Identifying the limiting factors driving the winter wheat yield gap on smallholder farms by agronomic diagnosis in North China Plain

Journal of Integrative Agriculture 2019, 18(8): 1701–1713 Available online at www.sciencedirect.com

ScienceDirect

RESEARCH ARTICLE

Identifying the limiting factors driving the winter wheat yield gap on smallholder farms by agronomic diagnosis in North China Plain CAO Hong-zhu1, LI Ya-nan1, CHEN Guang-feng2, 3, CHEN Dong-dong1, QU Hong-rui1, MA Wen-qi1 1 2 3

College of Resources and Environmental Science, Hebei Agricultural University, Baoding 071000, P.R.China College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, P.R.China National Agricultural Technology Extension and Service Center, Ministry of Agriculture and Rural Affairs, Beijing 100125, P.R.China

Abstract North China Plain (NCP) is the primary winter wheat production region in China, characterized by smallholder farming systems. Whereas the winter wheat average yield of smallholder farmers is currently low, the yield potential and limiting factors driving the current yield gap remain unclear. Therefore, increasing the wheat yield in NCP is essential for the national food security. This study monitored wheat yield, management practices and soil nutrient data in 132 farmers’ fields of Xushui County, Baoding City, Hebei Province during 2014–2016. These data were analyzed using variance and path analysis to determine the yield gap and the contribution of yield components (i.e., spikes per hectare, grain number per spike and 1 000-grain weight) to wheat yield. Then, the limiting factors of yield components and the optimizing strategies were identified by a boundary line approach. The results showed that the attainable potential yield for winter wheat was 10 514 kg ha–1. The yield gaps varied strongly between three yield groups (i.e., high, middle and low), which were divided by yield level and contained 44 farmers in each group, and amounted to 2 493, 1 636 and 814 kg ha–1, respectively. For the three yield components, only spikes per hectare was significantly different (P<0.01) among the three yield groups. For all 132 farmers’ fields, correlation between yield and spikes per hectare (r=0.51, P<0.01), was significantly positive, while correlations with grain number per spike (r=–0.16) and 1 000-grain weight (r=–0.10) were not significant. The path analysis also showed that the spikes per hectare of winter wheat were the most important component to the wheat yield. Boundary line analysis showed that seeding date was the most limiting factor of spikes per hectare with the highest contribution rate (26.7%), followed by basal N input (22.1%) and seeding rate (14.5%), which indicated that management factors in the seeding step were the most important for affecting spikes per hectare. For desired spikes per hectare (>6.598×106 ha–1), the seeding rate should range from 210–300 kg ha–1, seeding date should range from 3th to 8th October, and basal N input should range from 90–180 kg ha–1. Compared to these reasonable ranges of management measures, most of the farmers’ practices were not suitable, and both lower and higher levels of management existed. It is concluded that the strategies for optimizing yield components could be achieved by improving wheat seeding quality and optimizing farmers’ nutrient management practices in the NCP.

Received 12 June, 2018 Accepted 12 November, 2018 CAO Hong-zhu, Mobile: +86-15231208163, E-mail: 991746237@ qq.com; Correspondence MA Wen-qi, Tel: +86-312-7528220, E-mail: [email protected] © 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(19)62574-8

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Keywords: yield gaps, smallholder, limiting factors, path analysis, boundary line analysis

1. Introduction Sustainably feeding the world’s growing population is a great challenge (Tilman et al. 2002, 2011; Godfray et al. 2010), and closing the yield gaps especially on smallholder farms (Lobell et al. 2009; Mueller et al. 2012; Van Ittersum et al. 2013) is a vital strategy to address that challenge (Foley et al. 2011). It is estimated that China accounts for 193 million small farms (each less than 2 ha) (Hazell et al. 2007), and these smallholder farmers are the main body and the main contributor for food security in China. Winter wheat is one of three main food crops in China, and North China Plain (NCP) is the typical wheat growing region in China, contributing 50% of the nation’s wheat production (NBSC 2012). However, the yields from smallholder farmers are relatively low (Mueller et al. 2012), with large variations (Lu and Fan 2013). Therefore, increasing wheat yield of smallholder farmers in the NCP is very important for sustainable food production. An effective method is to close the yield gap, which can be defined as the difference between the maximum and average farmer’s yield (Lobell et al. 2009), by improved management. The biophysical and socioeconomic factors which commonly affect wheat growth and yields in farmers’ fields, include nutrient imbalances, soil health problems, water stress, lack of knowledge on best management practices and limited time devoted to agricultural activities (Lobell et al. 2009). For example, under specific hydrological and climatic conditions, the yield gap among smallholder farmers is mainly due to unsuitable agricultural inputs, management practices and soil conditions (Grassini et al. 2011; Chen et al. 2014). Identifying these limiting factors is the fundamental step to close the existing yield gap. Several methods such as crop growth models (Wang et al. 2008; Chen et al. 2011) and field trials (Liu et al. 2015) have been used to assess potential yield and identify the yield limiting factors. However, the farmers’ actual production situation is seldom taken into account by those methods. Agronomic diagnosis is a very useful tool to identify the strategies for narrowing the yield gap by understanding its causes (Lobell and Ortiz-Monasterio 2006), especially for the contribution of yield components (Dore et al. 1997). For wheat, spikes per unit area, grain number per spike and grain weight codetermine its grain yield. The importance of each yield component to grain yield depends on the growth periods when water stress

occurs (Hochman et al. 1982) and management practices (Blue et al. 1990). When developing breeding strategies, the contributions of yield components and plant traits to grain yield are important. Simple correlation analysis between a single variable factor and grain yield may not provide a complete understanding of the importance of each yield component in determining the grain yield (Dewey and Lu 1959; Singh et al. 1979). The variation in yield and its determining factors are greatly dependent on smallholder farm production, and onfarm study is a valuable diagnostic method. Some on-farm studies have tried to identify and rank the cropping practices that are responsible in interaction with the environment, for a large proportion of the total variability in crop production, crop quality and environmental impact in a region (Sene et al. 2001; Homma et al. 2004). These studies were based on monitoring and a series of measurements in a group of fields, which were cultivated by farmers using current management practices, instead of experimental trials. Science and Technology Backyard (STB) is a useful platform to identify the limiting factors of yield gap and to close the yield gap on smallholder farms (Zhang et al. 2016). Based on agronomic diagnostic methods, this research was conducted in the Xushui Science and Technology Backyard (XSSTB), China. The objectives of this research were to: (1) understand the contribution of yield components to wheat yield on smallholder farms, (2) identify the limiting factors of management and soil nutrients for wheat yield components and yield gaps on smallholder farms, and (3) explore the improved strategies of yield components to close the wheat yield gap on smallholder farms.

