Developing sustainable summer maize production for smallholder farmers in the North China Plain: An agronomic diagnosis method

Journal of Integrative Agriculture 2019, 18(8): 1667–1679 Available online at www.sciencedirect.com

ScienceDirect

RESEARCH ARTICLE

Developing sustainable summer maize production for smallholder farmers in the North China Plain: An agronomic diagnosis method CHEN Guang-feng1, 3, 4, CAO Hong-zhu2, CHEN Dong-dong2, ZHANG Ling-bo1, ZHAO Wei-li1, ZHANG Yu1, MA Wen-qi2, JIANG Rong-feng1, 3, ZHANG Hong-yan1, 3, ZHANG Fu-suo1, 3 1

College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, P.R.China

2

College of Resources and Environment Science, Hebei Agricultural University, Baoding 071001, P.R.China Key Laboratory of Plant-Soil Interactions, Ministry of Education, Beijing 100193, P.R.China 4 National Agricultural Technology Extension and Service Center, Ministry of Agriculture and Rural Affairs, Beijing 100125, P.R.China 3

Abstract With an increasing population and changing diet structure, summer maize is increasingly becoming an important energy crop in China. However, traditional farmer practices for maize production are inefficient and unsustainable. To ensure food security and sustainable development of summer maize production in China, an improved, more sustainable farmer management system is needed. Establishing this system requires a comprehensive understanding of the limitations of current farming practice and the ways it could be improved. In our study, 235 plots from three villages in the North China Plain (NCP) were monitored. Maize production on farms was evaluated; our results showed that the maize yield and nitrogen partial factor productivity (PFPN) were variable on smallholder farms at 6.6–13.7 t ha–1 and 15.4–88.7 kg kg–1, respectively. Traditional farming practices also have a large environmental impact (nitrogen surplus: –64.2–323.78 kg ha–1). Key yield components were identified by agronomic diagnosis. Grain yield depend heavily on grain numbers per hectare rather than on the 1 000-grain weight. A set of improved management practices (IP) for maize production was designed by employing a boundary line (BL) approach and tested on farms. Results showed that the IP could increase yield by 18.4% and PFPN by 31.1%, compared with traditional farmer practices (FP), and reduce the nitrogen (N) surplus by 57.9 kg ha–1. However, in terms of IP effect, there was a large heterogeneity among different smallholder farmers’ fields, meaning that, precise technologies were needed in different sites especially for N fertilizer management. Our results are valuable for policymakers and smallholder farmers for meeting the objectives of green development in agricultural production. Keywords: smallholder farmers, sustainable production, yield gap, agronomic diagnosis, North China Plain

1. Introduction Received 22 June, 2018 Accepted 5 November, 2018 CHEN Guang-feng, Mobile: +86-15652770767, E-mail: [email protected]; Correspondence ZHANG Hong-yan, Mobile: +86-13718372760, 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(18)62151-3

Increasing population and environmental degradation have placed unprecedented pressures on agricultural and natural resources, due to dietary changes and bioenergy use (Foley et al. 2011). Many studies have determined that cereal production needs to be doubled (even tripled) to meet the food demand (World Bank 2008; Godfray et al. 2010).

