predicted yield by the boundary line analysis. x is the independent variable, while K and R are constants. For other factors, polynomial (quadratic) regression lines (Schnug et al. 1996) were fitted through the boundary points to achieve the highest coefficient of determination (R2): Yp=a1x2+a2x+b (2) Where, a1, a2 and b are constants. The boundary line was used to obtain the maximum predicted yield (Yp) for each factor. The best-fit boundary line was obtained by minimizing the root mean squared error (RMSE) between Yp and the actual yield of an individual mango household. The yield gap between Yatt and Y p of each mango
household was calculated for each factor and then expressed as percentage of Yatt. For each region, the actual yield of an individual mango household was plotted against the Ymin. According to von Liebig’s law of minimum (Von Liebig 1840), the minimum predicted yield (Ymin) of the Yp was identified (Shatar and McBratney 2004). For each factor, the number of the household samples in which was the most limiting identified by the boundary line analysis, was calculated then expressed as percentage of the total households for each region. The explainable yield gap was defined as the difference between Yatt and Ymin, and the unexplained yield gap was defined as the difference between Ymin and the actual yield of the individual mango household. Scenario analysis was used to predict the development of mango industry in Tianyang County in 2020. We set two scenarios: Si (estimated as usual) and Sii (predicted in optimization strategy). For Si, according to development trend of the planting area, yield level and the rate of fertilizer N in the past 5 years (the data were from the Fruit Statistics Department in the county), we forecasted the development of planting area, yield and the rate of fertilizer N with the conventional management by 2020. For Sii, according to the contributions to the yield gap by the yield-limiting factors, we predicted the yield and the rate of fertilizer N based on the hypotheses that mango was cultivated by the farmers with optimization strategy, closing the yield gap by 80% all over the whole county.
3. Results 3.1. Mango yield gap The average yield of mango was 18.3 t ha–1 yr–1 in the Northern Mountains, 17.0 t ha–1 yr–1 in the Central Valley and 15.4 t ha–1 yr–1 in the Southern Mountains. There was no significant difference in mango yield between the Northern Mountains and Central Valley, whereas the average yield in the Southern Mountains was significantly lower than that of the other two regions (Fig. 1). The maximum yield was 27.0 t ha–1 yr–1 in both the Northern Mountains and Central Valley and 29.3 t ha–1 yr–1 in the Southern Mountains. Accordingly, the farmer-based yield gap (the gap between the average yield and the maximum yield obtained by farmers) was the highest in the Southern Mountains, followed by the Central Valley, and the lowest in the Northern Mountains.
3.2. Yield-related factors of mango production The results from the farmer surveys showed that the main yield-related factors of mango were the application rates of fertilizer N, P2O5 and K2O, farmer age, education, and work experience, labour, planting density, tree age,
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plant height, variety quantity, planting scale and irrigation times (Table 1). The rate of fertilizer P2O5 application was 290.9, 266.2 and 225.6 kg ha–1 in the Northern Mountains, Central Valley and Southern Mountains, respectively. The rate of P2O5 application in the Northern Mountains was significantly higher than that in the Southern Mountains, but there was no significant difference between either of these two regions and the Central Valley. The times of irrigation in the Central Valley was significantly higher than that in the Northern Mountains, whereas there was no significant difference between either of these two regions and the Southern Mountains. These results indicated that irrigation and fertilization management contributed to differences in mango yield among the three regions. The work experience of farmers was higher in the
Mango yield (t ha–1 yr–1)
35 30
Median Mean *
25 20 15 10 5 0
Northern Mountains
Central Valley
Southern Mountains
Fig. 1 Actual mango yields of farmers in 2015 in the three mango growing regions of Tianyang County, Guangxi, China. * indicates significance at the 5% level (P<0.05). The box boundaries indicate upper and lower quartiles, the whisker caps indicate 90th and 10th percentiles, and the points indicate the outliers. n=35 in the Northern Mountains, n=34 in the Southern Mountains and n=34 in the Central Valley.
Northern Mountains and Southern Mountains than in the Central Valley. The density of mango trees was significantly higher in the Southern Mountains than in the other two regions. The planting scale in the Northern Mountains was 950 plants per household, which was significantly higher than that in each of the other two regions. These results indicated that farmer experience, planting density and planting scale might have contributed to differences in mango yield among the three regions.
3.3. Yield-related factors and their contributions to yield gap The relationships between each of 13 yield-related factors and mango yield were evaluated with boundary line models in all three regions (Fig. 2; Appendix A). Separate boundary lines were constructed for each factor and region. Boundary lines were polynomial curves for most yield-related factors, specifically fertilizer N, farmer age, work experience, labour, planting density, tree age, plant height, variety quantity, planting scale and irrigation times. Yield increased until the attainable yield was reached and then declined. For the remaining three factors (fertilizer P2O5, fertilizer K2O and education), yield increased until the attainable yield was reached and then plateaued. Yield gap varied with factor and region according to the boundary line model (Fig. 3). In the Northern Mountains, the largest yield gap was explained by density (18.9%), followed by tree age (17.4%), plant height (15.7%), irrigation times (15.4%), fertilizer K2O (13.7%), planting scale (12.1%), farmer age (11.5%), labour (11.5%) and fertilizer P2O5 (10.6%). In the Central Valley, the yield gap explained by work experience was 23.8%, following by tree age and planting density (23.1 and 21.7%, respectively). In the Southern Mountains, the largest yield gap was explained by density (36.0%), followed by irrigation
Table 1 Detailed description of mango production factors in three representing regions of Tianyang County, Guangxi, China1) Factors Fertilizer N rate (kg ha–1) Fertilizer P2O5 rate (kg ha–1) Fertilizer K2O rate (kg ha–1) Farmer age (yr) Education year (yr) Work experience (yr) Labour (no.) Density (plants ha–1) Tree age (yr) Plant height (m) Variety quantity (no.) Planting scale (plants per household) Irrigation times (no.) 1)
NM (n=35) 316.9±19.7 a 290.9±18.2 a 403.6±23.26 a 45.4±0.5 a 9.0±0.4 a 10.6±0.8 b 2.2±1.3 a 697.3±41.9 b 11.7±0.9 a 2.6±0.1 a 2.0±0.2 a 950.0±79.4 a 0.5±0.1 b
CV (n=34) 279.5±16.2 a 266.2±16.7 ab 348.7±16.2 a 44.5±1.1 a 8.9±0.3 a 14.4±0.7 a 2.6±0.6 a 708.0±37.4 b 11.4±0.5 a 2.8±0.1 a 2.4±0.2 a 602.5±61.5 b 0.9±0.1 a
NM, Northern Mountains; CV, Central Valley; SM, Southern Mountains. Different lowercase letters indicate significant differences among the three regions at 5% level (P≤0.05).
