Increasing Maize Production and Advancing Rational Water Allocation and Usage Based on the Optimal Planting Density and Irrigation Levels in Northwest China

Abstract

Increasing water-use efficiency by optimizing planting density and irrigation regimes in maize is crucial for food security under limited water resources. In this study, six plant densities (6.0, 7.5, 9.0, 10.5, 12.0, and 13.5 × 10⁴ plants ha⁻¹) and three irrigation amounts (300, 450, and 600 mm) were assessed to analyze the effects of planting density and irrigation amount on the yield and yield components. We also explored the combination of maize production capacity and maximizing economic returns in a planting region. The results demonstrated that as planting density increased, grain yield first increased and then decreased. The optimum planting density was 9 × 10⁴ plants ha⁻¹ when the irrigation amount was 300 mm, and 10.5 × 10⁴ plants ha⁻¹ under both 450 mm and 600 mm irrigation amounts. The relationship between planting density, irrigation amount, planting area, production capacity, and economic return showed that a current production capacity with 6.75 × 10⁴ plants ha⁻¹, 600 mm, and 1.066 million ha, could be achieved with 10.5 × 10⁴ plants ha⁻¹, 344 mm, and 1.1 million ha. The water-use efficiency of irrigation was increased by 74%. Current returns could be achieved with 10.5 × 10⁴ plants ha⁻¹, 405 mm, and 1.1 million ha, or by 9.0 × 10⁴ plants ha⁻¹, 449 mm, and 1.1 million ha. These observations demonstrated that appropriately increasing the planting area and reducing the irrigation amount per hectare under an optimal planting density could achieve a greater economic return and water-use efficiency than either increasing the planting density or reducing the irrigation volume alone in North Xinjiang. We concluded that concurrent optimization of the maize planting density, irrigation amount and plant area will not only meet the demand of food security but also achieve a rational use of water resources and farmland.

1. Introduction

Maize is the largest food crop in China, accounting for 40% of total food production, and more than 63% of production was used for fodder in 2021 according to the statistics of the Ministry of Agriculture and Rural Affairs in China. In recent years, living standards have increased, as has the demand for animal resources such as meat, eggs, and milk, which has also increased the demand for maize. At the same time, the COVID-19 pandemic has intensified the risk of the Chinese grain demand exceeding its supply, and the current ongoing war in Europe also increased the risk of insufficient maize supply worldwide, making it important to increase the maize production capacity and economic benefits to ensure food security [1,2]. China has limited cultivated lands and water resources in its maize cultivation area [3,4], which means that increasing maize production and efficiently allocating water resources by optimizing maize planting density and irrigation regimes is extremely important [5,6,7,8].

Increasing maize planting areas and grain yield per unit are the primary approaches for improving the maize production capacity. However, increasing maize planting areas is difficult due to limited cultivated land and water resources [9,10]. This is particularly true in Huang-Huai-Hai’s main maize area, where there is no potential for increasing cultivated land due to a high multiple-cropping index [11,12,13]. In the Northwest Maize District, water deficiency limits opportunities to increase the maize planting area [14,15]. Increasing planting density, applying high-yield maize hybrids, and adopting advanced cultivation techniques can effectively produce higher maize yields [5,16]. Compared to traditional methods, maize high-yield and integrating water–fertilizer cultivation techniques have the advantages of saving water, optimizing fertilizer usage doses [17,18,19], and reducing labor requirements, which could make it easier to irrigate and fertilize during the middle and later stages of the maize growth period [20,21]. Additionally, this approach can enhance water- and fertilizer-use efficiency [22], increase the land production rate and maize production capacity, and improve economic returns [19,23].