2. Materials and methods 2.1. Site description This research was carried out in Xushui County, Hebei Province, 39°06´N and 115°39´E in the NCP, where the XSSTB is located. The per capita arable area is less than 0.1 ha. It belongs to the typical winter wheat growing area of smallholders in the NCP. The climate of this site is a monsoon climate of medium latitudes, with annual rainfall ranging from 527 to 556 mm.

2.2. Data collection In this study, the limiting factors included two categories. One is the management practice that can be improved.

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The second is the resource restriction, such as irrigation water availability, poor soil quality and low fertility. Here, the resource factors were indicated by soil nutrient content (soil nitrate, Olsen P, available K and irrigation times). To collect data, 45 fields of smallholder farmers were selected randomly in 2014 and 2015, and 42 fields in 2016. Farmers’ management practices were monitored, including information on seeding date, seeding rate, nitrogen (N) fertilizer, phosphate (P2O5) fertilizer and potash (K2O) fertilizer inputs, and irrigation times. All these data were recorded by XSSTB staff and students in a very timely fashion after the farmers had completed their field work. For instance, researchers of XSSTB noted wheat seeding date, and rate and basal fertilizer applications for each field. To obtain accurate amounts of fertilizer inputs, during the wheat growing period the fertilizer inputs were weighted on fields and the area of field receiving fertilizer was recorded. At harvest, the wheat plant samples were collected and the average spikes per hectare, grain number per spike and 1 000-grain weight were counted. Wheat grain yields were measured from three plots of 1.0 m2 selected randomly in each field. Grain yields were adjusted to 14.0% moisture content. The soil samples from the tillage layer (0–20 cm) of every field were collected and soil nutrient content was measured in October of each year, before winter wheat planting.

2.3. Data analysis In order to compare the differences of yield components between different farmers, we divided 132 farmers into high (H), middle (M), low (L) yield groups according to the ascending yield order, and each group contained 44 farmers. Then data were analyzed using the analysis of variance and path coefficients procedure described by Shipley (2000). Path analysis was performed using correlation considering the grain yield as a response variable, and the spikes per hectare, grain number per spike and 1 000-grain weight as predictor variables. In this study, yield gap was defined as the difference between the farmers’ highest yield and average yield (Lobell and Ortiz-Monasterio 2006). The farmers’ highest yield was the highest one among all farmers’ yield data for three years. The average yield was calculated for the data within each farmer group. The data were also subjected to boundary line analysis, a technique that has been widely used in the analysis of potential limiting factors. The underlying assumption is that the data on the boundary line best represents the relationship between two variables, while the potential influence of other limiting factors can be considered minimal (Webb 1972). To explore the contributions of individual

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management factors and soil nutrient content to the yield components in more detail, we adapted the boundary line approaches as introduced by Webb (1972), Fermont (2009) and Shatar and Mcbratney (2004). The approach for building a boundary line in this study was the one referred to by Chen et al. (2018). The main steps to obtain a boundary line are as follows: (1) Construct a scatter plot of data: y is yield or yield components and x is the limiting factor. (2) Extract the data points from the upper boundary of the data cloud. (3) Fit a function to the data points obtained from the second step. Then each of the boundary line functions can be used to predict the potential yield components. The gap between the highest attainable yield components (i.e., the farmers’ highest yield components) and the farmers’ actual yield components is taken as the total gap, which was the sum of two parts of the gaps. One part (defined as the explainable gap) was attributed to the individual limiting factors (x). The other part (defined as the unexplainable gap) was attributed to other unknown factors. The contribution of each factor was expressed as the proportion of the explainable gap to the total gap. The most important limiting factor for explaining the reduction at the field level was identified according to von Liebig’s law of the minimum (Von Liebig 1863). For the factor that was the most limiting, the number of corresponding farmers was counted. The average contribution proportion for each factor on all of the monitored fields was calculated, and the sum of the average proportions of total factors was regarded as 100%. The relative values were used to compare the relative contributions. The boundary line analysis and the variance and path analysis were performed in Microsoft Office Excel 2010 and IBM SPSS Statistics 19. Significance of differences was identified using analysis of variance (ANOVA) and Duncan’s test at 0.01 level or 0.05 level.

3. Results 3.1. Correlations and contributions of three yield components to wheat yield Average winter wheat yields at different yield levels under current farmer practices in Xushui County ranged from 8 020 to 9 698 kg ha–1 (Table 1). The yields were significantly different among the three groups (P<0.01). The winter wheat potential yield was 10 514 kg ha–1, the yield gaps varied strongly, and the gaps were 2 493, 1 636 and 815.1 kg ha–1 in the L, M and H yield group, respectively. However, among all yield components, only spikes per

