Quality and Crop Yield Potential of Moderately Degraded Alfisols Under Different Nutrient Inputs and Cropping Patterns

AbstractOn the face of the rapid advances in genome editing technology and greatly expanded knowledge on plant genome and genes, there is a strong demand to develop an effective tool to guide designing crops for higher yields. Here we developed a highly mechanistic model of Whole plAnt Carbon Nitrogen Interaction (WACNI), which predicts crop yield based on major metabolic and biophysical processes in source, sink and transport tissues. WACNI accurately predicted the yield responses of so far reported source, sink and transport related genetic manipulations on rice grain yields. Systematic sensitivity analysis with WACNI was used to classify the source, sink and transport related molecular processes into four categories, i.e. universal yield enhancers, universal yield inhibitors, conditional yield enhancers and weak yield regulators. Simulations using WACNI further show that even without a major change in leaf photosynthetic properties, 54.6% to 73% grain yield increase can be potentially achieved by optimizing these molecular processes during the rice grain filling period while simply combining all the ‘superior’ molecular modules together cannot achieve the optimal yield level. A common macroscopic feature in all these designed high-yield lines is that they all show ‘a sustained and steady growth of grain sink’, which might be used as a generic selection criteria in high-yield rice breeding. Overall, WACNI can serve as a tool to facilitate plant source sink interaction research and guide future crops breeding by design.One sentence summaryA mechanistic model of source, sink flow model is developed and used to demonstrate that optimization of the whole plant carbon nitrogen metabolism can dramatically increase crop yield potential.

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The impacts of crop yield and price volatility on producers’ cropping patterns: A dynamic optimal crop rotation model

Agricultural Systems ◽

10.1016/j.agsy.2012.11.001 ◽

2013 ◽

Vol 116 ◽

pp. 52-59 ◽

Cited By ~ 10

Author(s):

Ruohong Cai ◽

Jeffrey D. Mullen ◽

Michael E. Wetzstein ◽

John C. Bergstrom

Keyword(s):

Crop Rotation ◽

Crop Yield ◽

Price Volatility ◽

Cropping Patterns ◽

Rotation Model

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Crop yield components – photoassimilate supply- or utilisation limited-organ development?

Functional Plant Biology ◽

10.1071/fp14048 ◽

2014 ◽

Vol 41 (9) ◽

pp. 893 ◽

Cited By ~ 18

Author(s):

John W. Patrick ◽

Kim Colyvas

Keyword(s):

Carbon Dioxide ◽

Crop Yield ◽

Yield Components ◽

Plant Biomass ◽

Soluble Sugars ◽

Optimal Growth ◽

Yield Potential ◽

Fleshy Fruit ◽

Crop Species ◽

Growth Environment

Yield potential is the genome-encoded capacity of a crop species to generate yield in an optimal growth environment. Ninety per cent of plant biomass is derived from the photosynthetic reduction of carbon dioxide to organic carbon (photoassimilates – primarily sucrose). Thus, development of yield components (organ numbers and individual organ masses) can be limited by photoassimilate supply (photosynthesis arranged in series with phloem transport) or by their inherent capacity to utilise imported photoassimilates for growth or storage. To this end, photoassimilate supply/utilisation of crop yield has been quantitatively re-evaluated using published responses of yield components to elevated carbon dioxide concentrations across a selection of key crop species including cereal and pulse grains, fleshy fruits, tubers and sugar storing stems and tap roots. The analysis demonstrates that development of harvested organ numbers is strongly limited by photoassimilate supply. Vegetative branching and, to a lesser extent, flower/pod/fleshy fruit abortion, are the major yield components contributing to sensitivity of organ numbers to photoassimilate supply. In contrast, harvested organ size is partially dependent (eudicots), or completely independent (cereals), of photoassimilate supply. Processes limiting photoassimilate utilisation by harvested organs include membrane transport of soluble sugars and their allocation into polymeric storage products.