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However, the growth of grain yield of many food crops in the global scale is slowing and even stagnating (Ray et al. 2012; Grassini et al. 2013). There are large yield variations and gaps across our existing agricultural land, especially in developing counties (Licker et al. 2010; Neumann et al. 2010; Ray et al. 2015). In China, smallholder farmers dominate agricultural production but tend to have poor knowledge of farming technologies and scattered field plots (Zhang et al. 2016). This is particularly the case in the North China Plain (NCP), where the farmers’ average yield is only about 41% of its modeled potential with a mean cultivated area of 0.7 hectare per household (Meng et al. 2013). In practice, biophysical, agronomic and socio-economic factors that affect farmers are all attributed to the yield gap, which is the gap between farmer actual yield and attainable or potential yield (Lobell et al. 2009; Liang et al. 2011; Affholder et al. 2013). For instance, Liang et al. (2011) identified poor irrigation and technical service, incorrect nutrient management, and high labor cost as major causes of the yield gap. Zhao et al. (2016) showed that farmer training was a useful method to achieve sustainable production. Yield-limiting factors are numerous and complex (Liang et al. 2011; Xiao and Tao 2014; Zhang et al. 2016). In China, grain yield increases strongly rely on increasing nitrogen (N) fertilizer inputs (Cui et al. 2014). Use of synthetic N fertilizer has fed more than half of the world population in the early 20th century (Zhang et al. 2015). In 2010, China, which only accounts for 7% of the global cropland, consumed more than 30% of global fertilizer (FAO 2016). This is almost twice as much as N recovered in crops in China (Chen et al. 2011). Overused fertilizer has caused low nutrient use efficiency (NUE) and has negatively affected the environment through soil acidification (Guo et al. 2010), air pollution (Liu et al. 2013), and lakes clogged with algal blooms (Zhang et al. 2013). Agronomists have already developed many methods to address the challenge of sustainable crop production (Cui et al. 2008; Chen et al. 2011; Xu et al. 2014). Some studies showed that the N application rate could be reduced by 30–60% without yield losses on experiment fields (Cui et al. 2008; Ju et al. 2009). An integrated soil-crop system management approach (ISSM) developed by Chen et al. (2011) is a model-driven production system. Cui et al. (2008) proposed a precise N management strategy based on soil mineral nitrogen (Nmin; NO3-N+NH4-N, 0–90 cm depth) testing to calculate a recommended of N application rate. Xu et al. (2014) proposed new fertilizer recommendations for smallholder farmers with a software program called Nutrient Expert. Those recommendations could significantly increase NUE without yield losses. However, these technologies were well not widely distributed to farmers,

partially due to the fact that these proposed technologies were often too complicated for farmers to learn, and the optimum practices are not designed by applying farmers’ field data. A simpler and economically cheaper technique is required (Zhang et al. 2016). The agronomic diagnostic method has been used on many crops to develop sustainable cropping systems. Its principle is to analyze the relationship between yield-limiting factors and yield components (Doré et al. 1997; Makowski et al. 2007; Valantin-Morison and Meynard 2008), to design optimal management practices in a given area (Doré et al. 2008). The objectives of this study were to: i) document the summer maize production situation on smallholder farmers’ fields in the NCP; ii) design an improved management practice using famers’ production data, and iii) test whether the improved practices could simultaneously increase yield and improve NUE.

2. Materials and methods 2.1. Site description Our study was conducted in three representative agricultural production counties - Laoling (37°43´N and 117°13´E), Xushui (39°06´N and 115°39´E) and Quzhou (36°45´N and 114°57´E) - in the NCP. In each county, a village with a Science and Technology Backyard (STB; Zhang et al. 2016) was selected: Nanxia Village in Laoling County; Yangong Village in Xushui County; and Wangzhuang Village in Quzhou County. The major crop rotation system in the three villages is winter wheat-summer maize: winter wheat is often sown in the mid-to-late October and harvested in the early June of the next year, while summer maize is sown in mid-June and harvested in early October. The climate at all study sites is a warm temperate continental monsoon climate. Average annual precipitation ranges between 527–556 mm, and 60–70% of the annual precipitation occurs from late June till late October. The per capita arable land in all our study sites was approximately 0.1 ha, a typical smallholder farm in China.

2.2. Research framework Obtaining the farmers’ production data Our field research was conducted in four steps (Fig. 1). Obtaining the farmers’ production data was the first step. In total, 235 plots (77 in Nanxia, 45 in Yangong and 113 in Wangzhuang) were randomly selected in 2015 (June to October). Field practice data were recorded immediately by observers after farmers finished their field work. Such data include total nitrogen fertilizer input (N, including basal fertilization

CHEN Guang-feng et al. Journal of Integrative Agriculture 2019, 18(8): 1667–1679

1. Monitoring the smallholder farmers’ fields in three STB villages in the NCP

2. Current status of farmers summer maize production Agronomic diagnosis

Boundary line approach

3. Designing the improved management practices

4. Testing practices by farmers and survey farmers’ perceptions

Fig. 1 The research framework of this study, which included four steps. STB, Science and Technology Backyard; NCP, North China Plain.