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significant. In L and H yield groups, the correlation of yield to spikes per hectare was also positive and significant. These results indicated that the harvested population of winter wheat was the most important factor for the yield gaps of the monitored farmers. For all 132 farmer fields, the direct path coefficients of spikes per hectare, grain number per spike and 1 000-grain weight to wheat yield were 0.68, 0.09, and 0.38, respectively. For the low yield group, all direct path coefficients were positive, and the contribution of the spikes per hectare (x1→y=0.71) was the largest, followed by 1 000-grain weight (x3→y=0.55) and grain number per spike (x2→y=0.29). In

hectare was significantly different in the different groups (Table 2). The higher yield group had more spikes per hectare, and at the same time with plant density increasing, grain number per spike and 1 000-grain weight did not decrease. Therefore, under the current situation, farmers can gain high yield by increasing the wheat harvested population. Results from the correlation and path analysis are shown in Table 3. For all 132 farmer fields, the correlation of yield with spikes per hectare (r=0.51, P<0.01) was positive and significant, while correlations with grain number per spike (r=–0.16) and 1 000-grain weight (r=–0.10) were not

Table 1 Statistical description of winter wheat yields and yield gaps in three yield groups Yield group1) L M H Overall

n

Average

Standard deviation

Minimum

Maximum

Median

Yield gap

44 44 44 132

8 020.4 c 8 877.3 b 9 698.9 a 8 865.5

412 169.6 338.5 758.9

6 755 8 584 9 180 6 755

8 580 9 165 10 514 10 514

8 041 8 878 9 660 8 877

2 493.6 1 636.7 815.1 1 648.4

Maximum 7.04 7.59 8.33 49.0 52.0 50.0 52.0 69.0 54.0

Median 5.87 6.14 6.49 38.0 39.0 37.5 40.0 41.0 41.0

1)

L, group with low wheat yield; M, group with middle wheat yield; H, group with high wheat yield. Values followed by different letters within a row are significantly different at P<0.01.

Table 2 Statistical description of yield components of winter wheat in three yield groups Yield component Spikes per ha (106 ha–1) Grain number per spike

1 000-grain weight (g)

Yield group1) L M H L M H L M H

n 44 44 44 44 44 44 44 44 44

Average 5.770 c 6.138 b 6.598 a 38.6 a 38.8 a 37.5 a 40.8 a 42.3 a 41.9 a

Standard deviation 0.65 0.69 0.87 3.6 4.8 4.0 3.8 5.1 3.2

Minimum 3.67 4.79 4.71 31.0 25.0 31.0 34.0 33.0 37.0

1)

L, group with low wheat yield; M, group with middle wheat yield; H, group with high wheat yield. Values followed by different letters within a row are significantly different at P<0.05.

Table 3 Correlation analysis and path analysis of the yield with yield components in three yield groups1) Yield group2) L

M

H

Overall

1)

Yield component

Correlation coefficient

x1 x2 x3 x1 x2 x3 x1 x2 x3 x1 x2 x3

0.31* 0.07 0.05 0.25 –0.19 0.13 0.46** –0.43** –0.12 0.51** –0.16 –0.10

x1→y

Path coefficient x2→y

x3→y

0.71 –0.14 –0.45 0.38 –0.01 –0.18 0.35 –0.18 –0.14 0.68 –0.18 –0.26

–0.06 0.29 –0.04 0.00 –0.08 0.03 0.13 –0.26 –0.03 –0.02 0.09 –0.02

–0.35 –0.08 0.55 –0.13 –0.10 0.27 –0.02 0.00 0.04 –0.14 –0.07 0.38

x1, spikes per hectare; x2, grain number per spike; x3, 1 000-grain weight; y, yield. L, group with low wheat yield; M, group with middle wheat yield; H, group with high wheat yield. Scribied data are the direct path coefficients. * and **, significant differences at the P<0.05 and P<0.01 levels, respectively.

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addition, the absolute values of direct path coefficients were higher than the corresponding indirect path coefficients, which means that the yield components in the low yield group mainly had direct effects on yield. For the middle yield group, direct path coefficients for spikes per hectare (r=0.25, x1→y=0.38) and 1000-grain weight (r=0.13, x3→y=0.27) to yield were positive, and the absolute values of direct path coefficients were higher than that of the corresponding indirect path coefficients. However, the correlation and path coefficients for grain number per spike and corresponding indirect path coefficients (r=–0.19, x2→y=–0.08) were negative, and grain number per spike had a negative effect on yield, though the effect was small. While the absolute value (x2→y=–0.08) was smaller than the indirect path coefficient (x2→x3→y=–0.1), the effect of grain number per spike to yield was accounted for by grain weight. For the middle yield group, farmers should increase spikes per hectare and 1 000-grain weight to increase wheat yield. For the high yield group, there were positive direct path coefficients for spikes per hectare (r=0.46, x1→y=0.35, P<0.01) to yield, while the correlation and direct path coefficients between grain number per spike and wheat yield were significantly negative (r=–0.43, x2→y=–0.26, P<0.01). Among the three yield components, the contribution of the spikes per hectare was the largest, followed by the contributions of grain number per spike and grain weight, for which the influence was negative. These results also showed that spikes per hectare was the most important factor for ensuring high wheat yield in smallholder farmers’ fields.

3.2. Limiting factors to the yield components Results of boundary line analysis are shown in Fig. 1.

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The corresponding proportions of plots of management factors (seeding rate, seeding date, N input, basal N, top dressing N, P input and K input) to total plots for spikes per hectare, grain number per spike and grain weight were 77.1, 45.5, and 72.1%, respectively. Compared to resource factors, management practices were the more important factors affecting yield components. For spikes per hectare, seeding date was the most limiting factor with a 26.7% contribution rate; followed by basal N input (22.1%), seeding rate (14.5%) and all of these three factors belonged to the seeding application step. For grain number per spike, soil available K was the most limiting factor with a 25.8% contribution rate; followed by N input (16.7%) and nitrate N (15.2%), which indicates that mineral nutrition accounted for 91.7% of the plot in the total monitored fields. For grain weight, P2O5 input was the most limiting factor with a 23.3% contribution rate, followed by available K (22.5%), and seeding date (18.6%). Moreover, the applications of N and K2O fertilizers accounted for 27.1% of the plots, which can be regarded as the most important factors as well. The irrigation times did not significantly influence wheat yield components, because the surveyed farmers irrigated winter wheat at least two times.