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Application of prognostic breeding in maize

Crop and Pasture Science ◽

10.1071/cp15206 ◽

2016 ◽

Vol 67 (6) ◽

pp. 605 ◽

Cited By ~ 4

Author(s):

Vasileios Greveniotis ◽

Vasilia A. Fasoula

Keyword(s):

Crop Yield ◽

Genetic Gain ◽

Crop Improvement ◽

Genetic Material ◽

Breeding Strategy ◽

Yield Potential ◽

Selection Strategy ◽

Plant Yield ◽

High Selection ◽

Selection Of

Innovative approaches and new efficiencies in plant breeding are required to accelerate the progress of genetic improvement through selection. One such approach is the application of prognostic breeding, which is an integrated crop-improvement methodology that enables selection of plants for high crop yield potential by evaluating its two components: plant yield potential and stability of performance. Plant yield and stability are assessed concurrently in each generation by utilising the plant prognostic equation. The genetic material used for this study was 2350 F2 plants (C0) of the commercial maize hybrid Costanza. The study presents the results of the application of prognostic breeding for 6 years in two contrasting environments (A and B), starting from C0 and ending in C5. It utilises ultra-high selection pressures (1.5% to 0.5%) to isolate superior lines with crop yield comparable to Costanza, and estimates the annual genetic gain accomplished through application of this selection strategy. Application of prognostic breeding led to the isolation of superior lines whose productivity was comparable to Costanza. The productivity gap between Costanza and the best selection was reduced from 87% (C0) to 0.5% (C5) in trial 1 (environment A), from 87% (C0) to 2% (C5) in trial 2 (environment B) and from 70% (C0) to 1% (C3) in trial 3 (environment B). Genetic gain was much higher (up to 50%) in the early cycles C0–C2 of prognostic breeding and smaller in cycles C3–C5. The best lines selected were evaluated in randomised complete block trials across both environments and 2 years. Across years, the top two lines in environments A and B averaged 87% and 91% of the Costanza yield, respectively, and they had higher prolificacy (greater number of ears per plant) than Costanza. Across all cycles, the average annual genetic gain ranged from 23% to 36% in the different trials, providing evidence that selection efficiency can be significantly maximised by using this breeding strategy.

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Prediction of Sugarcane Yield Based on NDVI and Concentration of Leaf-Tissue Nutrients in Fields Managed with Straw Removal

Agronomy ◽

10.3390/agronomy8090196 ◽

2018 ◽

Vol 8 (9) ◽

pp. 196 ◽

Cited By ~ 7

Author(s):

Izaias Pinheiro Lisboa ◽

Júnior Melo Damian ◽

Maurício Roberto Cherubin ◽

Pedro Silva Barros ◽

Peterson Ricardo Fiorio ◽

...

Keyword(s):

Crop Yield ◽

Satellite Images ◽

Vegetation Index ◽

Prediction Models ◽

Leaf Tissue ◽

Yield Potential ◽

Nutrient Concentrations ◽

Bioenergy Production ◽

Sugarcane Yield ◽

Hyperspectral Sensor

The total or partial removal of sugarcane (Saccharum spp. L.) straw for bioenergy production may deplete soil quality and consequently affect negatively crop yield. Plants with lower yield potential may present lower concentration of leaf-tissue nutrients, which in turn changes light reflectance of canopy in different wavelengths. Therefore, vegetation indexes, such as the normalized difference vegetation index (NDVI) associated with concentration of leaf-tissue nutrients could be a useful tool for monitoring potential sugarcane yield changes under straw management. Two sites in São Paulo state, Brazil were utilized to evaluate the potential of NDVI for monitoring sugarcane yield changes imposed by different straw removal rates. The treatments were established with 0%, 25%, 50%, and 100% straw removal. The data used for the NDVI calculation was obtained using satellite images (CBERS-4) and hyperspectral sensor (FieldSpec Spectroradiometer, Malvern Panalytical, Almelo, Netherlands). Besides sugarcane yield, the concentration of the leaf-tissue nutrients (N, P, K, Ca, and S) were also determined. The NDVI efficiently predicted sugarcane yield under different rates of straw removal, with the highest performance achieved with NDVI derived from satellite images than hyperspectral sensor. In addition, leaf-tissue N and P concentrations were also important parameters to compose the prediction models of sugarcane yield. A prediction model approach based on data of NDVI and leaf-tissue nutrient concentrations may help the Brazilian sugarcane sector to monitor crop yield changes in areas intensively managed for bioenergy production.

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