and top-dressing), amounts of phosphate fertilizer (P2O5) and potassium fertilizer (K2O), top-dressing date, sowing date, irrigation times and crop protection practices during the summer maize growing season. At maturity, the average planting density in terms of plants per hectare was determined from field investigation. Grain yields were measured by students from three plots, which were selected randomly in each field. Each plot was about 14.4 m2 (3 rows and 8 m long). Grain numbers per spike were counted from 8–10 plants in each plot, and three sets of 200 kernels were counted and weighted to determine the 1 000-grain weight. The 1 000-grain weight and grain yield were corrected to 15.5% moisture content. Current status of farmers summer maize production Nitrogen fertilizer partial factor productivity (PFPN), defined as the ratio of the crop yield per unit of applied N fertilizer, is used as an indicator of the NUE (Dobermann et al. 2000; Chen et al. 2014). (1) PFPN=Y/X Where, X is the N applied rate (expressed as kg ha–1) and Y is the maize yield (expressed as t ha–1), the same as below. To evaluate the environmental impact of summer maize production in the NCP, we used the reactive nitrogen (Nr) losses (which included total N2O emission, NO3 leaching and NH3 volatilization) intensity and N surplus as the main outcomes. The amounts of total N2O emission, NO3 leaching and NH3 volatilization per hectare were estimated using empirical models with N applied rate rather than measured directly (eqs. (2)–(5)), Cui et al. 2013). (2) NO3-N=4.46e(0.0094X)

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NH3-N=0.24X+1.30 (3) Total N2O emission=0.48e(0.0058X) + (4) Indirect N2O emission (5) Indirect N2O emission=0.01NH3-N+0.0075NO3-N Nr losses intensity was defined as the ratio of the Nr losses per unit of crop yield. N surplus in our study was defined as N applied rate minus above ground N uptake of maize, N uptake was derived from maize N uptake and the grain yield (Hou et al. 2012). Nr losses intensity=Nr losses/Y (6) (7) N surplus=X–Nuptake (8) Nuptake=22.3Y0.887 Economic benefit was defined as the difference between the gross income of grain and input cost (without including labor cost). Designing the improved management practices The grain number per hectare was calculated based on plant density and kernels per spike. Grain numbers and 1 000-grain weight are two important yield components. An agronomic diagnosis method was used to understand the relationship between summer maize yield and yield components. Using this method, the relationships between key yield component and relative management practices were established, and boundary line analysis was used to quantify the contribution of individual factors to maize yield (Webb 1972). The assumption of boundary line approach was that the data on the boundary line best represents the relationship between two variables, while other limiting factors’ potential influence can be considered minimal (Elliott and Dejong 1993), and the detailed process of creating boundary lines was according to previously studies by Wairegi et al. (2010) and Chen et al. (2018). In our study, the boundary lines of plant density, N applied rate and sowing date were all presented as quadratic curves, and that of total P2O5, total K2O, and basal N rate were sigmoid curves. The optimum values of quadratic curves and sigmoid curves were the recommended measures for each management practice, and were determined by calculating the first derivative and inflection point of the corresponding boundary lines. The improved management practices (IP) were composed of all recommended measures. Testing the improved management practices The designed IP were tested on farmers’ fields and compared with traditional farmer practices (FP). The experiment was conducted by 34 smallholder farmers in 2016 (16 in Nanxia, 11 in Yangong and 7 in Wangzhuang). The management practices and grain yields of the two treatments were all obtained at an early stage. In order to evaluate the farmers’ perceptions of improved management practices, face-toface interviews were carried out in three villages. The survey included five key production technologies, such as hybrid cultivar selection, planting density increasing, fertilizer spilt

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application, fertilizer selection and delayed harvest. In total, 300 farmers were selected to report their perceptions, and a total of 252 valid questionnaires were collected.

2.3. Data analysis The boundary line approach was applied using MS Excel 2010 (Microsoft, Redmond, USA) and figures were created in Sigmaplot 10 (Systat Software, San Jose, USA). Oneway ANOVA was applied in SAS for Windows (SAS Institute Inc., Cary, USA).

production of smallholder farms had high environmental impact. For example, the NH3 volatilizations in Nanxia, Yangong, and Wangzhuang were 71.19, 49.04 and 58.78 kg ha–1, respectively. NO3 leaching ranged from 22.89–412.09 kg ha–1 and total N2O emission ranged from 1.21 to 12.10 kg ha –1. Interestingly, the N surplus of summer maize production was negative in some fields, which indicates that after long periods of over application of N in a wheat/ maize double cropping system; the N mineralization is an important factor for crop N demand (Hartmann et al. 2014).