SM (n=34) 304.5±23.7 a 225.6±15.8 b 365.6±24.6 a 44.5±1.1 a 9.2±0.4 a 12.0±0.9 b 2.4±1.6 a 903.1±56.7 a 10.3±0.9 a 2.6±0.1 a 2.1±0.1 a 534.3±51.8 b 0.6±0.1 ab
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Southern Mountains
Northern Mountains
B
40
Mango yield (t ha–1 yr–1)
Mango yield (t ha–1 yr–1)
A
30 20 10 0
0
100
200
300
400
500
600
40 30 20 10 0 100
700
Central Valley
200
Fertilizer N (kg ha–1)
Mango yield (t ha–1 yr–1)
Mango yield (t ha–1 yr–1)
30 25 20 15 10 5 0
500
600
200
400 600 Fertilizer K2O (kg ha–1)
30 20 10 0
800
30
30
Mango yield (t ha–1 yr–1)
F 35
25 20 15 10 5 0
5
10 15 Tree age (yr)
20
0
200 400 600 800 1 0001 2001 4001 600 Density (plants ha–1)
E 35 Mango yield (t ha–1 yr–1)
400
D 40
C 35
0
300
Fertilizer P2O5 (kg ha–1)
25
30
25 20 15 10 5 0
0
0.5
1.0 1.5 2.0 2.5 Irrigation times (no.)
3.0
3.5
Fig. 2 Relationship between mango yield and the main factors limiting production in three regions of Tianyang County, Guangxi, China predicted by boundary line analysis. The solid black line represents the boundary line for the Northern Mountains, the dashed black line represents the boundary line for the Central Valley, and the dotted black line represents the boundary line for the Southern Mountains. The relationships with other production factors are shown in Appendix A.
times, labour and planting scale (33.5, 33.3 and 32.3%,
1.2 and 1.0 t ha–1 yr–1, respectively (Fig. 4), indicating that
respectively).
the 13 evaluated factors were major influencing factors and that other unconsidered factors had little effect on yield. The
3.4. Explained yield gap and production constraints
boundary line analysis indicated that some of the production constraints of mango yield were limiting in all three regions,
The average explained yield gap was the largest in the
whereas others were limiting in only one or two regions.
Southern Mountains (14.8 t ha–1 yr–1), followed by the Central
Fertilization management, i.e., fertilizer N, P2O5 and K2O
yr ,
application, was the most limiting factor in all three regions.
respectively). The unexplained yield gap averaged 2.6,
Density was another major limiting factor in all three regions
Valley and Northern Mountains (10.9 and 6.1 t ha
–1
–1
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50
Yield gap (%)
40
Yield gap (%)
B
Yield gap (%)
C
Mean
Northern Mountains
30 20 10 0 –10 60
Central Valley
40
30 Predicted yield (t ha–1 yr–1)
Median A
y=27.0 t ha–1 yr–1
25
y=2x y=x
Explainable yield gap ymin
20
15 Unexplained yield gap 10 Northern Mountains Central Valley Southern Mountains
5 0
0
5
10 15 20 Observed yield (t ha–1 yr–1)
25
30
20 0 120 Southern Mountains 100 80 60 40 20 0 –20
Fe Fe rtil rt ize Fe ilize r N rti r P ra liz er 2 O te K 5 a F 2 O te Ed arm ra W uc er te or ati a k on ge ex y pe ea rie r n La ce bo D ur e Tr nsi Pl ee ty Va an a rie t h ge Pl ty q eig a u ht Irr ntin ant ig g ity at sc io a n le tim es
–40
Fig. 3 The explained yield gap for limiting factors, expressed as a percentage of the attained maximum yield in the Northern Mountains (A), Central Valley (B), and Southern Mountains (C) of Tianyang County, Guangxi, China. The box boundaries indicate upper and lower quartiles, the whisker caps indicate 90th and 10th percentiles, and the points indicate the outliers. n=35 in the Northern Mountains, n=34 in the Southern Mountains and n=34 in the Central Valley.
(12.1, 10.9 and 13.1%, respectively) (Fig. 5). Tree age influenced mango yield by 11.1 and 11.7% in the Northern Mountains and Central Valley. Irrigation times influenced mango yield by 9.9 and 12.2% in the Northern Mountains and Southern Mountains. Plant height, work experience and labour separately influenced the Northern Mountains, Central Valley and Southern Mountains.
3.5. Scenario analysis According to the yield variability explained by the limiting factors, an increase in mango yield was predicted for all three regions. We hypothesized that, if the optimization strategy for the main production factors can help to achieve 80% of
Fig. 4 Observed yield against the minimum predicted yield by the boundary line analysis for the three regions in Tianyang County, Guangxi, China. The horizontal line (y=27.0 t ha–1 yr–1) represents the maximum yield observed in the Northern Mountains, the maximum yields for the Central Valley (27.0 t ha–1 yr–1) and Southern Mountains (29.3 t ha–1 yr–1) are not shown. The above arrow indicates the difference between the minimum predicted yield and maximum attainable yield (defined as the explainable yield gap), and the lower arrow indicates the difference between the minimum predicted yield and the observed yield (defined as the unexplained yield gap) based on production constraints. The dotted lines are 1:2 and 1:1 diagonal lines depicting the relationship y=2x and y=x, respectively. n=35 in the Northern Mountains, n=34 in the Southern Mountains and n=34 in the Central Valley.
the contribution of production factors to mango yield, which was obtained by the boundary line analysis, fertilization management and planting density which contributed most to the yield gap with 12 and 10%, respectively. For other main production factors, the mango yield increased by 25% in Northern Mountains, and by 19 and 30% in the Central Valley and the Southern Mountains, respectively (Table 2). Overall, mango yield increased by a total of 47% in the Northern Mountains and by 41 and 52% in the Central Valley and the Southern Mountains, respectively (Table 2). The mango industry is in a stage of continuous development in Tianyang. For the Si, if the trends of previous years continue, the harvest area of mango will reach approximately 2.8×104 ha by 2020, and yield will increase by 15% (Fig. 6). In addition, under current trends, the application of production materials such as fertilizer N will increase by approximately 60% (average of the three regions) for the high yield of mango, in conflict with the needs of mango industrial development and national policy (zero growth in fertilizer use by 2020). For the Sii, by improving management strategies, such as employing optimal fertilizer and irrigation management, high-density planting and farmer training, the predicted yield under the scenario analysis would increase by approximately 50% (Table 2; Fig. 6). In
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Fertilizer N rate Fertilizer P2O5 rate
Southern Mountains
Fertilizer K2O rate Farmers age Education years Work experience
Central Valley
Labour Density Tree age Plant height
Northern Mountains
Variety quantity Planting scale 0
20
40
60
80
100
Irrigation times
Proportion (%)
Fig. 5 The production-limiting factors identified from the boundary line analysis and their corresponding contributions (proportions) in the three regions of Tianyang County, Guangxi, China. Each pattern indicates the percentage of the yield gap (Yp) based on an individual factor out of the total Yp of all factors. Table 2 Scenario analysis of the rate of yield increase in three representing regions of Tianyang County, Guangxi, China Regions All Northern Mountains
Central Valley Southern Mountains
1)
Limiting factors
Management strategy
Increasing rate (80% of Yp) (%)1）
Total rate (%)
Fertilizer Density Tree age Plant height Irrigation times Working experience Tree age Labour Planting scale Irrigation times
Optimal management High density planting Old tree improvement Dwarfing cultivar Optimized irrigation Farmer training Old tree improvement Adjustment structure Planting structure Optimized irrigation
12 10 9 8 8 10 9 10 10 10
12 10 25
19 30
Yp indicates the contribution to the mango yield gap by the production factors, expressed as the percentage of Yatt. For 80% of Yp, we assumed that implementation of a management strategy could reduce production constraints and close the yield gap. If mango was cultivated by the farmers with optimization strategy, closing the yield gap by 80% all over the whole county, the rate of increase can be calculated by the yield gaps obtained from the boundary line system.
addition, fertilizer N use would be reduced by 20% based on the optimal rate of the fertilizer N (300 kg ha–1 approximately) corresponding to the maximum yield as observed from the boundary line, which would be beneficial to the development of the mango industry.