3.3. The limiting factors and improved strategies for spikes per hectare The boundary lines of spikes per hectare (Y-axis) to management and soil nutrient factors (X-axis) can be described by a quadratic function (Fig. 2). Fig. 1 showed that the seeding and N fertilizer application were the most limiting factors for spikes per hectare. From Fig. 2, it can be induced that if the desired rate of spikes (6.598×106 ha−1 in

Seeding rate 1 000-grain weight

Seeding date N input Basal N Top dressing N P input

Grain number per spike

K input Irrigation times Nitrate N Olsen P

Spikes per hectare

Available K

0 20 40 60 80 The corresponding proportions of the limiting factors (%)

100

Fig. 1 The limiting factors identified by the boundary line approach and the corresponding proportions of plots (%) in which these factors were most limiting for spikes per hectare, grain number per spike and 1 000-grain weight.

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farmers had suitable application values, there were other factors affecting the desired spikes per hectare, and there were similar results for other factors. Therefore, we cannot provide a single solution to all farmers and the improved strategies have to be adapted for each farmer’s situation. For nitrogen input, the boundary lines of spikes per hectare to basal N input (R2=0.700) and topdressing N input (R2=0.464) had higher R2 than total N input, which indicated spikes per hectare was dramatically affected by

Spikes per hectare (104 ha–1)

900

Spikes per hectare (104 ha–1)

900

Spikes per hectare (104 ha–1)

900

Spikes per hectare (104 ha–1)

the high yield farmer group, Table 2) is achieved, the seeding rate should range from 210 to 300 kg ha−1, the seeding date should range from October 3th to 8th, and basal N input should range from 90–180 kg ha−1. When seeding rate was about 255 kg ha–1, seeding date was October 5th and basal N input was about 130 kg ha–1, the highest spikes per hectare achieved was 8.3×106 ha–1. However, compared to these optimized values, some farmers had lower values and some farmers had higher values. Although some

900

800 700 600 500

.1

2

0

0 1

ct

.1

y=–22.78x2+200.87x+394.23 R2=1.0**

O

.8

ct

O

ct

.6

O

ct

.4 ct

.2

O

200 250 300 Seeding rate (kg ha–1)

ct

0

O

300 0

y=–7.45x2+82.33x+577.11 R2=0.60**

y=–0.04x2+21.17x–1 836.8 R2=0.51**

O

400

2 3 Irrigation times

4

Seeding date 800 700 600 500 400 300 0

y=–0.0039x2+1.89x+559.97 R2=0.24** 0

200 300 400 N input (kg ha–1)

y=–0.0074x2+1.95x+673.79 R2=0.46**

y=–0.038x2+10.29x+95.56 R2=0.70** 0

50

100 150 Basal N (kg ha–1)

200 0

50 100 150 200 250 Top dressing N (kg ha–1)

800 700 600 500 400 300 0

y=–0.0099x2+3.18x+535.0 R2=0.57** 0 50

100 150 200 250 300 0 P input (kg ha–1)

y=–0.061x2+7.80x+573.67 R2=0.78** 20 40 60 80 100 120 140 160 K input (kg ha–1)

800 700 600 500 400 300 0

y=–0.029x2+2.98x+759.88 R2=0.44** 0 20 40 60 80 100 120 140 0 Nitrate N (mg kg–1)

y=–0.019x2+3.06x+663.15 R2=0.47**

y=–0.090x2+4.72x+729.41 R2=0.61** 20

40 60 80 Olsen P (mg kg–1)

0

50 100 150 200 Available K (mg kg–1)

Fig. 2 Relationships of management practices and soil nutrients with the spikes per hectare. The lines are boundary lines, which represent the best response trend of the data cloud. **, significant difference at the P<0.01 level.

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was 50 (achieved by the top 5% of farmers), far above the average level of 38 (Table 2). Available K, total N input, and nitrate N were the most important limiting factors for this yield component (Fig. 3). In order to achieve the highest grain number per spike, available K should be 105 mg kg–1, total N input should be 310 kg ha–1, and nitrate N should be 17–70 mg kg–1. Farmers’ practices were largely varied among each other, which were inconsistent with the aforementioned optimized values.

the distribution ratio of N fertilizer.

3.4. The limiting factors and improved strategies for grain number per spike

y=–0.0012x2+0.56x–21.8 R2=0.77**

60 55 50 45 40 35 30 25 20 0

60 55 50 45 40 35 30 25 20 0

0 1

2

ct

.1

.1

2

0 O

ct

ct

O

O

ct

.8

y=–0.00073x2+0.21x+34.1 R2=0.61**

.6

y=–0.00070x2+0.23x+29.6 R2=0.54**

O

O

Grain number per spike Grain number per spike Grain number per spike

60 55 50 45 40 35 30 25 20 0

.4

3 Irrigation times

ct

200 250 300 350 Seeding rate (kg ha–1)

.2

0

y=–x2+10.2x+27 R2=1**

y=–0.260x2+3.89x+34.5 R2=0.61**

O

60 55 50 45 40 35 30 25 20 0

ct

Grain number per spike

The boundary lines of grain number per spike to management and soil conditions all showed moderately good to high estimation relationships (R2 ranged from 0.54 to 1, P<0.01). Results indicated that the maximum grain number per spike

4

Seeding date y=–0.0003x +0.21x+12.8 R2=0.62** 2

0150 200 250 300 350 400 N input (kg ha–1)

0

y=–0.00073x2+0.28x+22.0 R2=0.59**

0 50

100 150 200 250 300 0 P input (kg ha–1) y=–0.0009x2+0.04x+46.0 R2=0.63**

0

20 40 60 80 100 120 140 0 Nitrate N (mg kg–1)

100 200 Basal N (kg ha–1)

300

0

50

100 150 200 250 300 Top dressing N (kg ha–1)

y=–0.0029x2+0.35x+38.3 R2=0.60**

20 40 60 80 100 120 140 160 K input (kg ha–1) y=–0.009x2+0.82x+32.9 R2=0.65**

20 40 60 80 Olsen P (mg kg–1)

y=–0.0017x2+0.41x+22.9 R2=0.78**

0

50

100 150 200 Available K (mg kg–1)

Fig. 3 Relationships of management and soil nutrients with grain number per spike. The lines are boundary lines, which represent the best response trend of the data cloud. **, significant difference at the P<0.01 level.