The Northwest Maize District of China is a typical arid–semiarid region. There are ample light and heat resources and a large landmass, but its limited water resources present a challenge for agricultural production [24]. A previous study found that the average maize planting density in China (from 6.0 × 10⁴ to 6.75 × 10⁴ plants ha⁻¹) was significantly lower than in America (from 6.75 × 10⁴ to 9.0 × 10⁴ plants ha⁻¹), and that Northwest China is a high-maize-density district with an average planting density of 6.77 × 10⁴ plants ha⁻¹ [25]. Therefore, increasing the maize planting density is a key approach to increasing maize production in China. In parts of Northwest China, agricultural water use accounts for more than 90% of the total water used, limiting its potential for development as an agricultural region [26,27,28]. At the same time, the Northwest Maize District was conceived to develop high-yield agriculture because of its high light and heat resources. In 2020, a maize production record was achieved at Qitai Farms, with 24.95 t ha⁻¹ and 13.5 × 10⁴ plant ha⁻¹ in a small area, which was achieved using a high planting density and integrating water and fertilizer cultivation techniques.

In recent years, the Chinese government has actively promoted water-saving and efficient agricultural techniques, providing an opportunity to develop new techniques for saving water and increasing yields during maize production. With the adoption of density-dependent hybrid maize, a high planting density, and drip irrigation techniques, high-yield maize production gradually increased to 15 t ha⁻¹ under a conventional amount of irrigation (600 mm) in Xinjiang. Previous research found that rationally decreasing the irrigation amount based on the conventional rate had no significant effect on high-yield maize production and could achieve both high yields and efficient water use [29,30]. Most previous studies focused on the improvement of water-use efficiency and yield by using a single agronomy practice or planting mode [31,32,33,34,35,36], and few integrated studies were conducted on the comprehensive application of technologies to improve irrigation water-use efficiency and increase and stabilize yield under the limited water resources from the aspects of planting density, irrigation regime, and planting area. Therefore, this study was conducted to identify the optimum combination of planting density and irrigation amount to maximize maize production and economic returns in North Xinjiang, and construct a regression function between grain yield, irrigation amount, and planting density to analyze the maize production capacity and economic returns with different planting areas, which will help guide future planting approaches in this region. In future research, we will integrate optimal fertilizer usage, water and nitrogen co-limitation, evapotranspiration, and the soil environment to perform an in-depth analysis and build an optimized water–fertilizer–district mode to improve water-use efficiency, nitrogen-use efficiency and ensuring food security.

2. Material and Methods

2.1. Experiment Site

The study was conducted at the Xinjiang Qitai experimental station of the Institute of Crop Sciences, CAAS, in 2019 and 2020. The Qitai experimental station is located at 43°50′ N, 89°46′ E, at an altitude of 1020 m. The soil texture was loam with a pH of 7.9 and a volume weight of 1.38 g cm⁻³. The contents of its organic matter, alkali-hydrolyzed nitrogen, available phosphorus, and available potassium were 13.3 g kg⁻¹, 82.9 mg kg⁻¹, 53.8 mg kg⁻¹, and 105.6 mg kg⁻¹, respectively. The precipitation in the maize-growing season from April to October was between 160 and 190 mm, the daily mean temperature was between 16.2 and 16.5 °C, and the duration of sunshine was 1696.5 h. The active accumulated temperature was between 3160 and 3499.5 °C, and the frost-free season was between 156 and 181 d.

2.2. Experimental Design

The experiment was designed according to a two-factor randomized block trial with three irrigation amounts and six planting densities using the maize cultivar “Jiushenghe 2468”. The three irrigation treatments were 300 mm, 450 mm, and 600 mm. The six planting densities were 6.0 × 10⁴ plants ha⁻¹ (D6), 7.5 × 10⁴ plants ha⁻¹ (D7.5), 9 × 10⁴ plants ha⁻¹ (D9), 10.5 × 10⁴ plants ha⁻¹ (D10.5), 12 × 10⁴ plants ha⁻¹ (D12), and 13.5 × 10⁴ plants ha⁻¹ (D13.5). Three replications were set as controls. The row length of each treatment block was 10 m, and the maize was planted in eight rows, which were planted with wide and narrow row spaces of 70 cm and 40 cm, respectively (Figure 1). The area of each treatment block was 44 m². The irrigation and fertilization method was drip irrigation under film, and a differential pressure fertilization pipe was used for fertilization. Water was prevented from moving between the plots using waterproof membranes, which partitioned the plots vertically and by 1 m wide buffer zones between the plots.