3.2. Improved summer maize management practices

3. Results 3.1. Yield and environmental impact of summer maize production Among all 235 smallholder farms monitored in 2015, the mean yield of summer maize was 9.1 t ha–1 in Nanxia village (6.6 to 12.0 t ha–1), 10.6 t ha–1 in Yangong Village (8.1 to 12.7 t ha–1), and 10.9 t ha–1 in Wangzhuang Village (7.8 to13.7 t ha–1). There was also a large variation in terms of yield components. For example, the plant density of all the monitored fields from 41 550 to 76 350 plants ha–1, and average densities were 59 040, 60 820 and 56 890 plants ha–1 in Nanxia, Yangong and Wangzhuang, respectively. The average grain numbers per spike in Nanxia was 511.8, which was much lower than the other two sites (571.1 and 625.7). In addition, the mean 1 000-grain weight in Nanxia was 324.1 g (251.9–384.9 g), 319.2 g (276.3–372.5 g) in Yangong, and 291.0 g (231.0–354.6 g) in Wangzhuang (Table 1). PFPN in the research plots was also significantly different (15.4–88.7 kg kg–1). Average N applied rate in Nanxia was 291 kg ha–1, which was higher than that in Yangong (199 kg ha–1) and Wangzhuang (238 kg ha–1). In addition, maize

In our study, summer maize yield depended more heavily on the grain numbers per hectare (P<0.01 and R2=0.5781) than on the 1 000-grain weight (Fig. 2). This result clearly indicates that a higher grain number should be the first (or foremost) measure to achieve a higher yield, but the yield limiting factors appeared during the grain formation stage, not the seed filling stage. Most farmers had a poor grain number that was attributed to the low plant density; among all the monitored plots, increasing plant density did not lead to a decrease of grain numbers’ boundary line, even at the highest value (Fig. 3-A). According to the structure of summer maize sowing machine in the NCP and the tendency of the boundary line between plant density and grain number, the seeding density of an improved production system was optimized to 72 000 seeds per hectare (row spacing, and planting distances were 60 cm ×23 cm). Excess N application in smallholder farms reduced the boundary values of the grain number. The minimum N applied to achieve the maximum grain number was 213.5 kg ha–1 (Fig. 3-B). The boundary lines between grain number and rates of P2O5 and K2O applied were all presented as parabola plus platform models, because the rates of P2O5 and K2O on smallholder farms were not too high enough

Table 1 Summer maize yield, yield components, N rates, reactive nitrogen (Nr) losses (NH3 volatilization, NO3 leaching and N2O emission) and N surplus amounts for 235 smallholder farmers in the North China Plain (NCP) in 2015 Site Nanxia

Yangong

Wangzhuang

NCP

n Min. Mean Max. Min. Mean Max. Min. Mean Max. Mean CV (%)

77

45

113

235

Yield (t ha–1) 6.6 9.1 12 8.1 10.6 12.7 7.8 10.9 13.7 10.2 13.8

Plant density Grain 1 000-grain N NH3 NO3 Total N2O N surplus (1 000 plants number per weight (g) (kg ha–1) (kg ha–1) (kg ha–1) (kg ha–1) (kg ha–1) –1 ha ) spike 44.79 59.04 76.35 50.56 60.82 72.5 41.55 56.89 68.41 58.33 10.3

387.9 511.8 627.8 472.5 571.1 679.1 468.6 625.7 747.3 577.9 12.4

251.9 324.1 384.9 276.3 319.2 372.5 231 291 354.6 307.2 9

174 291 482 136 199 312 102 238 375 247.8 30.9

43.06 71.19 116.86 33.84 49.04 76.18 25.78 58.78 108.26 60.8 30.2

22.89 85.19 412.09 15.96 32.81 83.76 11.63 55.49 294.2 59.9 85.2

1.92 4.16 12.1 1.51 2.33 4.32 1.21 3.14 9.65 3.3 49.4

32.17 132.97 323.78 –52.3 18.59 145.57 –64.16 54.44 228.08 72.6 113.4

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Nanxia

Yangong

y=1.734+0.0025*x R2=0.5781**

12 10 8 6 0

0

2 400

3 200

Wangzhuang

B 14

Yield (t ha–1)

Yiled (t ha–1)

A 14

4 000

4 800

Grain number (104 ha–1)

12 10 8 6 0

0200

250 300 350 1 000-grain weight (g)

400

450

Fig. 2 Relationships of maize yield to the grain number (A) and to 1 000-grain weight (B) in three sites. The black line is the trend line and the dotted lines are the 95% confidence interval.

to affect the grain number. The agronomically optimized rates of P2O5 and K2O application were all about 50–60 kg ha–1 (Fig. 3-C and D). The boundary line between basal N rate and grain number was also a parabola plus platform model, and the optimum basal N rate was 70–100 kg ha–1 (Fig. 3-E). According to the total N applied rate and experts’ suggestions, the basal N rate was set at 70 kg ha–1, which was a third of the total N amount. In addition, the boundary line between sowing date and grain number was quadratic (Fig. 3-F), and the optimized sowing date was an interval (10 June to 20 June), because of regional differences and management operations of smallholder farmers.