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3.5. The limiting factors and improved strategies for 1 000-grain weight

(R2=0.13), K2O input (R2=0.25) and available K (R2=0.24). The potential 1 000-grain weight was 52.6 g (attained by the top 5% of farmers), far higher than the farmers’ average level (41.5 g). P2O5 input, available K, and seeding date were the top three limiting factors for grain weight (Fig. 1). In order to minimize the gap between average grain weight and potential grain weight, P2O5 input should range from 130 to 240 kg ha–1, available K should range from 50 to

60 50

y=–1.39x2+9.76x+37.1 R2=1.00 **

y=–0.2963x2+3.5x+38.9 R2=0.49**

y=–0.0011x2+0.62x–32.9 R2=0.50**

55 45 40

ct .1 0 O ct .1 2

ct .8

0 1

2 3 Irrigation times

O

ct .6

O

O

200 250 300 350 Seeding rate (kg ha–1)

ct .2

0

O

30 0

ct .4

35

O

1 000-grain weight (g)

The relationships between 1 000-grain weight and monitored factors based on the boundary line analysis are shown in Fig. 4. Management practices and soil conditions all showed moderately good or high estimations for this yield component (R2 ranged from 0.31 to 1, P<0.01), except for basal N input

4

1 000-grain weight (g)

1 000-grain weight (g)

1 000-grain weight (g)

Seeding date 60

y=–0.0005x2+0.29x+11.9 R2=0.31**

55 50

y=–0.0004x2+0.09x+44.8 R2=0.13**

y=–0.0013x2+0.42x+16.6 R2=0.60**

45 40 35 30 0

0150 200 250 300 350 400 N input (kg ha–1)

60

0

50 100 150 200 250 300 0 Basal N (kg ha–1)

y=–0.0009x2+0.38x+13.4 R2=0.73**

55

50 100 150 200 250 300 Top dressing N (kg ha–1)

y=–0.0015x2+0.15x+45.0 R2=0.25**

50 45 40 35 30 0

0 50 100 150 200 250 300 P input (kg ha–1)

60

0

y=–0.0005x2+0.12x+45.9 R2=0.78**

55

20 40 60 80 100 120 140 160 K input (kg ha–1) y=–0.0073x2+0.44x+45.7 R2=0.81**

y=–0.0007x2+0.11x+44.0 R2=0.24**

50 45 40 35 30 0

0

20 40 60 80 100 120 140 0 Nitrate N (mg kg–1)

20

40 60 80 Olsen P (mg kg–1)

100 50

100 150 200 Available K (mg kg–1)

Fig. 4 Relationships of management practices and soil nutrients with 1 000-grain weight. The lines are boundary lines, which represent the best response trends of the data cloud. **, significant difference at the P<0.01 level.

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100 mg kg–1 and seeding date should range from 4th to 8th October. In practice, fewer farmers could reach these optimized ranges, and most farmers had lower or higher values, which decreased the 1 000-grain weight.

4. Discussion 4.1. The relative importance of yield components for closing the wheat yield gap on smallholder farms Optimizing spike number per hectare is a key method to maximize yields in most crops, because it can increase the vigor of individual plants and hence their grain yields (Weiner et al. 2001). In low, middle, and high yield groups in this study, the direct path coefficients of spikes per hectare on wheat yield were positive and the highest among the yield components examined. The correlation coefficients between spikes per hectare and yield were significantly positive correlations (P<0.01) except for the middle yield group (Table 3), which indicated that achieving a high spikes number per hectare would guarantee high yields of winter wheat. These results are similar to those of previous studies (Simane et al. 1998; Del Blanco et al. 2001). In this study, farmers who had higher yield also had higher spikes number per hectare (Table 1), which indicated that increasing the effective spikes of wheat per area is an important method to close the wheat yield gap. However, there is a consistent negative correlation between these yield components (Bavec et al. 2002; Slafer 2003), and a trade-off existed among them, indicating that the yield components could influence each other. High plant densities could decrease grain number per spike and 1 000-grain weight, and thus could decrease the grain yield per spike and per area (Ram et al. 2013; Gaju et al. 2014). Due to water and nutrient source limitations and physiological processes basic to grain growth, there exists a limit to the grain number increases which can be accompanied by grain weight reductions (Sadras 2007; Reynolds et al. 2009). In this study, there were significant negative relationships between spikes per hectare and grain number per spike (Fig. 5-A), between spikes per hectare and 1 000-grain weight (Fig. 5-B), between grain number per spike and 1 000-grain weight (Fig. 5-C), and between grain numbers per hectare and 1 000-grain weight (Fig. 5-D), respectively. Quantifying the most appropriate yield component values has a significant benefit for closing yield gaps. A combination of boundary line analysis and trade-off analysis was introduced to give the maximum and optimum values of yield components (González et al. 2011). The wheat yield is determined by the product of spikes per hectare, grain number per spike, and 1 000-grain weight. The results of boundary line analysis showed that, with

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increasing spikes per hectare, the grain number per spike first increased and then decreased slightly (Fig. 5-A). Within the certain range of grain number per spike, the 1 000-grain weight first decreased and then stabilized (Fig. 5-D). Based on these functions, the changes of optimized grain number per spike, 1 000-grain weight and potential yield with spikes per hectare were obtained (Fig. 5-E). The results show that within the practical scope of spikes per hectare in farmers’ fields, the potential yield should increase rapidly from 8 820 to 17 393 kg ha–1 with the increase of spikes per hectare if the boundary line situation could be achieved. However, under the average situation of the farmers (Fig. 5-F), the wheat yield should also increase with the increase of spikes per hectare, but the increase rate would be much slower than that under the boundary line conditions. This indicates that there is great potential for closing the yield gap by optimizing yield components and accordingly improving management practices.

