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Front. Agr. Sci. Eng.    2021, Vol. 8 Issue (4) : 525-544    https://doi.org/10.15302/J-FASE-2020355
RESEARCH ARTICLE
PLANT DENSITY, IRRIGATION AND NITROGEN MANAGEMENT: THREE MAJOR PRACTICES IN CLOSING YIELD GAPS FOR AGRICULTURAL SUSTAINABILITY IN NORTH-WEST CHINA
Xiuwei GUO1, Manoj Kumar SHUKLA2, Di WU1, Shichao CHEN1, Donghao LI1, Taisheng DU1()
1. Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China.
2. Plant and Environmental Sciences Department, New Mexico State University, Las Cruces, NM 88003, USA.
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Abstract

• A relative yield of 70% was obtained under both border and drip irrigation.

• Drip irrigation saved water and lowered yield variability compared to border irrigation.

• Drip irrigation led to accumulation of soil nitrogen and phosphorus in the root zone.

• Relative yield may increase 8% to 10% by optimizing field management.

• Plant density, irrigation and nitrogen are major factors closing yield gap in NW China.

Agriculture faces the dual challenges of food security and environmental sustainability. Here, we investigate current maize production at the field scale, analyze the yield gaps and impacting factors, and recommend measures for sustainably closing yield gaps. An experiment was conducted on a 3.9-ha maize seed production field in arid north-western China, managed with border and drip irrigation, respectively, in 2015 and 2016. The relative yield reached 70% in both years. However, drip irrigation saved 227 mm irrigation water during a drier growing season compared with traditional border irrigation, accounting for 44% of the maize evapotranspiration (ET). Yield variability under drip irrigation was 12.1%, lower than the 18.8% under border irrigation. Boundary line analysis indicates that a relative yield increase of 8% to 10% might be obtained by optimizing the yield-limiting factors. Plant density and soil available water content and available nitrogen were the three major factors involved. In conclusion, closing yield gaps with agricultural sustainability may be realized by optimizing agronomic, irrigation and fertilizer management, using water-saving irrigation methods and using site-specific management.
Keywords boundary line analysis      irrigation method      precision agriculture      spatial variability      yield gaps      yield-limiting factors     
Corresponding Author(s): Taisheng DU   
Just Accepted Date: 16 July 2020   Online First Date: 02 December 2020    Issue Date: 19 November 2021
 Cite this article:   
Xiuwei GUO,Manoj Kumar SHUKLA,Di WU, et al. PLANT DENSITY, IRRIGATION AND NITROGEN MANAGEMENT: THREE MAJOR PRACTICES IN CLOSING YIELD GAPS FOR AGRICULTURAL SUSTAINABILITY IN NORTH-WEST CHINA[J]. Front. Agr. Sci. Eng. , 2021, 8(4): 525-544.
 URL:  
https://academic.hep.com.cn/fase/EN/10.15302/J-FASE-2020355
https://academic.hep.com.cn/fase/EN/Y2021/V8/I4/525
Fig.1  Sampling location and elevation map of the study area. (a) Border irrigation in 2015; (b) drip irrigation in 2016. The area with red dashed line demonstrates the irrigation units with five irrigation events; the other irrigation units applied only the first four irrigation events. The red stars mark the location of the eddy covariance system.
No. Border irrigation in 2015 Drip irrigation in 2016
Date Irrigation amount (mm) Date Irrigation amount (mm)
0 Last winter >150 – –
1 May 30–June 1 158 April 24–25 50
2 Jun 29–July 2 158 June 10–12 51
3 July 20, 22 and 25 150 June 22–24 51
4 August 9 and 12 158 July 2–3 50
5 September 5–6 95 July 15–16 66
6 – – July 27–29 55
7 – – August 6–7 68
8 – – August 21–22 62
Total 679 452
Tab.1  Irrigation schedule summary
Parameter θFC BD0–20cm BD20–40cm BD40–60cm BD60–80cm BD80–100cm
pr1 2.281E-01 1.568E+00 1.642E+00 1.460E+00 1.600E+00 1.555E+00