4. Discussion 4.1. Yield gap analysis of mango production in Tianyang County Integrated application of nutrients has a beneficial effect on mango yield (Reddy et al. 2000; Gautam et al. 2012; Boora 2016). However, smallholder farmers typically improve crop yield by increasing the inputs of fertilizer (West et al. 2014). It was reported that overuse of nitrogen can lead to excessive vegetative growth of trees and aggravate the occurrence of
plant diseases and insect pests, which consequently reduce fruit yield (Ndabamenye et al. 2013). Additionally, a lack of nitrogen fertilizer can also reduce fruit weight and yield. In this study, fertilization management, including fertilizer N, P2O5 and K2O application, varied greatly in all three regions (Table 1; Fig. 2). Mango yield showed a trend of increasing first and then decreasing, with the increasing of the rate of fertilizer N (Fig. 2). Therefore, inappropriate N fertilization management appears to be the major factor limiting mango yield in Tianyang County. Planting density is an important factor affecting tree productivity. High-density orchards have been reported to generally be more productive than low-density orchards (Joglekar et al. 2013; Menzel and Le Lagadec 2017). Mango yield has been shown to increase with increasing planting density through the control of tree growth and improved light interception and distribution (Menzel and Le Lagadec
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Management strategy 3.0
Harvest area (104 ha) Mango yield (10 t ha–1) Fertilizer N rate (104 t)
2.5
Predicted yield (50% increase) Estimated yield (15% increase)
Value
2.0 1.5 1.0
Estimated rate (60% increase)
0.5
Predicted rate (20% decrease)
0
2011
2012
2013
2014
2015
2016 Year
2017
2018
2019
2020
Fig. 6 A conceptual framework of the mango industry in Tianyang County, Guangxi, China by 2020. According to the rate of development in the past few years, the harvest area of mango is likely to reach 2.8×104 ha, and the estimated yield would increase by 15%, but the estimated fertilizer N rate would increase by 60%. By implementing management strategies to reduce production constraints, the predicted yield would increase by 50%, but the predicted fertilizer N rate would decrease by 20%.
2017). In the traditional orchards of smallholder farmers, the planting density of fruit trees is low. This leads to large trees, which increase the difficulty of spraying and harvesting and reduce mango yield (Khan et al. 2015). In the present study, planting density varied greatly and had a strong influence on mango yield in all three regions. Improper planting density resulted in the decline in mango yield. Planting density is thus a major limiting factor in the three regions as evidenced from the boundary line analysis.
4.2. Yield gap analysis of mango in the Northern Mountains According to previous studies, tree age and plant height are important factors affecting yield. The mango yield is low at the early stage of bearing fruit (Fitchett et al. 2014) and increases with time, reaching the highest stage of fruit production in 10 to 20 years (Khan et al. 2015). Then, yield begins to decline in the later stage of fruiting, as the trees shade each other and begin ageing (Singh et al. 2010). However, as tree age increases, height increases, which leads to greater management challenges and affects mango yield. In the traditional planting patterns, tall trees have been found to be less productive than dwarfing cultivars (Menzel and Le Lagadec 2017). In this region, the age of most mango trees is less than 10 years, representing the early fruit stage, which is a limiting factor affecting yield. According to the boundary line analysis, yield decreased at plant heights greater than 2.5 m. However, tree age and plant height varied greatly in the Northern
Mountains, affecting the yield of mango. Compared with their explanatory power in the other two regions, tree age and plant height explained more of the variation in yield gap in the Northern Mountains. Therefore, tree age and plant height appear to be the main yield-limiting factors in the Northern Mountains. Irrigation can increase mango productivity. Previous studies have found that the application of specific irrigation strategies (Carr 2013; Gonzalez et al. 2004; Spreer et al. 2007; Wei et al. 2017) such as drip irrigation (Adak et al. 2017) can increase mango tree productivity and fruit yield. Deficit irrigation can affect tree growth and mango yield (Spreer et al. 2009). However, excessive irrigation can also lead to the reduction of mango yield, with effect on the flower bud differentiation and flowering (Gonzalez et al. 2004). In the present study, mountain terrain limited the implementation of irrigation operations. Furthermore, irrigation infrastructure is unpopular among smallholder farmers due to the cost of implementation. Thus, mango cultivation is typically a type of rain-fed agriculture in this region. Compared with irrigation management in the Central Valley, mountain areas have little irrigation management of mango. Moreover, the heterogeneous distribution of rainfall also affected mango yield in the mountain regions. The establishment of mountain irrigation facilities, such as reservoirs and potentially drip irrigation, would relieve water stress and increase mango yield. Thus, the results indicate that irrigation time is a main yield-limiting factor in the Northern Mountains.
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4.3. Yield gap analysis of mango in the Central Valley The managerial quality of working people is an important factor affecting mango production (Mwatawala et al. 2015). In the Central Valley, smallholder farmers lack advanced management techniques and effective training. Moreover, the ageing of farmers severely limits the upgrading of management technology, thus affecting the increase of mango yield. In addition, labour was also divided among multiple farm crops, such as tomato, which is another important crop in the Central Valley. Furthermore, mango production follows a traditional intensive cropping system, with a low degree of mechanization. The lack of work experience and labour might have contributed to the decline of mango yield. However, in Tianyang County, with the increase of work experience, the immediate management information from the government or enterprises will be excluded by the farmers. They were more willing to follow their own traditional management practices. Thus, it is easy to cause unreasonable management of fertilization, irrigation and pest and disease control, resulting in decline of mango yield. According to the state of labour in the farmer household of China, adult men are primarily responsible for the management practices in orchard. Women and the elderly are involved in the production system, with the expansion of planting scale and the lack of labour. However, in fact, women and the elderly are lack of experience for management practices, resulting in decline of mango yield. Tree age is also a yield-limiting factor in the Central Valley, similar to the finding in the Northern Mountains. Therefore, work experience, labour and tree age appear to be the main yield-limiting factors in the Central Valley.