4.2. Wheat yield gap and limiting factors on smallholder farms The results of this study suggest that a large space exists to increase wheat yields in smallholders’ fields. The yield gaps measured as the gap between the highest yield and the average yield in different farmer groups could range from 814 to 2 493 kg ha–1 (Table 1) and the overall farmers’ average wheat yield was 8 865.6 kg ha–1. The average wheat yield was higher than in previous studies in the NCP, in which the average on-farm yields were only 7 330 kg ha–1 (Li et al. 2014) or 7 700 kg ha–1 (Lu and Fan 2013). The reason for this discrepancy may be that the cultivars and management of winter wheat have been improved during the latest five to six years. The potential yield of winter wheat in this study should be achievable by treating the trade-offs of the three yield components appropriately and the potential yields should be 8 820–17 393 kg ha–1 within the scope of farmers practices (Fig. 5-E). These values are within the range of wheat potential yield in the NCP from 5 900–17 400 kg ha–1 found by previous studies (Wu et al. 2006; Wang et al. 2008; Chen et al. 2011; Lu and Fan 2013; Li et al. 2014). Wheat yield and yield components were largely influenced by various factors, such as management practices and soil conditions (Anderson 2010; Shah et al. 2013). In this study, seeding rate, seeding date and N fertilization (Fig. 2) were the top three limiting factors for spike number per hectare, in agreement with previous findings (Giunta and Motzo 2004; Neugschwandtner and Kaul 2014; Neugschwandtner et al. 2015). In this study, when total N application was 195–275 kg ha–1, spike number per hectare remained at relatively high levels, in agreement with other studies

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45 40 35 30 25

55

1 000-grain weight (g) Grain number per spike

8

9

45 40 35

60 50 40 30 20 10 0

45 40 35

30 35 40 45 50 Grain number per spike

F

9

50 45 40 35

55

20 18 16 14 12 10 8 6 Grain number per spike 4 1 000-grain weight 2 Potential yield 0 0 4 5 6 7 8 9 Spikes per hectare (106 ha–1)

4 5 6 7 8 Spikes per hectare (106 ha–1) y=–0.037x+50.3 R2=0.14** y=0.0004x2–0.24x+82.7 R2=0.88**

55

30 0 0 25

0 3

D 60

y=–0.019x+42.2 R2=0.0005** y=–0.10x2+8.07x–107.4 R2=0.55**

50

30 0

E

4 5 6 7 Spikes per hectare (106 ha–1)

1 000-grain weight (g) Grain number per spike

1 000-grain weight (g)

C 60

0 3

R2=0.16** R2=0.83**

50

30 0

1 000-grain weight (g)

20 0

y=–1.61x+51.4 y=–3.00x+65.9

55

60 50 40 30 20 10 0

0 100

150 200 250 300 350 400 Grain number per hectare (106 ha–1)

20 Grain number per spike 18 1 000-grain weight 16 Potential yield 14 12 10 8 6 4 2 0 0 4 5 6 7 8 9 Spikes per hectare (106 ha–1)

Potential yield (103 kg ha–1)

50

B 60

y=–1.38x+46.8 R2=0.074** y=–0.84x2+11.2x+9.28 R2=0.30**

1 000-grain weight (g)

55

Potential yield (103 kg ha-1)

Grain number per spike

A 60

Fig. 5 Relationships between wheat yield and yield components. A, spikes per hectare and grain number per spike (r=–0.271, P<0.01). B, spikes per hectare and 1 000-grain weight (r=–0.396, P<0.01). C, grain number per spike and 1 000-grain weight (r=–0.023). D, grain number per hectare and 1 000-grain weight (r=–0.377, P<0.01). E, the variation of yield and yield components achieved by boundary line and F, the variation of yield and yield components achieved by average levels. The solid lines are boundary lines, which represent the best response trends of the data cloud. **, significant difference at the P<0.01 level.

(Wang et al. 2018). Adjusting seeding date and seeding rate to appropriate levels is effective for achieving high spikes per area. This study showed that the appropriate seeding date should range from October 3th to 8th, and the seeding rate should range from 210–300 kg ha–1, which agreed with previous studies in China (Zhang et al. 2017). Grain number per spike and grain weight are also important yield components; but seeding rate didn’t show significant influences on them, and they were mainly influenced by soil nutrients (Fig. 1), especially the contents of phosphorus and potassium.

4.3. Strategies for improving yield components to close the yield gap on smallholder farms Closing yield gaps on smallholder farms is a key measure for sustainably feeding the world’s growing population (Lobell et al. 2009; Mueller et al. 2012; van Ittersum et al. 2013), but also presents a great challenge because the yields of smallholder farmers exist with huge variations. In this study, wheat yields on smallholder farms in the same village often showed great variations due to the varied management and soil conditions. It is necessary to understand the limiting

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factors of yield components (Lobell et al. 2009) and to develop innovative technologies and translate scientific knowledge into farming action based on on-farm studies (Zhang et al. 2016). In addition, yield gap studies usually focus on identifying the potential yield and the scale of the yield gap over time for a given scale. However, they should also pay more attention to the magnitude of inter-annual variability, for the yield gap may vary slightly or dramatically between different years (Hoffmann et al. 2018). The boundary line approach has been broadly applied in yield gap analysis (Webb 1972; Shatar et al. 2004; Fermont 2009). It was also used to optimize fertilization (Ahmad et al. 2013). In this study, based on the monitored data from smallholder farmer fields, we used this approach to determine the optimizing ranges of management measures for desired wheat yield components (Figs. 2–4). However, compared to these reasonable ranges for the management measures, most farmers’ practices were not suitable and both lower and higher applications existed. Therefore, an effective strategy would be to change farmers management practices according to these optimized ranges, especially improving wheat seeding quality and optimizing farmers’ nutrient management practices in the NCP.

