Intra‐specific competition affects the density tolerance and grain yield of maize hybrids

Zhai, Lichao; Li, Haishan; Song, Shijia; Ming, Bo; Li, Shaokun; Jia, Xin; Zhang, Lihua

doi:10.1002/agj2.20438

Cited by 5 publications

(6 citation statements)

References 41 publications

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“…Although reasonably increasing the plant density is an important agronomic practice for grain yield improvements [19,20], the present study showed that the effect of plant density on grain yield was not significant. The discrepancy may be associated with the density tolerance of tested maize cultivars in these studies, because selecting higher density-tolerant maize cultivars is the key to realize more grain yield under higher plant density [40]. Furthermore, the present results found that the effect of sowing date on grain yield was greater than cultivar and plant density effects, which is different from those of previous reports [10,25,41,42].…”

Section: Discussioncontrasting

confidence: 99%

The Optimal Cultivar × Sowing Date × Plant Density for Grain Yield and Resource Use Efficiency of Summer Maize in the Northern Huang–Huai–Hai Plain of China

et al. 2021

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In order to explore the optimal cultivar × sowing date × plant density for summer maize (Zea mays L.) in the Northern Huang–Huai–Hai (HHH) Plain of China, field experiments were conducted over two consecutive years (2018–2019) on a loam soil in the Northern HHH Plain. A split–split plot design was employed in this study, and the main plots included three cultivars (HM1: early-maturing cultivar; ZD958: medium-maturing cultivar; DH605: late-maturing cultivar); subplots consisted of three sowing dates (SD1: June 10; SD2: June 17; SD3: June 24); sub-sub plots include two plant densities (PD1: 6.75 × 104 plants ha−1; PD2: 8.25 × 104 plants ha−1). The results showed that the effects of cultivar and plant density on grain yield of summer maize were not significant, and the sowing date was the major factor affecting the grain yield. Delayed sowing significantly decreased the grain yield of summer maize, this was due mainly to the reduced kernel weight, which is associated with the lower post-anthesis dry matter accumulation. Moreover, radiation use efficiency (RUE), temperature use efficiency (TUE), and water use efficiency (WUE) were significantly affected by cultivar, sowing date, and plant density. Selecting early- and medium-maturing cultivars was beneficial to the improvements in RUE and TUE, and plants grown at earlier sowing with higher plant density increased the RUE and TUE. The interactive analysis of cultivar × sowing date × plant density showed that the optimum grain yields of all tested cultivars were observed at SD1-PD2, and the optimum RUE and TUE for HM1, ZD958, and DH605 were observed at SD1-PD2, SD2-PD2, and SD2-PD2, respectively. The differences in the optimum grain yield, RUE, and TUE among the tested cultivars were not significant. These results suggested that plants grown at earlier sowing with reasonable dense planting had benefits of grain yield and resource use efficiency. In order to adapt to mechanized grain harvesting, early-maturing cultivar with lower grain moisture at harvest would be the better choice. Therefore, adopting early-maturing cultivars grown with earlier sowing with reasonably higher plant density would be the optimal planting pattern for summer maize production in the Northern HHH Plain of China in future.

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Section: Discussioncontrasting

confidence: 99%

The Optimal Cultivar × Sowing Date × Plant Density for Grain Yield and Resource Use Efficiency of Summer Maize in the Northern Huang–Huai–Hai Plain of China

et al. 2021

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“…The difference might be explained by the reduced competition between the plants in cropping system 4, whereas in cropping system 3, the number of crops was 50% higher. Studies revealed that higher plant density leads to a higher competition for resources, resulting in lower yields per plant [42]. During our assessment, similar results were found for pigeon pea with cropping system 1 (PP) showing higher plant survival rates than cropping systems 2 (MPP) and 3 (MPPGS), which both had higher plant densities.…”

Section: Assessment Of the Above-ground Woody Biomass Production Of Pigeon Pea And Gliricidiasupporting

confidence: 85%

Allometric equations for estimating on-farm fuel production of Gliricidia sepium (Gliricidia) shrubs and Cajanus cajan (pigeon pea) plants in semi-arid Tanzania