The sowing dates were 20 April 2019 and 15 April 2020, and the harvest date was 18 October in both years. The drip irrigation system included a single-wing labyrinth drip tape (Tianye, Inc.) placed in the middle of each narrow row. The dripper spacing was 30 cm, and the flow rate was 3.2 L h⁻¹ at an operating pressure of 0.1 MPa. A high-precision water meter (LXS-32F; Ningbo Water Meter Inc., Ningbo, China), pressure meter, and control valves were installed in each plot to ensure accurate discharge and stable pressure. All experimental plots were irrigated with 15 mm of water after sowing to ensure uniform and rapid germination. No irrigation was applied to the hardened seedlings for the first 60 days after sowing. Single water applications of 30, 46.7, and 63.3 mm were applied to the three irrigation treatments, respectively, at 9–10 d intervals during the whole irrigation period for a total of nine applications. Before sowing, basal fertilizers were applied (45 kg ha⁻¹ N, 75 kg ha⁻¹ P₂O₅, and 37.5 kg ha⁻¹ K₂O), and 309 kg ha⁻¹ N, 136.5 kg ha⁻¹ P₂O₅, and 150 kg ha⁻¹ K₂O were applied through drip irrigation during the growth period [37]. During the V6–V8 maize growth stages, 600 mL ha⁻¹ of the plant growth regulator, DA-6 Ethephon (Jinan, China), was applied. All weeds, diseases, and pests in the experimental plots were controlled.

2.3. Measuring Methods

2.3.1. Grain Yield and Yield Components

At harvest, an area of 11 m² (3 m long by four rows) was manually harvested from the four middle rows. The total number of plants and ears was counted. The kernel number per ear was measured using 20 randomly selected ears from each sampling area. Kernel weight was determined by counting 1000 kernels in triplicate. The grain yield and kernel weight were expressed at 14% moisture.

2.3.2. The Aboveground Dry Matter and LAI

Five typical plants in each treatment block were selected at the silking stage and maturity stage to measure the aboveground dry matter, which contained stems, leaves, sheaths, tassels, and ears at the silking stage, and contained stems, leaves, sheaths, tassels, seeds, and corncob in the maturity stage. All were dried at 105 °C for 30 min, then dried to a constant weight at 85 °C. Five typical plants in each treatment block were selected at the silking stage to measure the leaf area and calculate the LAI as Equation (1). L (cm) is the length of the leaf, B (cm) is the width of the leaf, D is the planting density per hectare, m (m²) is the number of leaves measured, R is the area of a hectare, 10,000 is the conversion coefficient.

$$LAI=\frac{{\displaystyle \sum _i^m{\displaystyle \sum _j^n(L_{ij}\times B_{ij})\times D}}}{m\times R\times 10,000}$$

(1)

2.4. The Maize Production Cost Survey

The questionnaire method was used to attain the maize production cost. It contained the main tasks from planting to harvest, which included seed, fertilizer, pesticide, mechanical operation, water and electricity, and labor with 22 items. The 350 farmer households, large grain growers, and agricultural cooperatives were investigated from 2019 to 2020 in the districts of Changji, Yili, and Tacheng based on production scale. The total planting area of maize for these three districts was roughly about 50% in Xinjiang, and the main extension districts for the high yield-density technique. The mean value of each item was used in this paper.

2.5. Data Statistic and Analysis

Statistical analyses were performed using Predictive Analytics Software (PASW) version 20.0 (IBM SPSS, Somers, New York, NY, USA). Data from each sampling date were analyzed separately. Means for datasets with three or more groups were tested using least significant difference tests (LSD, Fisher’s least significant differences). The effect of planting density and irrigation amount on grain yield, yield components, dry matter, and leaf area index were conducted by two-way ANOVA, with multiple comparisons. Pearson correlations were calculated to identify the relationships between the measured parameters. Excel 2016 was used for data processing and graphics production.

3. Results

3.1. Yield and Yield Components

Under the three irrigation amounts in two years, the harvest ear number increased as planting density increased (Figure 2). However, the kernel number per ear and the 1000-kernel weight both decreased as planting density increased. The grain yield first increased as the planting density increased, then decreased. Under the 300 mm irrigation, there was no significant different in grain yield between D9 and D7.5 in 2019; however, D9 was significantly higher than D7.5 in 2020.