5. Conclusion The present study showed that the yield of winter wheat is high among smallholders in the NCP. Looking at the yield components, wheat population quality is identified as the most important factor for achieving the high wheat yield. There were trade-offs among the three yield components, and the roles of the other yield components such as grain number per spike and grain weight cannot be ignored. The management factors are more important than resource factors, and the yield gap could be improved by optimizing farmers’ management practices. According to the analysis of farming practices, the seeding application step (seeding rate and seeding date) and basal N fertilizer application were the most important limiting factors for the yield gap. Compared to the reasonable range of management measures proposed in this study, most farmer practices have improper performances, such as the imbalanced fertilization at rates lower or higher than the optimized rate. It is concluded that exploring the strategies of optimizing yield components could be achieved by improving wheat seeding quality and optimizing farmers’ nutrient management practices in the NCP.

Acknowledgements This work was supported by the National Basic Research Program of China (2015CB150405), and the Special Fund

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for Agro-scientific Research in the Public Interest, China (201103003). We thank Graduate students Cui Shilei, Zhou Yuanyuan, Zhang Miao, Wang Shiqiang, Wang Haodan, Wang Liying and Wang Yaocong who helped with data collection. We also thank Hou Yong, the Associate Professor of China Agricultural University, who helped with language polishing. Our appreciation also goes to the farmers who participated in the field research.

References Ahmad F, Uddin S, Ahmad N, Islam R. 2013. Phosphorusmicrobes interaction on growth, yield and phosphorus-use efficiency of irrigated cotton. Archives of Agronomy and Soil Science, 59, 341–351. Anderson W K. 2010. Closing the gap between actual and potential yield of rainfed wheat. The impacts of environment, management and cultivar. Field Crops Research, 116, 14–22. Bavec M, Bavec F, Varga B, Kovacevic V. 2002. Relationships among yield, it’s quality and yield components, in winter wheat (Triticum aestivum L.) cultivars affected by seeding rates. Die Bodenkultur, 53, 143–151. Del Blanco I A, Rajaram S, Kronstad W E. 2001. Agronomic potential of synthetic hexaploid wheat-derived populations. Crop Science, 41, 670–676. Blue E N, Mason S C, Sander D H. 1990. Influence of planting date, seeding rate and phosphorus rate on wheat yield. Agronomy Journal, 82, 762–768. Chen C, Baethgen W E, Wang E L, Yu Q. 2011. Characterizing spatial and temporal variability of crop yield caused by climate and irrigation in the North China Plain. Theoretical and Applied Climatology, 106, 365–381. Chen G F, Cao H Z, Liang J, Ma W Q, Guo L F, Zhang S H, Jiang R F, Zhang H Y, Goulding K W T, Zhang F S. 2018. Factors affecting nitrogen use efficiency and grain yield of summer maize on smallholder farms in the North China Plain. Sustainability, 10, 363. Chen X P, Cui Z L, Fan M S, Vitousek P, Zhao M, Ma W Q, Wang Z L, Zhang W J, Yan X Y, Yang J C, Deng X P, Gao Q, Zhang Q, Guo S W, Ren J, Li S Q, Ye Y L, Wang Z H, Huang J L, Tang Y Q, et al. 2014. Producing more grain with lower environmental costs. Nature, 514, 486. Dewey D R, Lu K H. 1959. A correlation and path-coefficient analysis of components of crested wheatgrass seed production. Agronomy Journal, 51,70–74. Dore T, Sebillotte M, Meynard J M. 1997. A diagnostic method for assessing regional variations in crop yield. Agricultural Systems, 54, 169–188. Fermont A M, Van Asten P J A, Tittonell P, Van Wijk M T, Giller K E. 2009. Closing the cassava yield gap: An analysis from smallholder farms in East Africa. Field Crops Research, 112, 24–36. Foley J A, Ramankutty N, Brauman K A, Cassidy E S, Gerber J S, Johnston M, Mueller N D, O’Connell C, Ray D K, West P

1712

CAO Hong-zhu et al. Journal of Integrative Agriculture 2019, 18(8): 1701–1713

C, Balzer C, Bennett E M, Carpenter S R, Hill J, Monfreda C, Polasky S P, Rockstrom J, Sheehan J, Siebert S, Tilman D, et al. 2011. Solutions for a cultivated planet. Nature, 478, 337–342. Gaju O, Reynolds M P, Sparkes D L, Mayes S, Ribas-Vargas G, Cross J, Foulkes M J. 2014. Relationships between physiological traits, grain number and yield potential in a wheat DH population of large spike phenotype. Field Crops Research, 164, 126–135. Giunta F, Motzo R. 2004. Sowing rate and cultivar affect total biomass and grain yield of spring triticale (xTriticosecale Wittmack) grown in a Mediterranean-type environment. Field Crops Research, 87, 179–193. Godfray H C, Beddington J R, Crute I R, Haddad L, Lawrence D, Muir J F, Pretty J, Robinson S, Thomas S M, Toulmin C. 2010. Food security: The challenge of feeding 9 billion people. Science, 327, 812–818. González F G, Terrile I I, Falcón M O. 2011. Spike fertility and duration of stem elongation as promising traits to improve potential grain number (and yield): Variation in Modern Argentinean Wheats. Crop Science, 51, 1693. Grassini P, Thorburn J, Burr C, Cassman K G. 2011. High-yield irrigated maize in the Western U.S. Corn Belt: I. On-farm yield, yield potential, and impact of agronomic practices. Field Crops Research, 120, 142–150. Hazell P B R, Poulton C, Wiggins S, Dorward A. 2007. The Future of Small Farms for Poverty Reduction and Growth. International Food Policy Research Institute, Washington, USA. Hochman Z. 1982. Effect of water stress with phasic development on yield of wheat grown in a semi-arid environment. Field Crops Research, 5, 55–67. Hoffmann M P, Haakana M, Asseng S, Höhn J G, Palosuo T, Ruiz-Ramos M, Fronzek S, Ewert F, Gaiser T, Kassie B T, Paff K. 2018. How does inter-annual variability of attainable yield affect the magnitude of yield gaps for wheat and maize? An analysis at ten sites. Agricultural Systems, 159, 199–208. Homma K, Horie T, Shiraiwa T, Sripodok S, Supapoj N. 2004. Delay of heading date as an index of water stress in rainfed rice in mini-watersheds in Northeast Thailand. Field Crops Research, 88, 11–19. Van Ittersum M K, Cassman K G, Grassini P, Wolf J, Tittonell P, Hochman Z. 2013. Yield gap analysis with local to global relevance - A review. Field Crops Research, 143, 4–17. Li K N, Yang X G, Liu Z J, Zhang T Y, Lu S, Liu Y. 2014. Low yield gap of winter wheat in the North China Plain. European Journal of Agronomy, 59, 1–12. Von Liebig. 1863. The Natural Laws of Husbandry. Walter and Maberly, London, UK. Liu J G, Wang G Y, Kelly T, Zhang Y Y, Yang M, Chu Q Q. 2015. Effect of nitrogen and water deficit type on the yield gap between the potential and attainable wheat yield. Chilean Journal of Agricultural Research, 75, 457–464. Lobell D B, Cassman K G, Field C B. 2009. Crop yield gaps: Their importance, magnitudes, and causes. Annual Review

