Modeling leaf area development in soybean (<i>Glycine max</i> L.) based on the branch growth and leaf elongation

Nakano, Satoshi; Purcell, Larry C.; Homma, Koki; Shiraiwa, Tatsuhiko

doi:10.1080/1343943x.2019.1702468

Cited by 10 publications

(19 citation statements)

References 29 publications

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“…To increase the productivity of soybeans, it is necessary to use new techniques and improve those used, especially those that interfere with morphological and production components (Bawa et al, 2019). Among the techniques mentioned, the association of spatial arrangements and plant density has stood out as potential tools for increasing productivity (Nakano et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Plasticity on 'BRS 8381' Glycine max (L.) Merril Agronomic Attributes in Different Years of Cultivation in Roraima

Smiderle¹,

Menezes²,

Souza³

et al. 2020

JAS

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Soy has high plasticity, which expands its capacity to adapt to different environmental and management conditions, through morphological changes in production components. Thus, the objective was to evaluate the effects of different densities of soybean cultivar BRS 8381 on agronomic characteristics. The experiment was implemented and conducted in a Cerrado area, in the Agua Boa of Embrapa Roraima experimental field. A randomized block design was used in a 2*4 factorial scheme, with four replications. The treatments consisted of two years of soybean cultivation (2015 and 2016), sown in four plant densities (10, 14, 18 and 22 plants per linear meter). Agronomic characteristics were evaluated: plant height; height of insertion of the first pod; stem diameter; number of nodes on the stem; number of pods per plant; number of branches per plant; dry mass of the plant; harvest index and grain yield. The highest average productivity of 'BRS 8381' soybeans in the Cerrado area of Roraima is obtained with a population of 387.448 plants per hectare. 'BRS 8381' plants at density 22 plants m -1 linear are not suitable for the Cerrado conditions of Roraima. The cv. BRS 8381 is influenced by rainfall and temperature factors in the Cerrado conditions of Roraima.

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Section: Introductionmentioning

confidence: 99%

Plasticity on 'BRS 8381' Glycine max (L.) Merril Agronomic Attributes in Different Years of Cultivation in Roraima

Smiderle¹,

Menezes²,

Souza³

et al. 2020

JAS

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“…A rhizo canopy arises from the dynamic interactions among an individual plant, its neighbors, and the pedosphere, like a forest canopy expands dynamically in time and space. Even leaf area development and canopy closure of row crops, like soybean, is a complex process with incompletely understood effects of environment, neighbors, and management [ 64 , 65 ]. Analogous attempts to characterize dynamic heterogeneity of tree branch structure [ 66 ], a forest canopy [ 67 , 68 ], a city skyline [ 69 ], or traces of even historic human land use [ 70 ], have traditionally relied on transects, but technologies such as light detection and ranging (LiDAR) have dramatically increased coverage, resolution, and changed the way these processes are conceptualized ([ 71 ]; Anderson et al, 2006).…”

Section: Discussionmentioning

confidence: 99%

An Analysis of Soil Coring Strategies to Estimate Root Depth in Maize ( Zea mays ) and Common Bean ( Phaseolus vulgaris )

et al. 2020

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A soil coring protocol was developed to cooptimize the estimation of root length distribution (RLD) by depth and detection of functionally important variation in root system architecture (RSA) of maize and bean. The functional-structural model OpenSimRoot was used to perform in silico soil coring at six locations on three different maize and bean RSA phenotypes. Results were compared to two seasons of field soil coring and one trench. Two one-sided T-test (TOST) analysis of in silico data suggests a between-row location 5 cm from plant base (location 3), best estimates whole-plot RLD/D of deep, intermediate, and shallow RSA phenotypes, for both maize and bean. Quadratic discriminant analysis indicates location 3 has ~70% categorization accuracy for bean, while an in-row location next to the plant base (location 6) has ~85% categorization accuracy in maize. Analysis of field data suggests the more representative sampling locations vary by year and species. In silico and field studies suggest location 3 is most robust, although variation is significant among seasons, among replications within a field season, and among field soil coring, trench, and simulations. We propose that the characterization of the RLD profile as a dynamic rhizo canopy effectively describes how the RLD profile arises from interactions among an individual plant, its neighbors, and the pedosphere.

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“…In 2013 and 2014, the nitrogen concentration of each organ was measured using the dry combustion method (Sumigraph, NC-22 F, Sumika Chemical Analysis Service CO, Ltd, Japan) for one replicate. A fraction of canopy radiation interception (FRI) was measured using digital image analysis as previously reported (Nakano et al, 2020). Soil samples were obtained from a depth of 0.05-0.15 m at five randomly selected points before the basal fertilizer was applied.…”

Section: Field Experiments and Measurementsmentioning

confidence: 99%

“…The model consisted of six submodels for predicting phenology, leaf area, biomass production, biomass partitioning, nitrogen supply, and nitrogen partitioning ( Figure 1 and Table 2). The phenology and leaf area submodels specific to Japanese soybean cultivars have already been developed (Nakano et al, 2015(Nakano et al, , 2020. However, the previous leaf area submodel has only covered leaf area growth before seed filling.…”

Section: Model Developmentmentioning

confidence: 99%

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Modeling biomass and yield production based on nitrogen accumulation in soybean grown in upland fields converted from paddy fields in Japan

Nakano

Homma

Shiraiwa

2021

Plant Production Science

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Crop models can help in identifying constraints to crop production and enhancing crop yield. Because one of the major constraints is the availability of nitrogen for seed production, evaluation of nitrogen accumulation is important for soybean crop models. In the present study, we developed a soybean crop model to evaluate biomass production and nitrogen accumulation of Japanese soybean cultivars grown in upland fields converted from paddy fields. The model simplified the effects of leaf nitrogen deficit on biomass production by reducing only the leaf area while maintaining leaf nitrogen concentration, and we introduced a zero-order reaction kinetics model to estimate soil nitrogen supply. The proposed model was observed to simulate the accumulation of aboveground biomass and nitrogen content in both nodulating and nonnodulating soybean cultivars until the mid-seed filling period. The model accounts for 94% of the observed variations in aboveground biomass and nitrogen content and for 69% of the observed variation in seed biomass. The normalized root mean square errors of aboveground biomass, nitrogen content, and seed biomass are 19.3%, 19.6%, and 20.2%, respectively. Because the model does not include the effects of soil water status on pod formation and nitrogen fixation, seed biomass was overestimated in some cases. However, our model quantified the effects of changes in the soil nitrogen supply and biological nitrogen fixation on soybean production and will be useful for identifying and eliminating production constraints in Japanese soybeans.

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Modeling leaf area development in soybean (Glycine max L.) based on the branch growth and leaf elongation

Cited by 10 publications

References 29 publications

Plasticity on 'BRS 8381' Glycine max (L.) Merril Agronomic Attributes in Different Years of Cultivation in Roraima

Plasticity on 'BRS 8381' Glycine max (L.) Merril Agronomic Attributes in Different Years of Cultivation in Roraima

An Analysis of Soil Coring Strategies to Estimate Root Depth in Maize ( Zea mays ) and Common Bean ( Phaseolus vulgaris )

Modeling biomass and yield production based on nitrogen accumulation in soybean grown in upland fields converted from paddy fields in Japan

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