Under 450 mm and 600 mm irrigation, in 2019, the grain yield of D10.5 had no significant difference from D9; however, D10.5 was significantly higher than D12. In 2020, the grain yield of D10.5 had no significant difference with D12; however, D10.5 was significantly higher than D9. Under similar yield conditions, lower the planting density can reduce the risk of lodging, abortion and premature aging. Therefore, the optimal planting densities were 9.0 × 10⁴ plants ha⁻¹ under 300 mm irrigation, and at 10.5 × 10⁴ plants ha⁻¹ under 450 mm and 600 mm irrigation.

Two-way ANOVA showed that grain yield, harvest ear number, and kernel number per ear were significantly affected by planting density (

Table 1. Two-way ANOVA of the grain yield and yield components.

Factors	Grain Yield		Harvest Ear Number		Kernel Number per Ear		1000-Kernel Weight
	F	p Value	F	p Value	F	p Value	F	p Value
Irrigation amount (I)	9.72	0.00 **	1.81	0.19	0.85	0.44	0.54	0.59
Planting density (D)	5.53	0.00 **	109.62	0.00 **	14.20	0.00 **	2.29	0.09
I $\times$ D	0.25	0.99	0.49	0.88	0.95	0.51	0.15	1.00
R2	0.73		0.97		0.82		0.44

Note: ** indicates significance at the p < 0.01 level.

). Grain yield was significantly affected by irrigation amount, and the yield components displayed no significance as the irrigation amount varied. The interaction of planting density and irrigation amount also had no significant influence on the grain yield and yield components.

3.2. Dry Matter and Leaf Area Index (LAI)

Under three irrigation amounts, the dry matter weight and LAI increased as planting density increased (Figure 3). LAI at the silking stage was significantly affected by planting density. The dry matter at the silking stage was significantly affected by irrigation amount, planting density, and the interaction between the two. However, the dry matter at the maturity stage was only significantly affected by planting density.

Correlation analysis demonstrated that the LAI at the silking stage and dry matter at the silking and maturity stages were significantly positively correlated with grain yield and harvest ear number; however, they were significantly negatively correlated with kernel number per ear and 1000-kernel weight (

Table 2. Pearson correlation analysis between the yield components, dry matter, and LAI.

Growth Indicators	Grain Yield	Harvest Ear Number	Kernel Number per Ear	1000-Kernel Weight
Dry matter at silking	0.90 **	0.90 **	−0.71 **	−0.57 *
Dry matter at maturity	0.85 **	0.72 **	−0.56 *	−0.34
LAI at silking	0.87 **	0.93 **	−0.72 **	−0.65 **

Note: * and ** indicate significance at the p < 0.05 and p < 0.01 level, respectively.

).

3.3. Analysis of Maize Production Capacity

There were four hypotheses used to determine the optimum combination of planting density, irrigation amount, and planting areas to maximize the maize production capacity in this planting region:

a

The total amount of water resources used to irrigate maize in the study region was 6.4 billion m³, which was calculated by the current maize planting areas (1.066 million ha) and the average irrigation amount (600 mm); the total water resource amount was a constant value.

b

The irrigation amount was within the threshold 300–600 mm, 600 mm was the conventional irrigation amount.

c

There were plenty of cultivated land resources, but planting areas were limited by water resources, which increased as the irrigation amount decreased. Therefore, the planting area was within the threshold 1.0–2.0 million ha based on the conventional irrigation amount.

d

According to this study’s results, the optimum planting density was within the threshold 90,000–105,000 per ha.

We constructed a regression function to determine the relationship between the two factors (planting density and irrigation amount) and grain yield.

Table 3. Regression analysis of grain yield with planting density and the three irrigation amounts.