4.4. Yield gap analysis of mango in the Southern Mountains A small planting scale limits the potential application of production technology by farmers (Misiko et al. 2011; Blackie 2012). Planting scale varied greatly in the Southern Mountains. The mango industry developed later in this region than in the other two regions; thus, the planting scale was smaller in this region, with 500 plants per household. Little labour was involved in mango production in this region. Therefore, planting scale is the main yield-limiting factor in the Southern Mountains. In addition, irrigation times and labour influenced mango yield in the Southern Mountains, as found for the Northern Mountains and Central Valley. In addition, deficient irrigation affected mango yield in the Southern Mountains, with an insufficient distribution of rainfall. Thus, fertilization and irrigation management, planting density, labour and planting scale were the major
production constraints in the Southern Mountains. However, the boundary line analysis is to study the effect of certain factor on the yield gap, and the relationship between these factors can’t be analyzed. This method can obtain the contribution of a certain factor to yield gap, but other factors were considered to be unexplained variables. The relationship between factors needs further study.
4.5. Sustainable development of mango in Tianyang To pursue economic benefits, the traditional farmers tended to pursue higher yield of mango, accompanied by the increase of production materials, especially fertilizer, which was an important factor causing the decline of the environmental quality. China has issued a policy to achieve zero growth in fertilizer use by 2020 (MOA 2015). In this study, excessive fertilization was common in Tianyang County. If the trends of previous years continue, Mango yield could be increased, but more fertilizer should be used. According to the Scenario analysis, it was possible to increase the yield and reduce the application of fertilizer by the management strategy, which was beneficial to the sustainable development of mango industry and improvement of the environmental quality.
5. Conclusion The present study identified a large yield gap of mango in Tianyang County. Fertilization management, including fertilizer N, P2O5 and K2O application rates, and planting density were the main limiting factors of mango yield in all three regions. In addition, tree age influenced mango yield in the Northern Mountains and Central Valley. Irrigation times influenced mango yield in the Northern Mountains and Southern Mountains. Plant height, work experience and labour individually influenced mango yield in the Northern Mountains, Central Valley and Southern Mountains, respectively. Based on the scenario analysis of improved management strategies, predicted yield would increase by as much as approximately 50% and N fertilizer use would decrease by as much as approximately 20%. Decreasing the yield gap is of great significance for achieving high yield and the sustainable development of mango.
Acknowledgements This work was funded by the National Key Research and Development Program of China (2016YFE0101100 and 2016YFD0201137) and the Innovative Group Grant of the National Science Foundation of China (31421092). Appendix associated with this paper can be available on
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http://www.ChinaAgriSci.com/V2/En/appendix.htm
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Executive Editor-in-Chief ZHANG Wei-li Managing editor SUN Lu-juan
ScienceDirect
RESEARCH ARTICLE
Yield gap and production constraints of mango (Mangifera indica) cropping systems in Tianyang County, China ZHANG Dong1, 2*, WANG Chong1, 2*, LI Xiao-lin1, 2 1 2
College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, P.R.China Key Laboratory of Plant-Soil Interactions, Minstry of Education, Beijing 100193, P.R.China
Abstract Mango is an important cash crop in the tropics and subtropics. Determining the yield gap of mango and production constraints can potentially promote the sustainable development of the mango industry. In this study, boundary line analysis based on survey data from 103 smallholder farmers and a yield gap model were used to determine the yield gap and production constraints in mango plantations in the northern mountain, central valley and southern mountains regions of Tianyang County, Guangxi, China. The results indicated that the yield of mango in three representing regions of Tianyang County, Northern Mountains, Central Valley and Southern Mountains, was 18.3, 17.0 and 15.4 t ha–1 yr–1, with an explainable yield gap of 10.9, 6.1 and 14.8 t ha–1 yr–1, respectively. Fertilization management, including fertilizer N, P2O5 and K2O application rates, and planting density were the main limiting factors of mango yield in all three regions. In addition, tree age influenced mango yield in the Northern Mountains (11.1%) and Central Valley (11.7%) regions. Irrigation time influenced mango yield in the Northern Mountains (9.9%) and Southern Mountains (12.2%). Based on a scenario analysis, the predicted yield would increase by up to 50%, and fertilizer N use would be reduced by as much as approximately 20%. An improved understanding of production constraints will aid in the development of management strategy measures to increase mango yield. Keywords: smallholder, boundary line analysis, yield gap model, fertilization, planting density
important fruit species worldwide (together with bananas,
1. Introduction Mango (Mangifera indica) is an important tropical cash crop in the world (Jahurul et al. 2015) and one of the five most
oranges, grapes, and apples). Mango is widely planted in the tropics and subtropics (Perez et al. 2016). China is the world’s second largest producer of mango, with a total harvest area of 1.5×105 ha (NBSC 2014). Mango in China is mainly grown in four production areas, which were located in Hainan, Guangxi, Yunnan and Sichuan, respectively (Ma
Received 12 June, 2018 Accepted 3 September, 2018 ZHANG Dong, E-mail: [email protected]; WANG Chong, E-mail: [email protected]; Correspondence LI Xiao-lin, Tel/Fax: +86-10-62731325, E-mail: [email protected] * These authors contributed equally to this study. © 2019 CAAS. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/). doi: 10.1016/S2095-3119(18)62099-4
et al. 2006). However, mango is cultivated by smallholder farmers in most areas where mango is the major source of income, and mango production is limited by the biotic and abiotic management factors among the smallholder farmers (Vandeplas et al. 2010). The average yield of mango in the Guangxi Youjiang Valley production area (the second largest production area in China) was 9.2 t ha–1 yr–1 (BSG 2014). However, the average yield of mango in China is
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8.3 t ha–1 yr–1, which is significantly lower than the average yields in the USA (15.4 t ha–1 yr–1), South Africa (18.4 t ha–1 yr–1) and Brazil (18.4 t ha–1 yr–1) (FAO 2017). Therefore, improving mango yield in China is of great significance to farmer income, farmer economic status and the entire mango industry. Previous studies have indicated that breeding methods and integrated pest management can increase mango yield (Shamili et al. 2012; Khan et al. 2015; Galdino et al. 2016). However, the factors that limit mango yield vary widely and include not only biophysical production constraints, such as soil fertility, plant disease and rootstocks (Zuazo et al. 2006; Dayal et al. 2016), but also abiotic production constraints such as climate change (Normand et al. 2015), exogenous chemical materials (Singh and Bhattacherjee 2005; Malik and Singh 2006; Ali et al. 2017) and salinity (Zuazo et al. 2004). These yield-limiting factors were identified from field experiments performed under controlled environments. Furthermore, the production patterns of different regions and different farmers also affect mango yield (Bie 2004; Mwatawala et al. 2015). However, little information is available on mango production constraints as identified from farm surveys at the regional scale, and the effects of different constraints on yield reduction have not been partitioned. Therefore, there is an urgent need to identify the main production constraints of mango on smallholder farms. Smallholder farmers, which are defined based on the size of their crop land or their income level (Tittonell and Giller 2013), are the main group of mango producers with intensive managements (Zhao et al. 2014), cultivating 600–700 plants per household in Tianyang County, Guangxi, China (FOSBC 2015). There are many limitations to agricultural production among smallholder farmers, including shortages of agricultural production assets, such as land, labour, and knowledge, and access to technologies (Vandeplas et al. 2010). In addition, smallholder farmers play many different roles in rural; they not only act as growers and breeders but also have different social roles, which prevents them from achieving elaborate management in crop production for uncertainty in time and space. Overall, these factors may limit crop production. Due to these various limitations, there is a large gap between the average yields obtained by farmers and the experimental yields obtained under the same environmental conditions. The yield gap is defined as the difference between the actual yield of a farmer and potential yield (Asten et al. 2003); for practical purposes, the potential yield can be defined as the maximum yield achieved by local farmers (van Ittersum et al. 2013). The analysis of yield gaps can aid the identification of production constraints (Bie 2004). The present study aims to: (i) identify the main production constraints on mango yield in a given production area and
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(ii) quantify the mango yield gap associated with each of these constraints. Multiple regression and boundary line analysis were used to explore the interactions between production constraints and yield of mango, and to quantify their contributions to the yield gap.