3.3. Effect of improved management practices on summer maize production Compared with traditional farmer practices, the seeding density of IP was increased by 18.0%, total N applied rate was reduced by 13.6%, basal N rate was reduced by 50.6%, top dressing N rate was increased 44.8%, and top-dressing date was delayed almost 20 days; P2O5 was reduced 15.2% and K2O was increased 8.5%. There was no difference in sowing date (Fig. 4); the smallholder farmers did not sow on different dates on two neighboring fields for economic reasons, including limited seeding machines and irrigation facilities. Yield and economic benefits Among all 34 experimental fields, the average yield of IP treatment was 11.1 t ha–1 (8.9–13.6 t ha–1), which was significantly higher than the FP treatment with an increase of 18.4% (P<0.01). The average yield of FP was 9.4 t ha–1 (6.7–11.4 t ha–1) (Fig. 5-A). The economic benefits of IP (15 013 CNY ha–1) increased by 20.5% compared with FP (12 454 CNY ha–1) (Fig. 5-D). On the other hand, the yield gap (yield increase over FP treatment) declined significantly with increases in the farmers’ yield. The increase ratios over the FP treatment

ranged from –6.5 to 49.8%; the probability of negative effect of IP would increase if the farmers’ yield more than 10.5 t ha–1 (Fig. 6-A). The decrease in yield increased the amount over farmers’ typical yield and lead to a decline in economic benefits, because the smallholder farmers who had negative effects of yield and economic output were almost the same (Fig. 6-A and C). Nitrogen partial factor productivity The average PFPN for IP was 53.1 kg kg–1 (42.2 to 64.9 kg kg–1), but was only 40.5 kg kg–1 (23.7–59.5 kg kg–1) for FP. The PFPN of IP was significantly higher than FP, increasing by 31.1% (–13.8– 103.3%, Figs. 5-B and 6-B). Moreover, the IP treatment also narrowed the variation of PFPN (variable coefficient of IP was 10.6%, and FP was 24.5%), but played a minimal role in reducing yield variation (10.6% of IP and 11.2% of FP). There was an obvious regional difference in the PFPN changes, which means that designing optimum N applied rate should consider regional differences. The decrease in PFPN gap (increased amount over farmers’ typical value) was also mainly attributed to yield gap (increased amount over farmers’ typical value) decrease (Fig. 6). Nitrogen surplus and reactive nitrogen losses intensity Improved management practices significantly reduced the N surplus amount. The average N surplus of IP was 20.9 kg ha–1 with a range from –16.3 to 55.6 kg ha–1, relative to the farmers’ traditional practice, which was about 78.7 kg ha–1 (–1.3–163.6 kg ha–1) (Fig. 5-C). In addition, there was a significant positive correlation between N surplus gap (decrease over farmers’ typical value) and farmers’ N surplus amount. Compared with FP, the N surplus of IP decreased by 79.1, 68.3 and –0.1% in Nanxia, Yangong and Wangzhuang, respectively (Fig. 7-A). Similarly, the correlation between Nr losses intensity of FP and the Nr losses intensities gap (decrease over farmers’ typical value) was also significantly positive. The Nr losses intensity of IP declined by 48.0, 17.0 and 0.0% compared to the

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B 4 500

4 500

Grain number (104 ha–1)

Grain number (104 ha–1)

A

4 000 3 500 3 000 2 500 2 000 0

y=–3 422+212.4x–1.468x R2=0.9577**

2

0

40

50

60

70

4 000 3 500 3 000 2 500 2 000 0

80

y=3 471+7.048x–0.0165x2 R2=0.8035** 0

100

Plant density (1 000 plants ha )

3 500 3 000 2 500 y=4 222/[1+exp(–(x+10.80)/14.80)] R2=0.8454** 0

60 120 180 P2O5 rate (kg ha–1)

Grain number (104 ha–1)

Grain number (104 ha–1)

500

600

4 000 3 500 3 000 2 500 2 000 0

240

F 4 500

4 000

4 000

3 500 3 000 2 500

y=4 208/[1+exp(–(x+1.484)/20.65)] R2=0.7048** 0

100

200

300

Basal N rate (kg ha–1)