	F-Test	Significance F	R Square
Regression Analysis	31.23	4.49 × 10−6 **	0.8064
	Coefficients	t-test	p value
Intercept	9719.14	8.69	3.05 × 10−7 **
Irrigation amount	8.93708	5.27	9.43 × 10−5 **
Planting density	0.04775	5.89	2.97 × 10−5 **

Note: ** indicates significance at the p < 0.01 level.

shows that the regression model significantly accounted for the relationship between the two factors (planting density and irrigation amount) and grain yield, which was depicted as a linear equation of the two unknowns, and the R² was 0.8064. The coefficients of planting density and irrigation amount were 0.04775 and 8.93708, respectively, both of which could explain the effect on grain yield, and the intercept was 9719.14. The fitting curve of regression is described in Figure 4. The function is written as Equation (2), where X₁ is the irrigation amount and X₂ is planting density. Water-use efficiency of irrigation (WUEI, kg m⁻³) was calculated using the grain yield-Y (kg ha⁻¹) and irrigation amount -X₁ (mm) in Equation (3), 10,000/1000 is conversion coefficient.

Y (X₁, X₂) = 8.93708X₁ + 0.04775X₂ + 9719.14

(2)

WUEI = Y/(X₁ * 10,000/1000)

(3)

According to the above hypotheses and Equation (2), the relationship between maize production capacity and planting density, irrigation amount, and planting area was drawn (Figure 5). It shows that as planting area increased, the irrigation amount per hectare decreased and the maize production capacity increased when the planting density was D9 or D10.5.

Current production capacity with D6.75, 600 mm, and 1.066 million ha, could be achieved using D10.5, 543 mm, 1.0 million ha; or using D9, 424 mm, and 1.1 million ha; and D10.5, 344 mm, and 1.1 million ha. The water-use efficiency of irrigation was calculated using Equation (3), and the value was 3.05, 3.37, 4.32, and 5.32 kg m⁻³, respectively. Therefore, using the above hypotheses and grain yield function (Equation (1)), the planting structure could be adjusted to attain the optimum combination of planting density, irrigation amount, and planting area to increase production capacity and efficiently use water when irrigating.

3.4. Analysis of Maize Production Economic Return

According to the cost components per hectare of the main production chain for maize (

Table 4. Cost components per hectare of the main production chain for maize.

Production Chain		Mean Value (yuan ha−1)	Price per Plant (yuan)	Price per mm (yuan)
Mechanical operation		3225
Irrigation and fertilization equipment		1595
Pesticide		150	-	-
Mulching		612	-	-
Labor		142.5
Seed		1292	0.01077	-
Fertilizer	DAP	1498	0.01248	-
	Urea	2299	0.01915	-
	Potassic fertilizer	3760	0.03133	-
The electricity and water use for irrigation		3375	-	9.375
Total		17,948.44	0.07374	9.375

), the seed and fertilizer costs increased as planting density increased, and the electricity and water costs relating to irrigation increased as the irrigation amount increased. Other costs only changed as the planting area varied. Therefore, the maize cost function per hectare, which contained two independent variables, can be described by Equation (4).

C (X1, X2) = 9.375X₁ + 0.07374X₂ + 5724.5

(4)

X₁ is irrigation amount (mm) and X₂ is planting density (plants ha⁻¹ ).

Integrating the grain yield per hectare function Y (X1, X2) and the maize cost per hectare function C (X1, X2) produced the maize economic return function Z (X1, X2, p, A), as in Equation (5).

Z (X1, X2, p, A) =(p * Y − C) * A =(9.84X1 + 0.02892X2 + 15171.65) * A

(5)

where X1 is the irrigation amount (mm), X2 is the planting density (plants ha⁻¹), p is the maize price (2.15 yuan per kilogram in this study), and A is the planting area (hectare).

According to the above hypotheses and Equation (5), the relationship between economic return and planting density, irrigation amount, and planting area was drawn (Figure 6). It shows that as the planting area increased, the irrigation amount per hectare decreased and the maize production economic return increased when the planting density was D9 or D10.5. Current returns with D6.75, 600 mm, and 1.066 million ha could be achieved using D10.5, 405 mm, and 1.1 million ha, or using D9, 449 mm, and 1.1 million ha. Therefore, using the above hypotheses and maize production economic return function (Equation (4)), the planting structure could be adjusted to attain the optimum combination of planting density, irrigation amount, and planting area, which will increase the economic returns of maize production increase water-use efficiency in the study region.

