Response of cotton fruit growth, intraspecific competition and yield to plant density

Li, Xiaofei; Han, Yingchun; Wang, Guoping; Feng, Lu; Wang, Zhanbiao; Yang, Beifang; Du, Weihua; Lei, Yaping; Xiong, Shiwu; Zhi, Xiaoyu; Xing, Feifei; Fan, Zhengyi; Xin, Minghua; Li, Yabing

doi:10.1016/j.eja.2019.125991

Cited by 34 publications

(14 citation statements)

References 39 publications

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“…Increased plant density promotes a decrease in per-plant leaf area directly reducing the interception of photosynthetically active radiation, decreasing the DM accumulation at the plant-level, correlation already demonstrated in the current work between DM and leaf area (Table 2). Similar to as was reported in our study, Carvalho, Távora, Pinho, and Pitombeira (2000) documented that high LAI was linked to superior plant density but with more light intercepted by the upper leaves relative to the lower leaves within the crop canopy (Li, Zhang, Dai, Dong, & Kong, 2019;Smith & Whitelam, 1997;Yao, Zhang, Yi, Zhang, & Zhang, 2016), modifying the plant morphology, radiation use efficiency, and changing the DM accumulation (Bezerra et al, 2009;Kamara et al, 2014;Li et al, 2019Li et al, , 2020Wu et al, 2018). For this study, the lower level of water supply in the 2011 season resulted in cowpea plants with smaller leaf area and aboveground DM at the plant level (even under low plant density) relative to the rest of the evaluated growing season (Figure 4a).…”

Section: Crop Sciencesupporting

confidence: 89%

“…Under low plant density, perplant yield increases although per-unit area yield is lower (Bezerra et al, 2008(Bezerra et al, , 2009Carciochi et al, 2019;Duncan, 1986;Kamara et al, 2014;Santos, 2013), especially if genotypes with less plasticity are used and in low-yielding environments (Bezerra et al, 2009;Carciochi et al, 2019;Rondanini et al, 2017). By increasing plant density, perplant yield decreases but with an increase in per-unit area yield (Bezerra et al, 2008(Bezerra et al, , 2009Kamara et al, 2014;Li et al, 2020). Competition at high intensity in early stages of crop development can favor the emergence of unproductive plants and cause a decrease in the final productive stand and yield (Bezerra et al, 2008;Soratto et al, 2012) as well as increases the risk of lodging and impairing mechanized harvest procedures (Carciochi et al, 2019;Farias et al, 2019).…”

Section: Crop Sciencementioning

confidence: 99%

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Agronomic optimal plant density for semiupright cowpea as a second crop in southeastern Brazil

et al. 2020

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Cowpea [Vigna unguiculata (L.) Walp.] cultivars with high yield potential and suitable plant architecture for mechanized harvesting have recently shown a growing interest to be cultivated as a second crop in fall–winter season of southeastern Brazil. The agronomic optimal plant density (AOPD) is one of the main management factors defining attainable yield. However, in the scientific literature for semiupright cowpea cultivars grown in fall–winter season in southeastern Brazil the AOPD is not yet clearly defined. A 3‐yr study was conducted to evaluate the effect of five plant densities (100,000–500,000 plants ha−1) on growth, seed yield and its components for two cowpea cultivars (BRS Guariba and BRS Novaera) in Botucatu, southeastern Brazil. The AOPD for cowpea increased as the attainable yield improved. The increase in plant density promoted increases in leaf area index (LAI) and the position (measured in height) of the first pod insertion for cowpea plants, but negatively affected per‐plant scale surviving rate, leaf area, aboveground dry matter (DM), and yield resulting from intraspecific competition. Maximum cowpea seed yields were achieved with AOPD ranging from 216,630 to 290,537 plants ha−1 (8–24% greater than under the lowest plant density). On average, 259,000 plants ha−1 was estimated for the AOPD to achieve maximum seed yield of semiupright cowpea grown as a second crop in fall–winter season in southeastern Brazil.