pr2 –2.288E-02 6.582E-03 6.495E-03 –7.208E-03 –2.454E-02 3.084E-02
pr3 1.202E-03 3.729E-04 –1.786E-04 1.821E-03 2.280E-03 –1.260E-05
pr4 3.103E-03 –1.175E-03 –4.515E-04 9.433E-03 4.317E-03 –3.196E-03
pr5 1.727E-05 –2.200E-05 –2.170E-05 –1.049E-04 –2.940E-05 4.800E-05
pr6 –1.410E-04 –1.533E-04 –1.654E-04 –4.962E-04 –4.701E-04 –6.000E-04
Tab.2  Nonlinear regression parameters qFC and BD
Fig.2  Boundary line analysis (BLA) of different yield-limiting factors. (a) Plant density of female parents, 104 plants ha−1; (b) soil available potassium at 0–40 cm depth at the end of the growing season, mg·kg−1 K; (c) soil available nitrogen at 0–100 cm depth at the end of the growing season, mg·kg−1 N; (d) soil available water content at 0–100 cm depth on June 8 under border irrigation in 2015; (e,f) soil available water content at 0-100 cm depth on 6 August and 13 September 2016 under drip irrigation; (g) soil electrical conductivity at 0–40 cm depth when maize was sown, mS·cm−1; (h) soil pH in water at 0–40 cm depth when maize was sown. White circles (o) represent data serial in 2015; black circles (•) represent data serial in 2016; red crosses (×) represent the upper points eliminated as outliers; black crosses ( + ) represent the upper points used in fitting the upper boundary line.
ID Factor Optimum range Acceptable range Function form Parameters (significance codes and sample size)
I Num
(104 plants ha−1)		 7.61–7.84 6.57–8.88 min (y=ax2+bx+c, 1) a = –3.78E-02 (***); b = 0.584 (***); c = –1.26 (***); (F-test: ***, n = 5)
II AK
(mg·kg−1 K) §		 >80.5 >70.8 y = a(x–t1)+1; x<t1
y = 1; x>t1		 a = 5.17E-03 (***); t1 = 80.5 (※); (n = 6)
III AN
(mg·kg−1 N) §		 6.04–26.1 3.14–66.2 y = a(x–t1)+1; x<t1
y = 1; t1<x<t2
y = b(x–t2)+1; x>t2		 a = 1.73E−02 (***); b= –1.25E−03 (***); t1 = 6.04 (※); t2 = 26.1 (※); (n = 7)
IV-1 PropAWC
20150608 ¶		 >0.739 >0.679 y = a(x–t1)+1; x< t1
y = 1; x> t1		 a = 0.835 (***); t1 = 0.739 (※); (n = 5)
IV-2 PropAWC
20160806 ¶		 >0.371 >0.282 y = a(x–t1)+1; x< t1
y = 1; x>t1		 a = 0.559 (**); t1 = 0.371 (**); (n = 3)
V PropAWC
20160913 ¶		 >0.507 >0.422 y = a(x–t1)+1; x<t1
y = 1; x> t1		 a = 0.583 (***); t1 = 0.507 (***); (n = 6)
VI EC
(μS·cm−1)		 <224 <323 y = 1; x<t2
y = b(x–t2)+1; x>t2		 b = –5.06E-04 (***); t2 = 224 (※); (n = 9)
VII pH <8.41 <8.49 y = 1; t1<x<t2
y = b(x–t2)+1; x> t2		 b = –0.616 (***); t2 = 8.41 (***); (n = 14)
ID Percentage to the whole area under different reduction level (border irrigation in 2015) (%) Percentage to the whole area under different reduction level
(drip irrigation in 2016) (%)
Deficiency Optimum Excessive Deficiency Optimum Excessive
<0.95† [0.95, 1] 1 [0.95, 1] <0.95 <0.95 [0.95, 1] 1 [0.95, 1] <0.95
I 38 43 6 12 1 44 56 0 0 0
II 2 6 92 – – 3 9 87 – –
III 0 18 68 12 2 0 0 56 39 5
IV-1 5 5 89 – – – – – – –
IV-2 – – – – – 11 16 73 – –
V – – – – – 2 7 91 – –
VI – – 86 12 2 – – 78 19 3
VII – – 87 12 1 – – 100 0 0
Tab.3  Parameters of the BLA method for different yield-limiting factors and areal percentage under different yield reduction levels
Fig.3  Hierarchical cluster analysis results. (a) Dendrogram of the hierarchical cluster analysis; (b) the volume-weighted mean diameter of the class centroids in the soil profile at 0–100 cm depth (20 cm depth intervals) in each soil zone; (c) box plots of the volume-weighted mean diameter at 0–60 cm depth (orange lines) and 60–100 cm depth (purple lines) in each soil zone.
Fig.4  Box plots of remaining soil available nitrogen (at 0–100 cm depth), available phosphorus (0–40 cm depth) and available potassium (0–40 cm) at the end of the growing seasons. (a) Soil available nitrogen; (b) soil available phosphorus; (c) soil available potassium. The green line represents the 2015 results and the black line the 2016 result. Significance by independent-samples t-test using the SPSS 20.0 software package: ***, P<0.001; **, P<0.01; *, P<0.05; n.s., not significant (P>0.05).