Grain number (104 ha–1)

E 4 500 Grain number (104 ha–1)

400

D 4 500

4 000

2 000 0

300

–1

C 4 500

2 000 0

200

N rate (kg ha )

–1

y=4 264/[1+exp(–(x+47.21)/27.86)] R2=0.8258** 0

30

60 90 120 K2O rate (kg ha–1)

150

180

3 500 3 000 2 500

y=2 337+226.0x–6.787x2 R2=0.7989**

2 000 0 01/06 05/06 10/06 15/06 20/06 25/06 30/06 Sowing date (d/mon)

Fig. 3 Relationships of grain number to the plant density (A), 1 000-grain weight rate (B), P2O5 rate (C), K2O rates (D), basal N rate (E) and to sowing date (F) in the North China Plain (NCP). The lines are boundary lines which represent the best response trend of the data cloud.

FP in Nanxia, Yangong and Wangzhuang, respectively (Fig. 7-B). There were also regional differences in terms of environmental benefits between IP and FP.

4. Discussion 4.1. Summer maize yield and environmental impact In this study, the farmers’ yields ranged from 6.6 to 13.7 t ha–1, with about 58% of yield potential according to a previous

study in the NCP (Meng et al. 2013). Compared with the ISSM practices, farmers’ yield could also increase 40% (Chen et al. 2014). Moreover, there was a large variation in yields among different fields. These findings support the idea that a considerable space to increase the smallholder farmers’ maize yield. Compared with developed countries, the N applied rate on smallholder farms in our study was too high. For instance, average N applied rate was 248 kg ha–1 with a range from 102 to 482 kg ha–1. However, only 175 kg ha–1

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70

75%

50 0

10%

FP

IP

250 200 150 100 50

FP

IP

20/06

16/06 14/06 12/06 10/06 0

Total P2O5 rate (kg ha–1)

G

80

FP

IP

70 60 50 40 0

270 240 210 180 0

D

FP

IP

IP

FP

IP

FP

IP

FP

IP

200 150 100 50 0

F

50 45 40 35 30 25 0

H

FP

250

Days after sowing for top dressing (days)

18/06

300

100

Total K2O rate (kg ha–1)

Sowing date (d/mon)

50% 25%

60

0 E

90%

Top dressing N rate (kg ha–1)

Basal N rate (kg ha–1)

C

B

80

Total N rate (kg ha–1)

Seed density (1 000 plants ha–1)

A

80

60

40 0

Fig. 4 Comparison of seed density (A), total N rate (B), basal N rate (C), top dressing N rate (D), sowing date (E), days after sowing for top dressing (F), total P2O5 rate (G), K2O rate (H) between traditional farmer practices (FP) and improved management practices (IP). The black horizontal lines in the boxes indicate the 75th percentile (up), median (solid line across boxes) and 25th percentile of ordinate values (bottom), respectively; the upper and bottom bars outside the boxes show 90 and 10% values. The dotted lines across the boxes are the mean value.

N was needed to satisfy the mean yield level (10.2 t ha–1) according to the N uptake model (Hou et al. 2012). The

average N applied rate on irrigated maize in the Tri-Basin and Nebraska was 182 and 152 kg ha–1, respectively,

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B

16

Yield (t ha–1)

14 90%

10 10%

6 0

75% 50% 25%

10%

FP

**

60

90%

12

8

75% 50% 25%

50 40 30 20 0

IP

C 180

D

FP

IP

20 **

**

150 120

Economic benefit (1 000 CNY ha–1)

N surplus (kg ha–1)

70

** PFPN (kg kg–1)

A

90 60 30

16

12

0 –30

FP

8 0

IP

FP

IP

Fig. 5 Comparison of yield (A), nitrogen fertilizer partial factor productivity (PFPN; B), N surplus (C), economic benefit (D) between FP and IP. FP, traditional farmer practices; IP, improved management practices. PFPN, nitrogen fertilizer partial factor productivity. ** , significant difference at the P<0.01 level.