2. Materials and methods 2.1. Site description The study region was located in Tianyang County in central Youjiang River Valley, which is a main mango production area in China. Mango has been planted within the valley and on the mountains on either side for almost 30 years. The area lies approximately between latitudes 23°24´N and 23°58´N and between longitudes 106°36´E and 107°20´E at an altitude of 250–1 200 m above sea level. The mean annual temperature is 21.8–22.1°C, and the mean annual precipitation is 1 053–1 100 mm. The region has a typical subtropical monsoon climate. According to the difference of mean annual temperature and annual rainfall, the whole county was divided into three regions (hereafter referred to as the Northern Mountains, Central Valley and Southern Mountains). Farm surveys were carried out from July 16th to August 12th in 2015 among these three regions of Tianyang, and the study sites were distributed over the entire county.
2.2. Data collection and processing The mango household samples for survey were obtained randomly and participated in in-person questionnaire-based surveys with each household as a sample. Approximately 40 households were randomly selected from the major townships and villages (Fermont et al. 2009; Wang et al. 2015) with a minimum representation of 30 households in each region (Northern Mountains, Central Valley and Southern Mountains). Eliminating the household samples with incomplete production information and outliers, the complete and effective data from a total of 103 households (35 households in the Northern Mountains, 34 households in the Southern Mountains and 34 households in the Central Valley) were finally obtained during the survey for subsequent analysis. Structured interviews were used to collect data on the main production constraints. The following information was obtained by farmer recall: mango yield (t ha–1 yr–1, actual harvest yield in the past three years by recall from the farmer); fertilizer N, P2O5 and K2O application rates (kg ha–1, pure nutrient content calculated based on the amount of fertilizer input); farmer age (yr); education (yr); work experience (yr); labour (no., the number of people involved in mango production ); density (plants ha–1, calculated by row×column distance per tree); tree age (yr); plant height
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(m); variety quantity (no., all varieties in the orchard of the household); planting scale (plants per household); irrigation frequency (number, times of irrigation in a year, and 0 represents no irrigation performed by farmers). The yield, tree age, plant height, density, fertilization and irrigation management were the mean values of all the orchards in a household, who cultivated the same mango variety. The information was first recorded on the survey forms and then entered into a computer.
2.3. Statistical analysis Means of mango yield and production constraints were compared among regions using analysis of variance and means comparisons, which were performed with SPSS (IBM Statistics 20) for Windows and Microsoft Office 2010. All of the statistical results obtained in this study were considered significant at P<0.05. Boundary line analysis (Webb 1972), which has been used to quantify yield constraints and evaluate yield improvement potential (Shatar and McBratney 2004; Wairegi et al. 2010), was carried out to evaluate the relationship between mango yield gap and production constraints as well as the contributions of individual constraints to mango yield reduction. The followings were steps used to develop a boundary line: (i) Listing mango yield and corresponding production constraints. Scatter plots were conducted for a range of production constraints and mango yields. (ii) Identifying of the upper boundary points. The upper boundary points were built from scatter plots with the boundary line development system (BOLIDES) proposed by Schnug et al. (1996). (iii) Fitting of the curve to the upper boundary points. The regression lines were fitted separately for each region. For the rate of fertilizer P2O5 and K2O and education, the sigmoidal regression lines were fitted separately using the model developed by Fermont (2009): Yatt Y p= (1) 1+(Kexp(–Rx)) Where, Yatt is the highest yield obtained from the on-farm surveys in the surveyed region. Yp is the maximum predicted yield by the boundary line analysis. x is the independent variable, while K and R are constants. For other factors, polynomial (quadratic) regression lines (Schnug et al. 1996) were fitted through the boundary points to achieve the highest coefficient of determination (R2): Yp=a1x2+a2x+b (2) Where, a1, a2 and b are constants. The boundary line was used to obtain the maximum predicted yield (Yp) for each factor. The best-fit boundary line was obtained by minimizing the root mean squared error (RMSE) between Yp and the actual yield of an individual mango household. The yield gap between Yatt and Y p of each mango
household was calculated for each factor and then expressed as percentage of Yatt. For each region, the actual yield of an individual mango household was plotted against the Ymin. According to von Liebig’s law of minimum (Von Liebig 1840), the minimum predicted yield (Ymin) of the Yp was identified (Shatar and McBratney 2004). For each factor, the number of the household samples in which was the most limiting identified by the boundary line analysis, was calculated then expressed as percentage of the total households for each region. The explainable yield gap was defined as the difference between Yatt and Ymin, and the unexplained yield gap was defined as the difference between Ymin and the actual yield of the individual mango household. Scenario analysis was used to predict the development of mango industry in Tianyang County in 2020. We set two scenarios: Si (estimated as usual) and Sii (predicted in optimization strategy). For Si, according to development trend of the planting area, yield level and the rate of fertilizer N in the past 5 years (the data were from the Fruit Statistics Department in the county), we forecasted the development of planting area, yield and the rate of fertilizer N with the conventional management by 2020. For Sii, according to the contributions to the yield gap by the yield-limiting factors, we predicted the yield and the rate of fertilizer N based on the hypotheses that mango was cultivated by the farmers with optimization strategy, closing the yield gap by 80% all over the whole county.
3. Results 3.1. Mango yield gap The average yield of mango was 18.3 t ha–1 yr–1 in the Northern Mountains, 17.0 t ha–1 yr–1 in the Central Valley and 15.4 t ha–1 yr–1 in the Southern Mountains. There was no significant difference in mango yield between the Northern Mountains and Central Valley, whereas the average yield in the Southern Mountains was significantly lower than that of the other two regions (Fig. 1). The maximum yield was 27.0 t ha–1 yr–1 in both the Northern Mountains and Central Valley and 29.3 t ha–1 yr–1 in the Southern Mountains. Accordingly, the farmer-based yield gap (the gap between the average yield and the maximum yield obtained by farmers) was the highest in the Southern Mountains, followed by the Central Valley, and the lowest in the Northern Mountains.
