Effect of Plant Density and Nitrogen Rate on PAR Absorption and Maize Yield

Achieving optimal balance between maize yield and water use efficiency is an important challenge for irrigation maize production in arid areas. In this study, we conducted an experiment in Xinjiang China in 2016 and 2017 to quantify the response of maize yield and water use to plant density and irrigation schedules. The treatments included four irrigation levels: 360 (W1), 480 (W2), 600 (W3), and 720 mm (W4), and five plant densities: 7.5 (D1), 9.0 (D2), 10.5 (D3), 12.0 (D4), and 13.5 plants m−2 (D5). The results showed that increasing the plant density and the irrigation level could both significantly increase the leaf area index (LAI). However, LAI expansion significantly increased evapotranspiration (ETa) under irrigation. The combination of irrigation level 600 mm (W3) and plant density 12.0 plants m−2 (D4) produced the highest maize yield (21.0–21.2 t ha−1), ETa (784.1–797.8 mm), and water use efficiency (WUE) (2.64–2.70 kg m−3), with an LAI of 8.5–8.7 at the silking stage. The relationship between LAI and grain yield and evapotranspiration were quantified, and, based on this, the relationship between water use and maize productivity was analyzed. Moreover, the optimal LAI was established to determine the reasonable irrigation level and coordinate the relationship between the increase in grain yield and the decrease in water use efficiency.

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Maize yield response to deficit irrigation during low-sensitive growth stages and nitrogen rate under semi-arid climatic conditions

Agricultural Water Management ◽

10.1016/j.agwat.2009.08.003 ◽

2010 ◽

Vol 97 (1) ◽

pp. 12-22 ◽

Cited By ~ 57

Author(s):

Cyrus Mansouri-Far ◽

Seyed Ali Mohammad Modarres Sanavy ◽

Seyed Farhad Saberali

Keyword(s):

Deficit Irrigation ◽

Maize Yield ◽

Climatic Conditions ◽

Yield Response ◽

Growth Stages ◽

Nitrogen Rate ◽

Semi Arid

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Adapting to climate change for food security in the Rift Valley dry lands of Ethiopia: supplemental irrigation, plant density and sowing date

The Journal of Agricultural Science ◽

10.1017/s0021859616000897 ◽

2016 ◽

Vol 155 (5) ◽

pp. 703-724 ◽

Cited By ~ 15

Author(s):

A. MULUNEH ◽

L. STROOSNIJDER ◽

S. KEESSTRA ◽

B. BIAZIN

Keyword(s):

Climate Change ◽

Food Security ◽

Plant Density ◽

Maize Yield ◽

Sowing Date ◽

Supplemental Irrigation ◽

Dry Spells ◽

Negative Impacts ◽

Improve Food Security ◽

Impacts Of Climate Change

SUMMARYStudies on climate impacts and related adaptation strategies are becoming increasingly important to counteract the negative impacts of climate change. In Ethiopia, climate change is likely to affect crop yields negatively and therefore food security. However, quantitative evidence is lacking about the ability of farm-level adaptation options to offset the negative impacts of climate change and to improve food security. The MarkSim Global Climate Model weather generator was used to generate projected daily rainfall and temperature data originally taken from the ECHAM5 general circulation model and ensemble mean of six models under high (A2) and low (B1) emission scenarios. The FAO AquaCrop model was validated and subsequently used to predict maize yields and explore three adaptation options: supplemental irrigation (SI), increasing plant density and changing sowing date. The maximum level of maize yield was obtained when the second level of supplemental irrigation (SI2), which is the application of irrigation water when the soil water depletion reached 75% of the total available water in the root zone, is combined with 30 000 plants/ha plant density. It was also found that SI has a marginal effect in good rainfall years but using 94–111 mm of SI can avoid total crop failure in drought years. Hence, SI is a promising option to bridge dry spells and improve food security in the Rift Valley dry lands of Ethiopia. Expected longer dry spells during the shorter rainy season (Belg) in the future are likely to further reduce maize yield. This predicted lower maize production is only partly compensated by the expected increase in CO2 concentration. However, shifting the sowing period of maize from the current Belg season (mostly April or May) to the first month of the longer rainy season (Kiremt) (June) can offset the predicted yield reduction. In general, the present study showed that climate change will occur and, without adaptation, will have negative effects. Use of SI and shifting sowing dates are viable options for adapting to the changes, stabilizing or increasing yield and therefore improving food security for the future.