of Environment and Resources, 34, 179–204. Lobell D B, Ortiz-Monasterio J I. 2006. Regional importance of crop yield constraints: Linking simulation models and geostatistics to interpret spatial patterns. Ecological Modelling, 196, 173–182. Lu C H, Fan L. 2013. Winter wheat yield potentials and yield gaps in the North China Plain. Field Crops Research, 143, 98–105. Mueller N D, Gerber J S, Johnston M, Ray D K, Ramankutty N, Foley J A. 2012. Closing yield gaps through nutrient and water management. Nature, 490, 254–257. NBSC (National Bureau of Statistics of China). 2012. China Statistical Yearbook 2012. China Statistics Press, Beijing, China. (in Chinese) Neugschwandtner R W, Bohm K, Hall R M, Kaul H P. 2015. Development, growth, and nitrogen use of autumnand spring-sown facultative wheat. Acta Agriculturae Scandinavica Section (B: Soil and Plant Science), 65, 6–13. Neugschwandtner R W, Kaul H P. 2014. Sowing ratio and N fertilization affect yield and yield components of oat and pea in intercrops. Field Crops Research, 155, 159–163. Ram H, Dadhwal V, Vashist K K, Kaur H. 2013. Grain yield and water use efficiency of wheat (Triticum aestivum L.) in relation to irrigation levels and rice straw mulching in North West India. Agricultural Water Management, 128, 92–101. Reynolds M, Foulkes M J, Slafer G A, Berry P, Parry M A J, Snape J W, Angus W J. 2009. Raising yield potential in wheat. Journal of Experimental Botany, 60, 1899–1918. Sadras V O. 2007. Evolutionary aspects of the trade-off between seed size and number in crops. Field Crops Research, 100, 125–138. Sene M, Dore T, Gallet C. 2001. Relationships between biomass and phenolic production in grain sorghum grown under different conditions. Agronomy Journal, 93, 49–54. Shah Z, Ul Haq I, Rehman A, Khan A, Afzal M. 2013. Soil amendments and seed priming influence nutrients uptake, soil properties, yield and yield components of wheat (Triticum aestivum L.) in alkali soils. Soil Science and Plant Nutrition, 59, 262–270. Shatar T M, Mcbratney A B. 2004. Boundary-line analysis of field-scale yield response to soil properties. Journal of Agricultural Science, 142, 553–560. Shipley B. 2000. Cause and Correlation in Biology: A User’s Guide to Path Analysis, Structural Equations and Causal Inference. Cambridge University Press, Cambridge. Simane B, Struik P C, Rabbinge R. 1998. Growth and yield component analysis of durum wheat as an index of selection to terminal moisture stress. Tropical Agriculture, 75, 363–368. Singh D, Singh M, Sharma K C. 1979. Correlation and path coefficient analysis among flag leaf area, yield and yield attributes in wheat (Triticum aestivum L.). Cereal Research Communications, 7, 145–152, Slafer G A. 2003. Genetic basis of yield as viewed from a crop physiologist’s perspective. Annals of Applied Biology, 142, 117–128.

CAO Hong-zhu et al. Journal of Integrative Agriculture 2019, 18(8): 1701–1713

Tilman D, Balzer C, Hill J, Befort B L. 2011. Global food demand and the sustainable intensification of agriculture. Proceedings of the National Academy of Sciences of the United States of America, 108, 20260–20264. Tilman D, Cassman K G, Matson P A, Naylor R, Polasky S. 2002. Agricultural sustainability and intensive production practices. Nature, 418, 671–677. Wang E L, Yu Q, Wu D R, Xia J. 2008. Climate, agricultural production and hydrological balance in the North China Plain. International Journal of Climatology, 28, 1959–1970. Wang L F, Sun J T, Zhang Z B, Xu P, Shangguan Z P. 2018. Winter wheat grain yield in response to different production practices and soil fertility in northern China. Soil & Tillage Research, 176, 10–17. Webb R A. 1972. Use of boundary line in analysis of biological data. Journal of Horticultural Science, 47, 309–319

1713

Weiner J, Griepentrog H W, Kristensen L. 2001. Suppression of weeds by spring wheat Triticum aestivum increases with crop density and spatial uniformity. Journal of Applied Ecology, 38, 784–790. Wu D R, Yu Q, Lu C H, Hengsdijk H. 2006. Quantifying production potentials of winter wheat in the North China Plain. European Journal of Agronomy, 24, 226–235. 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. Zhang X T, Huang Y F, Ma X J, Ye Y L. 2017. Influence of seeding rate and nitrogen application on different genotype of wheat yield, dry matter accumulation and transportation. Plant Physiology Journal, 53, 1067–1076. (in Chinese)

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