3.2. Yield-related factors of mango production The results from the farmer surveys showed that the main yield-related factors of mango were the application rates of fertilizer N, P2O5 and K2O, farmer age, education, and work experience, labour, planting density, tree age,
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plant height, variety quantity, planting scale and irrigation times (Table 1). The rate of fertilizer P2O5 application was 290.9, 266.2 and 225.6 kg ha–1 in the Northern Mountains, Central Valley and Southern Mountains, respectively. The rate of P2O5 application in the Northern Mountains was significantly higher than that in the Southern Mountains, but there was no significant difference between either of these two regions and the Central Valley. The times of irrigation in the Central Valley was significantly higher than that in the Northern Mountains, whereas there was no significant difference between either of these two regions and the Southern Mountains. These results indicated that irrigation and fertilization management contributed to differences in mango yield among the three regions. The work experience of farmers was higher in the
Mango yield (t ha–1 yr–1)
35 30
Median Mean *
25 20 15 10 5 0
Northern Mountains
Central Valley
Southern Mountains
Fig. 1 Actual mango yields of farmers in 2015 in the three mango growing regions of Tianyang County, Guangxi, China. * indicates significance at the 5% level (P<0.05). The box boundaries indicate upper and lower quartiles, the whisker caps indicate 90th and 10th percentiles, and the points indicate the outliers. n=35 in the Northern Mountains, n=34 in the Southern Mountains and n=34 in the Central Valley.
Northern Mountains and Southern Mountains than in the Central Valley. The density of mango trees was significantly higher in the Southern Mountains than in the other two regions. The planting scale in the Northern Mountains was 950 plants per household, which was significantly higher than that in each of the other two regions. These results indicated that farmer experience, planting density and planting scale might have contributed to differences in mango yield among the three regions.
3.3. Yield-related factors and their contributions to yield gap The relationships between each of 13 yield-related factors and mango yield were evaluated with boundary line models in all three regions (Fig. 2; Appendix A). Separate boundary lines were constructed for each factor and region. Boundary lines were polynomial curves for most yield-related factors, specifically fertilizer N, farmer age, work experience, labour, planting density, tree age, plant height, variety quantity, planting scale and irrigation times. Yield increased until the attainable yield was reached and then declined. For the remaining three factors (fertilizer P2O5, fertilizer K2O and education), yield increased until the attainable yield was reached and then plateaued. Yield gap varied with factor and region according to the boundary line model (Fig. 3). In the Northern Mountains, the largest yield gap was explained by density (18.9%), followed by tree age (17.4%), plant height (15.7%), irrigation times (15.4%), fertilizer K2O (13.7%), planting scale (12.1%), farmer age (11.5%), labour (11.5%) and fertilizer P2O5 (10.6%). In the Central Valley, the yield gap explained by work experience was 23.8%, following by tree age and planting density (23.1 and 21.7%, respectively). In the Southern Mountains, the largest yield gap was explained by density (36.0%), followed by irrigation
Table 1 Detailed description of mango production factors in three representing regions of Tianyang County, Guangxi, China1) Factors Fertilizer N rate (kg ha–1) Fertilizer P2O5 rate (kg ha–1) Fertilizer K2O rate (kg ha–1) Farmer age (yr) Education year (yr) Work experience (yr) Labour (no.) Density (plants ha–1) Tree age (yr) Plant height (m) Variety quantity (no.) Planting scale (plants per household) Irrigation times (no.) 1)
NM (n=35) 316.9±19.7 a 290.9±18.2 a 403.6±23.26 a 45.4±0.5 a 9.0±0.4 a 10.6±0.8 b 2.2±1.3 a 697.3±41.9 b 11.7±0.9 a 2.6±0.1 a 2.0±0.2 a 950.0±79.4 a 0.5±0.1 b
CV (n=34) 279.5±16.2 a 266.2±16.7 ab 348.7±16.2 a 44.5±1.1 a 8.9±0.3 a 14.4±0.7 a 2.6±0.6 a 708.0±37.4 b 11.4±0.5 a 2.8±0.1 a 2.4±0.2 a 602.5±61.5 b 0.9±0.1 a
NM, Northern Mountains; CV, Central Valley; SM, Southern Mountains. Different lowercase letters indicate significant differences among the three regions at 5% level (P≤0.05).
SM (n=34) 304.5±23.7 a 225.6±15.8 b 365.6±24.6 a 44.5±1.1 a 9.2±0.4 a 12.0±0.9 b 2.4±1.6 a 903.1±56.7 a 10.3±0.9 a 2.6±0.1 a 2.1±0.1 a 534.3±51.8 b 0.6±0.1 ab
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Southern Mountains
Northern Mountains
B
40
Mango yield (t ha–1 yr–1)
Mango yield (t ha–1 yr–1)
A
30 20 10 0
0
100
200
300
400
500
600
40 30 20 10 0 100
700
Central Valley
200
Fertilizer N (kg ha–1)
Mango yield (t ha–1 yr–1)
Mango yield (t ha–1 yr–1)
30 25 20 15 10 5 0
500
600
200
400 600 Fertilizer K2O (kg ha–1)
30 20 10 0
800
30
30
Mango yield (t ha–1 yr–1)
F 35
25 20 15 10 5 0
5
10 15 Tree age (yr)
20
0
200 400 600 800 1 0001 2001 4001 600 Density (plants ha–1)
E 35 Mango yield (t ha–1 yr–1)
400
D 40
C 35
0
300
Fertilizer P2O5 (kg ha–1)
25
30
25 20 15 10 5 0
0
0.5
1.0 1.5 2.0 2.5 Irrigation times (no.)
3.0
3.5
Fig. 2 Relationship between mango yield and the main factors limiting production in three regions of Tianyang County, Guangxi, China predicted by boundary line analysis. The solid black line represents the boundary line for the Northern Mountains, the dashed black line represents the boundary line for the Central Valley, and the dotted black line represents the boundary line for the Southern Mountains. The relationships with other production factors are shown in Appendix A.
times, labour and planting scale (33.5, 33.3 and 32.3%,
1.2 and 1.0 t ha–1 yr–1, respectively (Fig. 4), indicating that
respectively).
the 13 evaluated factors were major influencing factors and that other unconsidered factors had little effect on yield. The
3.4. Explained yield gap and production constraints
boundary line analysis indicated that some of the production constraints of mango yield were limiting in all three regions,
The average explained yield gap was the largest in the
whereas others were limiting in only one or two regions.