4. Discussion

4.1. The Optimum Planting Density for Maximum Grain Yield

In general, the grain yield–density relationship first increased as the planting density increased and then decreased in this study, which coincides with previous research results [38,39,40,41]. This could be explained by when the yield-improving effects of the increasing harvest number per unit were higher than the yield-reducing effects of the decreasing kernel number and the 1000-kernel weight, the grain yield improved, otherwise, it declined. This resulted from the competition between individual plants and plant groups for resources, and the competitive effect on both vegetative and reproductive development [42]. As the two-way ANOVA shows (Table 1), the grain yield, harvest ear number, and kernel number per ear were significantly affected by planting density, and it is commonly believed that the maize grain yield is positively related to harvest number and negatively related to kernel number and 1000-kernel weight. Additionally, increasing the irrigation amount could improve grain yield by increasing the kernel number per ear and kernel weight, which was due to increasing the assimilation of silking stage dry matter per plant [43,44,45,46].

4.2. The Optimum Combination for Maximum Production Capacity and Economic Returns

The grain production capacity in this study region is intensively restrained by water resources. A great strategy for ensuring the food security and ecosystem safety to identify the optimum combination of planting density, irrigation amount and planting area was undertaken using the integrating water and fertilizer cultivation technique. In this study, when the planting area was 1.1 million hectares, the combination of 10.5$\times 10$⁴ plants ha⁻¹ and 405 mm irrigation amount, and 9$\times 10$⁴ plants ha⁻¹ and 449 mm irrigation amount both achieved current revenues comparable with the common method used in this region (6.75$\times 10$⁴ plants ha⁻¹ and 600 mm irrigation amount).Theoretically, reducing the irrigation amount per hectare reasonably under the optimum planting density and increasing planting areas appropriately could achieve better economic returns than only increasing the planting density and irrigation amount. At the same time, reducing the irrigation amount per hectare as grain yield increases used water resources more efficiently.

4.3. Acknowledge of Limitation

The key limitation of this study is that the regression function between grain yield, irrigation amount, and planting density to analyze the maize production capacity to increase economic returns in different planting areas was developed without considering different fertilizers. Over the last two decades, the study of water–N colimitation has been confirmed to explain yield gaps in wheat, barley, and canola in the Mediterranean-type environments of Australia and Europe, and for maize in the Pampas of Argentina [47,48]. Water enhances root N acquisition by mass flow, whereby water and dissolved nutrients move from the soil into the plant’s root. This is driven by the physiological process of transpiration [49]. With the intensive risk of soil degradation and insufficiency of water, the integrated water and fertilizer cultivation mode has been increasingly adopted. In future research, the fertilizer factor will be integrated into the grain yield function and remote sensing will be used to obtain the spatial and temporal water and nitrogen distribution to optimize planting density, irrigation amount and fertilizer dose to attain the maximum yield and resource efficiency allocation. Additionally, this study was based on particular a hypothesis and theory, and only conducted in one of district of Xinjiang; however, Xinjiang has great variations in precipitation and light. Next, we will conduct more research in muti-ecological conditions combined with the distribution of regional water resources to furthermore evaluate the efficient of water allocation and usage.

5. Conclusions

As planting density increased, grain yield first increased to its highest level and then decreased, while the optimum planting density increased as the irrigation amount increased. In the study region, the optimal planting densities were 9.0 × 10⁴ plants ha⁻¹ under 300 mm irrigation, and at 10.5 × 10⁴ plants ha⁻¹ under 450 mm and 600 mm irrigation. Increasing planting areas and reducing the irrigation amount per hectare under the optimum planting density can achieve a better maize production capacity, economic returns, and water-use efficiency than by only increasing planting density and irrigation amount. Current returns with D6.75, 600 mm, and 1.066 million ha in the study region could be achieved using D10.5, 405 mm, and 1.1 million ha, or using D9, 449 mm, and 1.1 million ha. The water-use efficiency of irrigation was increased by 48.5% and 31.5%, respectively. We concluded that concurrent optimization of maize planting density, irrigation amount and plant area will not only meet the demand the food security but also achieve a rational use of water resources and farmland.