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Background Fuelwood is considered to be the primary source of cooking energy in Tanzania and, due to ongoing deforestation, access to fuelwood is becoming more cumbersome. On-farm agroforestry systems can reduce dependency on off-farm fuel; however, the output of on-farm produced fuel is typically uncertain as production potentials are often not known. In this paper, we have developed allometric equations to model the above-ground woody biomass (AGWB) production from intercropped Gliricidia sepium (Jacq.) Kunth ex Walp (Gliricidia) shrubs and Cajanus cajan (L.) Millsp. (pigeon pea) plants. Methods We used a destructive sampling approach, for measuring the dendrometric characteristics, such as the root collar diameter at a 20 cm stem height (RCD20) and the stem height to estimate the AGWB production. The models are based on 112 Gliricidia and 80 pigeon pea observations from annually pruned plants. Seven allometric equations were fitted to derive the best-fit models for the AGWB production. Results We found that using a natural log-transformed linear model with RCD20 as a single predictor variable provides the highest explanatory value to estimate the AGWB production (Gliricidia: R2 = 95.7%, pigeon pea: R2 = 91.4%) while meeting Ordinary Least Square (OLS) estimator requirements. Adding stem height as an additional variable to predict the AGWB production does not improve model accuracy enough to justify the extra work for including it. Conclusions While on-farm pigeon pea plants produced a stable amount of woody biomass per annum, annual fuelwood production from Gliricidia shrubs increased over the years. Compared to the annual fuelwood consumption data from the literature, our results show that on-farm produced fuelwood can substantially offset the demand for off-farm fuel, potentially resulting in household fuelwood autarky.

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“…The semi‐dwarf wheat and rice phenotypes of the Green Revolution realized many traits of Donald's communal phenotype, except for the extreme uniculm type (Fischer, 2020 ; Jennings & Dejesus, 1968 ). The negative correlation between yield and competitive ability has been demonstrated experimentally in species of contrasting physiology and morphology, including cereals, pulses and oilseed crops (Hamblin & Donald, 1974 ; Harlan & Martini, 1938 ; Lake et al, 2016 ; López Pereira et al, 2017 ; Reynolds et al, 1994 ; Sakai, 1955 ; Sukumaran et al, 2015 ; Suneson & Wiebe, 1942 ; Zhai et al, 2021 ).…”

Section: Introductionmentioning

confidence: 98%

“…High‐yielding maize phenotypes feature more erect leaves that allow for higher stand density (Mantilla‐Perez & Salas Fernandez, 2017 ). The interaction between genotype and stand density is common in maize, highlighting genotype‐dependent variation in response to competition (Assefa et al, 2018 ; Hernández et al, 2014 ; Tollenaar, 1989 ; Zhai et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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Symmetric response to competition in binary mixtures of cultivars associates with genetic gain in wheat yield

Cossani

Sadras

2021

Evolutionary Applications

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The evolution in the definition of crop yield—from the ratio of seed harvested to seed sown to the contemporary measure of mass of seed per unit land area―has favoured less competitive phenotypes. Here we use binary mixtures of cultivars spanning five decades of selection for yield and agronomic adaptation to ask three questions. First, what is the degree of symmetry in the response of yield to neighbour; this is, if an older, more competitive cultivar increases yield by 10% with a less competitive neighbour in comparison to pure stands, would the newer, less competitive cultivar reduce yield by 10% when grown with older neighbour. Lack of symmetry would indicate factors other than competitive ability underly yield improvement. Second, what are the yield components underlying competitive interactions. Third, to what extent are the responses to neighbour mediated by radiation, water and nitrogen. A focus on yield components and resources can help the interpretation of shifts in the crop phenotype in response to selection for yield. The rate of genetic gain in yield over five decades was 24 kg ha−1 year−1 or 0.61% year−1. A strongly symmetrical yield response to neighbour indicates that yield improvement closely associates with a reduction in competitive ability. Response to neighbour was larger for grain number and biomass than for grain weight and allocation of biomass to grain. Under our experimental conditions, competition for radiation was dominant compared to competition of water and nitrogen. High‐yielding phenotypes had lower competitive ability for radiation but compensated with higher radiation use efficiency, a measure of canopy photosynthetic efficiency. Genetic and agronomic manipulation of the crop phenotype to reduce competitive ability could further improve wheat yield to meet the challenge of global food security.

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Intra‐specific competition affects the density tolerance and grain yield of maize hybrids

Cited by 5 publications

References 41 publications

The Optimal Cultivar × Sowing Date × Plant Density for Grain Yield and Resource Use Efficiency of Summer Maize in the Northern Huang–Huai–Hai Plain of China

The Optimal Cultivar × Sowing Date × Plant Density for Grain Yield and Resource Use Efficiency of Summer Maize in the Northern Huang–Huai–Hai Plain of China

Allometric equations for estimating on-farm fuel production of Gliricidia sepium (Gliricidia) shrubs and Cajanus cajan (pigeon pea) plants in semi-arid Tanzania

Symmetric response to competition in binary mixtures of cultivars associates with genetic gain in wheat yield

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