Southern Mountains (14.8 t ha–1 yr–1), followed by the Central
Fertilization management, i.e., fertilizer N, P2O5 and K2O
yr ,
application, was the most limiting factor in all three regions.
respectively). The unexplained yield gap averaged 2.6,
Density was another major limiting factor in all three regions
Valley and Northern Mountains (10.9 and 6.1 t ha
–1
–1
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50
Yield gap (%)
40
Yield gap (%)
B
Yield gap (%)
C
Mean
Northern Mountains
30 20 10 0 –10 60
Central Valley
40
30 Predicted yield (t ha–1 yr–1)
Median A
y=27.0 t ha–1 yr–1
25
y=2x y=x
Explainable yield gap ymin
20
15 Unexplained yield gap 10 Northern Mountains Central Valley Southern Mountains
5 0
0
5
10 15 20 Observed yield (t ha–1 yr–1)
25
30
20 0 120 Southern Mountains 100 80 60 40 20 0 –20
Fe Fe rtil rt ize Fe ilize r N rti r P ra liz er 2 O te K 5 a F 2 O te Ed arm ra W uc er te or ati a k on ge ex y pe ea rie r n La ce bo D ur e Tr nsi Pl ee ty Va an a rie t h ge Pl ty q eig a u ht Irr ntin ant ig g ity at sc io a n le tim es
–40
Fig. 3 The explained yield gap for limiting factors, expressed as a percentage of the attained maximum yield in the Northern Mountains (A), Central Valley (B), and Southern Mountains (C) of Tianyang County, Guangxi, China. The box boundaries indicate upper and lower quartiles, the whisker caps indicate 90th and 10th percentiles, and the points indicate the outliers. n=35 in the Northern Mountains, n=34 in the Southern Mountains and n=34 in the Central Valley.
(12.1, 10.9 and 13.1%, respectively) (Fig. 5). Tree age influenced mango yield by 11.1 and 11.7% in the Northern Mountains and Central Valley. Irrigation times influenced mango yield by 9.9 and 12.2% in the Northern Mountains and Southern Mountains. Plant height, work experience and labour separately influenced the Northern Mountains, Central Valley and Southern Mountains.
3.5. Scenario analysis According to the yield variability explained by the limiting factors, an increase in mango yield was predicted for all three regions. We hypothesized that, if the optimization strategy for the main production factors can help to achieve 80% of
Fig. 4 Observed yield against the minimum predicted yield by the boundary line analysis for the three regions in Tianyang County, Guangxi, China. The horizontal line (y=27.0 t ha–1 yr–1) represents the maximum yield observed in the Northern Mountains, the maximum yields for the Central Valley (27.0 t ha–1 yr–1) and Southern Mountains (29.3 t ha–1 yr–1) are not shown. The above arrow indicates the difference between the minimum predicted yield and maximum attainable yield (defined as the explainable yield gap), and the lower arrow indicates the difference between the minimum predicted yield and the observed yield (defined as the unexplained yield gap) based on production constraints. The dotted lines are 1:2 and 1:1 diagonal lines depicting the relationship y=2x and y=x, respectively. n=35 in the Northern Mountains, n=34 in the Southern Mountains and n=34 in the Central Valley.
the contribution of production factors to mango yield, which was obtained by the boundary line analysis, fertilization management and planting density which contributed most to the yield gap with 12 and 10%, respectively. For other main production factors, the mango yield increased by 25% in Northern Mountains, and by 19 and 30% in the Central Valley and the Southern Mountains, respectively (Table 2). Overall, mango yield increased by a total of 47% in the Northern Mountains and by 41 and 52% in the Central Valley and the Southern Mountains, respectively (Table 2). The mango industry is in a stage of continuous development in Tianyang. For the Si, if the trends of previous years continue, the harvest area of mango will reach approximately 2.8×104 ha by 2020, and yield will increase by 15% (Fig. 6). In addition, under current trends, the application of production materials such as fertilizer N will increase by approximately 60% (average of the three regions) for the high yield of mango, in conflict with the needs of mango industrial development and national policy (zero growth in fertilizer use by 2020). For the Sii, by improving management strategies, such as employing optimal fertilizer and irrigation management, high-density planting and farmer training, the predicted yield under the scenario analysis would increase by approximately 50% (Table 2; Fig. 6). In
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Fertilizer N rate Fertilizer P2O5 rate
Southern Mountains
Fertilizer K2O rate Farmers age Education years Work experience
Central Valley
Labour Density Tree age Plant height
Northern Mountains
Variety quantity Planting scale 0
20
40
60
80
100
Irrigation times
Proportion (%)
Fig. 5 The production-limiting factors identified from the boundary line analysis and their corresponding contributions (proportions) in the three regions of Tianyang County, Guangxi, China. Each pattern indicates the percentage of the yield gap (Yp) based on an individual factor out of the total Yp of all factors. Table 2 Scenario analysis of the rate of yield increase in three representing regions of Tianyang County, Guangxi, China Regions All Northern Mountains
Central Valley Southern Mountains
1)
Limiting factors
Management strategy
Increasing rate (80% of Yp) (%)1）
Total rate (%)
Fertilizer Density Tree age Plant height Irrigation times Working experience Tree age Labour Planting scale Irrigation times
Optimal management High density planting Old tree improvement Dwarfing cultivar Optimized irrigation Farmer training Old tree improvement Adjustment structure Planting structure Optimized irrigation
12 10 9 8 8 10 9 10 10 10
12 10 25
19 30
Yp indicates the contribution to the mango yield gap by the production factors, expressed as the percentage of Yatt. For 80% of Yp, we assumed that implementation of a management strategy could reduce production constraints and close the yield gap. If mango was cultivated by the farmers with optimization strategy, closing the yield gap by 80% all over the whole county, the rate of increase can be calculated by the yield gaps obtained from the boundary line system.
addition, fertilizer N use would be reduced by 20% based on the optimal rate of the fertilizer N (300 kg ha–1 approximately) corresponding to the maximum yield as observed from the boundary line, which would be beneficial to the development of the mango industry.
4. Discussion 4.1. Yield gap analysis of mango production in Tianyang County Integrated application of nutrients has a beneficial effect on mango yield (Reddy et al. 2000; Gautam et al. 2012; Boora 2016). However, smallholder farmers typically improve crop yield by increasing the inputs of fertilizer (West et al. 2014). It was reported that overuse of nitrogen can lead to excessive vegetative growth of trees and aggravate the occurrence of
plant diseases and insect pests, which consequently reduce fruit yield (Ndabamenye et al. 2013). Additionally, a lack of nitrogen fertilizer can also reduce fruit weight and yield. In this study, fertilization management, including fertilizer N, P2O5 and K2O application, varied greatly in all three regions (Table 1; Fig. 2). Mango yield showed a trend of increasing first and then decreasing, with the increasing of the rate of fertilizer N (Fig. 2). Therefore, inappropriate N fertilization management appears to be the major factor limiting mango yield in Tianyang County. Planting density is an important factor affecting tree productivity. High-density orchards have been reported to generally be more productive than low-density orchards (Joglekar et al. 2013; Menzel and Le Lagadec 2017). Mango yield has been shown to increase with increasing planting density through the control of tree growth and improved light interception and distribution (Menzel and Le Lagadec
ZHANG Dong et al. Journal of Integrative Agriculture 2019, 18(8): 1726–1736
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Management strategy 3.0
Harvest area (104 ha) Mango yield (10 t ha–1) Fertilizer N rate (104 t)
2.5
Predicted yield (50% increase) Estimated yield (15% increase)
Value
2.0 1.5 1.0
Estimated rate (60% increase)
0.5
Predicted rate (20% decrease)
0
2011
2012
2013
2014
2015
2016 Year
2017
2018
2019
2020
Fig. 6 A conceptual framework of the mango industry in Tianyang County, Guangxi, China by 2020. According to the rate of development in the past few years, the harvest area of mango is likely to reach 2.8×104 ha, and the estimated yield would increase by 15%, but the estimated fertilizer N rate would increase by 60%. By implementing management strategies to reduce production constraints, the predicted yield would increase by 50%, but the predicted fertilizer N rate would decrease by 20%.