Nanxia 5 y=6766.6–0.5349x R2=0.2137

Yield gap (t ha–1)

4 3 2 1 0

C

8

Economic benefit gap (1 000 CNY ha–1)

–1

6

c 06

7

8 9 10 Yield-FP (t ha–1)

a b

11

12

Wangzhuang

B 30 PFPN gap (kg kg–1)

A

Yangong

y=43.727–0.7692x R2=0.6855

20 c

10

d e

0 –10 –10 20

30

40 50 PFPN-FP (kg kg–1)

a b

60

70

y=10.0290–0.5998x R2=0.2101

4 2 0 –2 –2 0 8

c

a b

10 12 14 16 Economic benefit-FP (1 000 CNY ha–1)

Fig. 6 Effect of improved management practices (IP) compared with traditional farmer practices (FP) for yield (A), nitrogen fertilizer partial factor productivity (PFPN; B) and economic benefit (C). The effect was expressed as the relationship between increased amounts and farmers’ value. The black line is the trend line and the dotted lines are the 95% confidence interval and prediction zones. Different letters (a–e) indicate different fields.

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Nanxia

B

160

Nr losses intensity gap (kg t–1)

N surplus gap (kg ha–1)

A

y=–10.0659+08630x R2=0.8630**

120 80 40 0 –40

Yangong

0

50 100 N surplus-FP (kg ha–1)

150

Wangzhuang

12 9

y=–6.7238+0.9074x R2=0.9547**

6 3 0 –3

8 12 16 Nr losses intensity-FP (kg t–1)

20

Fig. 7 Effect of improved management practices (IP) compared with traditional farmer practices (FP) of N surplus (A) and reactive nitrogen (Nr) losses intensity (B). The black line is the trend line and the dotted lines are the 95% confidence and prediction zones.

whereas the average yield ranged from 12.5–14.0 t ha–1 in 2005–2007 seasons (Grassini et al. 2011). In this study, a few farmers did not do topdressing because of their unwillingness and poor labor resource, which also led to low N input. Most smallholder farmers in China believe that more fertilizer produces higher yields, however, overuse of N cannot promise a persistent increase in grain yield and economic benefit because of diminishing returns (Cassman et al. 2003). Excess N resulted in a low N recovery and widespread environmental impacts (Le et al. 2010; Liu et al. 2013). To solve this problem, China government started the action called “Zero Growth in Fertilizer Use” in 2015. However, it is still too early to say how it could address this issue in rural China.

4.2. Grain numbers and fertilizer applied Maize yield is the product of the grain numbers per unit area and grain weight (Fageria et al. 2010). There is an obvious trade-off between grain number and grain weight in maize (Kiniry et al. 1990; Severini et al. 2011). In our study, the contribution of grain numbers per hectare to the grain yield was more important than that of the grain weight (Fig. 2). Therefore, grain number should be selected for agronomic diagnosis to design optimum management practice using boundary lines. Previous studies suggested that N did not have a significant effect on grain weight but increased maize grain number significantly (Sorkhi and Fateh 2014; Selassie 2015). These results showed that the N was more pronounced during ear formation and grain initiation period instead of the grain filling stage (Sorkhi and Fateh 2014). The boundary line between grain number and N applied

rate was a quadratic curve in this study, which indicates that the overused N could reduce grain number. Excessive N application affected the apical grain growth and resulted in a higher grain abortion, consequently reducing grain number (Shen et al. 2006). The average rate of P2O5 in our study was 60 kg ha–1 with a range from 0 to 215 kg ha–1 (data not shown). The downside is that only 15–20% of applied P is absorbed by crops in the growing season (Zhang et al. 2008), which may be due to the high accumulation of P in the soil (Zhong et al. 2004; Li et al. 2011). The maize yield response to P2O5 was not sensitive and the best way to have efficient P use is to apply as much P as P removed through the crop. This trend is illustrated by Fig. 3-C, which shows that most farmers achieved the desired yield with less P2O5 input. The average rate of K2O in our study was 56 kg ha–1 with a range from 0 to 156 kg ha–1 (data not shown). A previous study found that the mean K2O input of maize was only 26 kg ha–1 by surveying 2 765 farmers in 2009 (Wu et al. 2013). Similar to P2O5, maize yield response to the amount of K2O applied was not sensitive.