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Combating Dual Challenges in Maize Under High Planting Density: Kernel Abortion and Stem Lodging

Frontiers in Plant Science ◽

10.3389/fpls.2021.699085 ◽

2021 ◽

Vol 12 ◽

Author(s):

Adnan Noor Shah ◽

Mohsin Tanveer ◽

Asad Abbas ◽

Mehmet Yildirim ◽

Anis Ali Shah ◽

...

Keyword(s):

Agricultural Land ◽

Plant Density ◽

Sugar Metabolism ◽

Strong Relationship ◽

Maize Yield ◽

Planting Density ◽

Lodging Resistance ◽

Genetic Characteristics ◽

Maize Productivity ◽

High Plant Density

High plant density is considered a proficient approach to increase maize production in countries with limited agricultural land; however, this creates a high risk of stem lodging and kernel abortion by reducing the ratio of biomass to the development of the stem and ear. Stem lodging and kernel abortion are major constraints in maize yield production for high plant density cropping; therefore, it is very important to overcome stem lodging and kernel abortion in maize. In this review, we discuss various morphophysiological and genetic characteristics of maize that may reduce the risk of stem lodging and kernel abortion, with a focus on carbohydrate metabolism and partitioning in maize. These characteristics illustrate a strong relationship between stem lodging resistance and kernel abortion. Previous studies have focused on targeting lignin and cellulose accumulation to improve lodging resistance. Nonetheless, a critical analysis of the literature showed that considering sugar metabolism and examining its effects on lodging resistance and kernel abortion in maize may provide considerable results to improve maize productivity. A constructive summary of management approaches that could be used to efficiently control the effects of stem lodging and kernel abortion is also included. The preferred management choice is based on the genotype of maize; nevertheless, various genetic and physiological approaches can control stem lodging and kernel abortion. However, plant growth regulators and nutrient application can also help reduce the risk for stem lodging and kernel abortion in maize.

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Identifying Ways to Narrow Maize Yield Gaps Based on Plant Density Experiments

Agronomy ◽

10.3390/agronomy10020281 ◽

2020 ◽

Vol 10 (2) ◽

pp. 281

Author(s):

Jian Li ◽

Man Wu ◽

Keru Wang ◽

Bo Ming ◽

Xiao Chang ◽

...

Keyword(s):

Management Practices ◽

Field Experiments ◽

Plant Density ◽

Population Level ◽

Maize Yield ◽

Limiting Factors ◽

Stable Range ◽

Maize Production ◽

Yield Gaps ◽

Loss Range

Exploring the maximum grain yields (GYs) and GY gaps in maize (Zea mays L.) can be beneficial for farmer to identify the GY-limiting factors and take adaptive management practices for a higher GY. The objective of this work was to identify the optimum maize plant density range and the ways to narrow maize GY gaps based on the variation of the GYs, dry matter (DM) accumulation and remobilization with changes in plant density. Field experiments were performed at the 71 Group and Qitai Farm in Xinjiang, China. Two modern cultivars, ZhengDan958 and ZhongDan909, were planted at 12 densities, ranging from 1.5 to 18 plants m−2. With increased plant density, single plant DM decreased exponentially, whereas population-level DM at the pre- (DMBS) and post- (DMAS) silking stages increased, and the amount of DM remobilization (ARDM) increased exponentially. Further analysis showed that plants were divided four density ranges: range I (<6.97 plants m−2), in which no DM remobilization occurred, DMBS and DMAS correlated significantly with GY; range II (6.97–9.54 plants m−2), in which the correlations of DMBS, DMAS, and ARDM with GY were significant; range III (9.54–10.67 plants m−2), in which GY and DMAS were not affected by density, DMBS increased significantly, and only the correlation of DMAS with GY was significant; and range IV (>10.67 plants m−2), in which the correlations of DMBS and ARDM with GY decreased significantly, while that of DMAS increased significantly. Therefore, ranges I and II were considered to be DM-dependent ranges, and a higher GY could be obtained by increasing the population-level DMAS, DMAS, and ARDM. Range III was considered the GY-stable range, increasing population-level DMBS, as well as preventing the loss of harvest index were the best way to enhance maize production. Range IV was interpreted as the GY-loss range, and a higher GY could be obtained by preventing the loss of HI and population-level DMAS.

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