2017). In the traditional orchards of smallholder farmers, the planting density of fruit trees is low. This leads to large trees, which increase the difficulty of spraying and harvesting and reduce mango yield (Khan et al. 2015). In the present study, planting density varied greatly and had a strong influence on mango yield in all three regions. Improper planting density resulted in the decline in mango yield. Planting density is thus a major limiting factor in the three regions as evidenced from the boundary line analysis.
4.2. Yield gap analysis of mango in the Northern Mountains According to previous studies, tree age and plant height are important factors affecting yield. The mango yield is low at the early stage of bearing fruit (Fitchett et al. 2014) and increases with time, reaching the highest stage of fruit production in 10 to 20 years (Khan et al. 2015). Then, yield begins to decline in the later stage of fruiting, as the trees shade each other and begin ageing (Singh et al. 2010). However, as tree age increases, height increases, which leads to greater management challenges and affects mango yield. In the traditional planting patterns, tall trees have been found to be less productive than dwarfing cultivars (Menzel and Le Lagadec 2017). In this region, the age of most mango trees is less than 10 years, representing the early fruit stage, which is a limiting factor affecting yield. According to the boundary line analysis, yield decreased at plant heights greater than 2.5 m. However, tree age and plant height varied greatly in the Northern
Mountains, affecting the yield of mango. Compared with their explanatory power in the other two regions, tree age and plant height explained more of the variation in yield gap in the Northern Mountains. Therefore, tree age and plant height appear to be the main yield-limiting factors in the Northern Mountains. Irrigation can increase mango productivity. Previous studies have found that the application of specific irrigation strategies (Carr 2013; Gonzalez et al. 2004; Spreer et al. 2007; Wei et al. 2017) such as drip irrigation (Adak et al. 2017) can increase mango tree productivity and fruit yield. Deficit irrigation can affect tree growth and mango yield (Spreer et al. 2009). However, excessive irrigation can also lead to the reduction of mango yield, with effect on the flower bud differentiation and flowering (Gonzalez et al. 2004). In the present study, mountain terrain limited the implementation of irrigation operations. Furthermore, irrigation infrastructure is unpopular among smallholder farmers due to the cost of implementation. Thus, mango cultivation is typically a type of rain-fed agriculture in this region. Compared with irrigation management in the Central Valley, mountain areas have little irrigation management of mango. Moreover, the heterogeneous distribution of rainfall also affected mango yield in the mountain regions. The establishment of mountain irrigation facilities, such as reservoirs and potentially drip irrigation, would relieve water stress and increase mango yield. Thus, the results indicate that irrigation time is a main yield-limiting factor in the Northern Mountains.
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4.3. Yield gap analysis of mango in the Central Valley The managerial quality of working people is an important factor affecting mango production (Mwatawala et al. 2015). In the Central Valley, smallholder farmers lack advanced management techniques and effective training. Moreover, the ageing of farmers severely limits the upgrading of management technology, thus affecting the increase of mango yield. In addition, labour was also divided among multiple farm crops, such as tomato, which is another important crop in the Central Valley. Furthermore, mango production follows a traditional intensive cropping system, with a low degree of mechanization. The lack of work experience and labour might have contributed to the decline of mango yield. However, in Tianyang County, with the increase of work experience, the immediate management information from the government or enterprises will be excluded by the farmers. They were more willing to follow their own traditional management practices. Thus, it is easy to cause unreasonable management of fertilization, irrigation and pest and disease control, resulting in decline of mango yield. According to the state of labour in the farmer household of China, adult men are primarily responsible for the management practices in orchard. Women and the elderly are involved in the production system, with the expansion of planting scale and the lack of labour. However, in fact, women and the elderly are lack of experience for management practices, resulting in decline of mango yield. Tree age is also a yield-limiting factor in the Central Valley, similar to the finding in the Northern Mountains. Therefore, work experience, labour and tree age appear to be the main yield-limiting factors in the Central Valley.
4.4. Yield gap analysis of mango in the Southern Mountains A small planting scale limits the potential application of production technology by farmers (Misiko et al. 2011; Blackie 2012). Planting scale varied greatly in the Southern Mountains. The mango industry developed later in this region than in the other two regions; thus, the planting scale was smaller in this region, with 500 plants per household. Little labour was involved in mango production in this region. Therefore, planting scale is the main yield-limiting factor in the Southern Mountains. In addition, irrigation times and labour influenced mango yield in the Southern Mountains, as found for the Northern Mountains and Central Valley. In addition, deficient irrigation affected mango yield in the Southern Mountains, with an insufficient distribution of rainfall. Thus, fertilization and irrigation management, planting density, labour and planting scale were the major
production constraints in the Southern Mountains. However, the boundary line analysis is to study the effect of certain factor on the yield gap, and the relationship between these factors can’t be analyzed. This method can obtain the contribution of a certain factor to yield gap, but other factors were considered to be unexplained variables. The relationship between factors needs further study.
4.5. Sustainable development of mango in Tianyang To pursue economic benefits, the traditional farmers tended to pursue higher yield of mango, accompanied by the increase of production materials, especially fertilizer, which was an important factor causing the decline of the environmental quality. China has issued a policy to achieve zero growth in fertilizer use by 2020 (MOA 2015). In this study, excessive fertilization was common in Tianyang County. If the trends of previous years continue, Mango yield could be increased, but more fertilizer should be used. According to the Scenario analysis, it was possible to increase the yield and reduce the application of fertilizer by the management strategy, which was beneficial to the sustainable development of mango industry and improvement of the environmental quality.
5. Conclusion The present study identified a large yield gap of mango in Tianyang County. Fertilization management, including fertilizer N, P2O5 and K2O application rates, and planting density were the main limiting factors of mango yield in all three regions. In addition, tree age influenced mango yield in the Northern Mountains and Central Valley. Irrigation times influenced mango yield in the Northern Mountains and Southern Mountains. Plant height, work experience and labour individually influenced mango yield in the Northern Mountains, Central Valley and Southern Mountains, respectively. Based on the scenario analysis of improved management strategies, predicted yield would increase by as much as approximately 50% and N fertilizer use would decrease by as much as approximately 20%. Decreasing the yield gap is of great significance for achieving high yield and the sustainable development of mango.
Acknowledgements This work was funded by the National Key Research and Development Program of China (2016YFE0101100 and 2016YFD0201137) and the Innovative Group Grant of the National Science Foundation of China (31421092). Appendix associated with this paper can be available on
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http://www.ChinaAgriSci.com/V2/En/appendix.htm
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