Fig.5  Spatial interpolation plots of remaining soil available nitrogen (at 0–100 cm depth), available phosphorus (0–40 cm depth) and available potassium (0–40 cm) at the end of the growing seasons. (a–c) Soil available nitrogen, available phosphorus and available potassium in 2015; (d–f) soil available nitrogen, available phosphorus and available potassium in 2016. Different soil zones are also shown.
Fig.6  Interpolation maps of the relative yield, locations of different soil zones and locations where yield was affected by different yield-limiting factors. (a,f) Relative yield in 2015 (a) and 2016 (f); (b,g) locations where plant density deficit or available potassium deficit occurred in 2015 (b) and 2016 (g); (c,h) locations where water deficit, available nitrogen deficit or surplus occurred in 2015 (c) and 2016 (h); (d,i) locations where high soil salinity or high pH occurred in 2015 (d) and 2016 (i); (e,g) predicted relative yield when all the considered yield-limiting factors (not including unknown factors) were at the optimum ranges in 2015 (e) and 2016 (g). The relative yield map in 2015 is also shown in (b–d) and the relative yield map in 2016 is shown in (g–i). Soil texture zones are also depicted in (a), (e), (f), and (j).
Zone (year) Soil texture
(0–60 cm/60–100 cm)		 Percentage of the whole area
(samples count) (%)		 AN0–100 cm
(mean±SD)
(mg·kg−1 N)		 AP0–40 cm
(mean±SD)
(mg·kg−1 P)		 AK0–40 cm
(mean±SD)
(mg·kg−1 K)
All Zones (2015) – 100 (185) 16.3±17.3 12.1±3.3 104.3±21.2
Zone 1 (2015) Silt loam/Silt loam 41 (71) 23.1±21.5 b¶ 13.2±3.3 c¶ 116.8±22.6 a¶
Zone 2 (2015) Loam/Silt loam 30 (53) 14.1±14.0 cd 11.6±2.9 d 103.5±13.5 b
Zone 3 (2015) Sandy loam/Silt loam 16 (36) 12.0±11.4 cd 10.2±3.3 e 87.5±15.7d
Zone 4 (2015) Sandy loam/Loamy sand 13 (25) 8.1±10.0 d 13.0±2.9 cd 94.7±14.7 cd
All Zones (2016) – 100 (184) 30.4±25.0 14.8±3.4 101.9±22.6
Zone 1 (2016) Silt loam/Silt loam 41 (82) 39.3±31.1 a 14.9±3.7 ab 112.9±22.4 a
Zone 2 (2016) Loam/Silt loam 30 (64) 25.2±18.0 b 15.0±3.2 ab 95.4±19.1 c
Zone 3 (2016) Sandy loam/Silt loam 16 (23) 21.4±9.4 b 13.5±2.9 bc 87.8±17.6 cd
Zone 4 (2016) Sandy loam/Loamy sand 13 (15) 17.8±8.3 bc 15.8±2.6 a 90.5±17.2 cd
Tab.4  Summary of the soil texture zones and residual soil NPK
Zone (year) RY
(mean±SD)		 CV (%) Yieldmax
(Mg·ha−1)		 P mm I mm ET† mm ET/(P+I)
(%)
All Zones (2015) 0.70±0.13 18.8 6.25 149 679 508 61
Zone 1 (2015) 0.70±0.14 ab¶ 19.7
Zone 2 (2015) 0.73±0.11 a 15.6
Zone 3 (2015) 0.71±0.14 ab 19.1
Zone 4 (2015) 0.65±0.14 b 21.2
All Zones (2016) 0.71±0.09 12.1 7.02 115 452 518 91
Zone 1 (2016) 0.71±0.08 a 11.3
Zone 2 (2016) 0.71±0.10 ab 13.5
Zone 3 (2016) 0.73±0.08 a 10.5
Zone 4 (2016) 0.69±0.09 ab 12.7
Tab.5  Crop yield and water consumption
Zone (year) Count RY
(mean±SD)		 RY'
(mean±SD)		 Δ (%) Δε (%)
All Zones (2015) 185 0.70±0.13 0.78±0.13 7.9 21.6
Zone 1 (2015) 71 0.70±0.14 0.76±0.14 5.9 24.2
Zone 2 (2015) 53 0.73±0.11 0.82±0.12 8.5 18.0
Zone 3 (2015) 36 0.71±0.14 0.79±0.13 7.9 21.3
Zone 4 (2015) 25 0.65±0.14 0.77±0.11 12.4 22.5
All Zones (2016) 184 0.71±0.09 0.81±0.09 9.7 19.2
Zone 1 (2016) 82 0.71±0.08 0.78±0.08 7.0 21.8
Zone 2 (2016) 64 0.71±0.10 0.82±0.09 11.8 17.6
Zone 3 (2016) 23 0.73±0.08 0.84±0.09 11.1 15.9
Zone 4 (2016) 15 0.69±0.09 0.83±0.10 13.8 16.9
Zone (Year) ΔNum (%) ΔPropAWC (%) ΔAN (%) ΔEC (%) ΔAK (%) ΔpH (%)
All Zones (2015) 4.1 1.4 1.1 0.4 0.4 0.6
Zone 1 (2015) 3.9 0.1 0.8 0.5 0.1 0.7
Zone 2 (2015) 5.3 1.0 1.0 0.6 0.0 0.5
Zone 3 (2015) 3.5 1.2 1.2 0.1 1.5 0.5
Zone 4 (2015) 3.3 6.0 1.9 0.1 0.4 0.8
All Zones (2016) 4.9 2.5 1.0 0.6 0.7 0.0
Zone 1 (2016) 3.4 0.6 1.6 0.9 0.4 0.0
Zone 2 (2016) 7.0 3.1 0.6 0.4 0.7 0.0
Zone 3 (2016) 4.4 4.5 0.2 0.0 1.8 0.1
Zone 4 (2016) 4.8 7.5 0.1 0.1 1.4 0.0
Tab.6  Yield gaps determined by different yield-limiting factors in different soil zones
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