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Section: Crop Sciencesupporting

confidence: 89%

Section: Crop Sciencementioning

confidence: 99%

Agronomic optimal plant density for semiupright cowpea as a second crop in southeastern Brazil

et al. 2020

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“…In the breeding stage, the corn stand count can provide the seed germination rate for breeders and geneticists. As for the planting stage, stand counting can provide the plant population density to farmers, which is an important trait for crop yield and quality [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. This information can also help farmers with some essential management practices, such as determining the necessity of replanting [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

A Convolutional Neural Network-Based Method for Corn Stand Counting in the Field

Wang

Xiang

Tang

et al. 2021

Sensors

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Accurate corn stand count in the field at early season is of great interest to corn breeders and plant geneticists. However, the commonly used manual counting method is time consuming, laborious, and prone to error. Nowadays, unmanned aerial vehicles (UAV) tend to be a popular base for plant-image-collecting platforms. However, detecting corn stands in the field is a challenging task, primarily because of camera motion, leaf fluttering caused by wind, shadows of plants caused by direct sunlight, and the complex soil background. As for the UAV system, there are mainly two limitations for early seedling detection and counting. First, flying height cannot ensure a high resolution for small objects. It is especially difficult to detect early corn seedlings at around one week after planting, because the plants are small and difficult to differentiate from the background. Second, the battery life and payload of UAV systems cannot support long-duration online counting work. In this research project, we developed an automated, robust, and high-throughput method for corn stand counting based on color images extracted from video clips. A pipeline developed based on the YoloV3 network and Kalman filter was used to count corn seedlings online. The results demonstrate that our method is accurate and reliable for stand counting, achieving an accuracy of over 98% at growth stages V2 and V3 (vegetative stages with two and three visible collars) with an average frame rate of 47 frames per second (FPS). This pipeline can also be mounted easily on manned cart, tractor, or field robotic systems for online corn counting.

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“…By adjusting row and plant spacing, an ideal planting population can be established, which affects initial development, canopy architecture, and yield of several crops, including yacon (Mota-Júnior et al, 2014;Torales et al, 2015;Li et al, 2020). Row and plant spacing determine the spatial distribution of roots and the ability of a crop to incorporate water and nutrients from the soil (Patanè et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Planting configuration can as well influence the plant's response to light, as increased planting may improve light interception capacity, which would reflect an increased photosynthetic capacity (Li et al, 2020). A lower density of plants can reduce light absorption rates, reducing yield potential (Li et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Developing row spacing and planting density recommendations for yacon (Smallanthus sonchifolius) in tropical highland conditions

Carvalho

Pedrosa

Oliveira

et al. 2021

Chil. j. agric. res.

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Yacon (Smallanthus sonchifolius [Poepp.] H. Rob., Asteraceae) is an Andean plant being explored as a functional food, due to high concentrations of fructooligosaccharides (FOS), a natural sweetener capable of reducing blood lipids and glucose levels, supporting prevention of chronic diseases, such as diabetes. Yacon commercial production is still being established, thus cropping practices and their impact on profitability are yet poorly explained. Improved planting recommendations are demanded, as it affects crop yield. In this background, a field trial was conducted to determine row spacing and plant populations for yacon production in tropical highland areas. The experimental design was a randomized complete block, in a split-plot scheme, where blocks were three row spacings (0.8, 1.0 and 1.2 m) and subplots covered three spacings between plants (0.4, 0.5 and 0.6 m), with four replicates. At harvest time, evaluations on accumulation of dry mass in the plant and tuber root production data (number, weight, total productivity, and per class) were made. Profitability indicators were estimated (gross income, operational profit, and benefit-cost ratio). Data suggest productive and economic variations among tested planting configurations, as an example higher productivity (31.5 t ha -1 ) and positive benefit-cost ratio (2.51) when using 1.0 m between rows and 0.4 m between plants for the conditions tested. These results point out the prospect to select better crop management practices for yacon, cutting costs, and increasing yield. Another key find was yacon's capacity to overcome intense drought and temperature stress, not previously documented for this crop. Therefore, yacon is a new crop with potential to boost farming income through crop diversification.

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Response of cotton fruit growth, intraspecific competition and yield to plant density

Cited by 34 publications

References 39 publications

Agronomic optimal plant density for semiupright cowpea as a second crop in southeastern Brazil

Agronomic optimal plant density for semiupright cowpea as a second crop in southeastern Brazil

A Convolutional Neural Network-Based Method for Corn Stand Counting in the Field

Developing row spacing and planting density recommendations for yacon (Smallanthus sonchifolius) in tropical highland conditions

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