4.3. Improved management practices An effective optimized management practice was needed to overcome the yield-limiting factors for smallholder farmers, and because of their limited knowledge, the practice should be easy to expand. Numerous studies have proposed many technology packages to guide farmers’ field production (Kalra et al. 2007; Cui et al. 2008; Chen et al. 2011; Wang et al. 2012). For example, Chen et al. (2011) designed sowing date and plant population of summer maize using a Hybrid-Maize simulation model according to long-term weather data; N applied rate was based on soil mineral N

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testing, and P and K inputs were in terms of soil testing. Other researchers suggested an integrated agronomic management strategy which could increase the maize yield and PFPN by 2-year experiments (Wang et al. 2014). However, both of those approaches neglected farmers’ perceptions; the optimum value of management practice was mostly based on simulation models, experiment results, or experts’ suggestions. In this study, we designed an improved management practices as a standard and uniform technology package by analyzing farmers’ data. The boundary line relationship between grain number and plant density showed that most farmers had a low density; the mean maize density in the NCP was only 70–80% of that in the USA, which ranges from 75 000 to 82 500 plants ha–1 (Li and Wang 2009). The designed optimum N applied rate (210 kg ha–1) was consistent with Wang et al. (2014), who suggested N input of optimized practice treatment was 200 kg ha–1 (with a mean yield of 13.3 t ha–1). Chen et al. (2014) advised N rate of improved practice for maize was 214 kg ha–1 and with a mean yield of 12.6 t ha–1. Optimized P2O5 and K2O applied rates were slightly lower than previous studies according to the corresponding yield uptake requirement ( Li et al. 2012 ; Wu et al. 2014), this may be a result of P accumulation in soil and straw return policy in China ( Zhao et al. 2014; Bai et al. 2015). In addition, sowing date was also an important factor for the yield by influencing grain formation process (Tsimba et al. 2013). Optimized sowing date was also designed by boundary line analysis. In this study, delayed sowing was avoided by guaranteeing good irrigation conditions after sowing in the three research villages. Overall, IP increased maize yield and PFPN significantly compared with FP in the three research sites, and with better environmental benefits. Even though there was obvious regional variation among environmental benefits, the results of this study determined that the approach to design a uniform IP system in the NCP based on farmers’ production data was viable. In addition, rapid testing and precise N management strategy, such as remote sensing technology, should receive more attention to narrow the regional differences.

4.4. Farmers’ perception of improved management practices Smallholder farming systems are typical of China’s agricultural production. More than 90% of the farms are small ( Zhang et al. 2013, 2016). However, many challenges prohibit sustainable agricultural production of smallholder farms. Poor agricultural technologies, limited knowledge, climate change and scattered fields are some of the major constraints (Binam et al. 2004; Mariano et al. 2012; Jia et al. 2013; Zhao et al. 2016). From the results of our survey, the adoption rate of five key maize production technologies differed with a range of 8.6–65.1% (Table 2). Plant density is important to the grain number and yield, and maize under-seeding resulted in a 20.6% yield gap (Zhang et al. 2016). However, 91% of farmers made density decisions according to advice of the sow machinists, and they did not adopt technologies for increasing density. In fact, most farmers and machinists were not aware of the technology. Farmers’ perception was the most important limiting factor in technology adoption, which was followed by labor cost and resource and facilities; the economic cost of new technology was ranked last (Table 2). This result indicates that improving farmers’ knowledge of new technologies could be the most useful approach in achieving sustainable production.

5. Conclusion Designing a sustainable production system for smallholder farmers by overcoming important limiting factors is necessary for the development of green agriculture in China. Our findings showed that maize yield and nutrient use efficiency were poor and varied among different types of smallholders. This phenomenon was also observed for environmental impact. We designed a set of uniform improved management practices using the agronomic diagnostic method and tested the practices with local farmers. Improved management practices could increase yield and PFPN by 18.4 and 31.1% compared with farmers’ traditional practice. The N surplus and Nr losses intensity were also reduced significantly. We found that farmers’

Table 2 Adoption rates and limiting factors’ contributions of improved management practices on smallholder farms. Technology Increase density Formula fertilizer Delay harvest Split application Recommend seed 1)

Adoption rate on-farms (%)1) 8.6 17.1 35.6 51.4 65.1

Resource and facility 0 0 64.7 8.9 0

Limiting factors’ contribution (%) Labor cost Limited market Economic cost Farmers’ perception 0 41.6 0 58.4 2.1 35 10 52.9 6.5 0 0 28.8 84.4 0 6.7 0 0 33.3 0 66.7

The adoption rate of each technology was expressed as the proportion of farmers using this technology to the total number of farmers.

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perceptions of new technology were the main constraint in adopting new technologies. The results are valuable for policymakers and farmers to make decisions or adopt technologies. However, the effect of optimum N management was very different in three sites. This result indicates that the sustainable solution for smallholder farmers should place more attention to the precise N strategy, in addition to the uniform practices.

Acknowledgements This work was supported by the National Basic Research Program of China (2015CB150405) and the National Key R&D Program of China (2016YFD0200401).

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