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6 results for “crop physiology”
Figure 1 in Crop physiological considerations for combining variable-density planting to optimize seed costs and weed suppression
Figure 1. Schematic representation of (A) an aerial image using an unmanned aerial vehicle (UAV) to scout fields in year 1, (B) detection of areas of high (orange) and low (yellow) weed density in year 1, and (C) implementation of year 1 weed maps to calibrate precision planter to plant in high (red) and low (green) crop densities in year 2.
Figure 2 in Crop physiological considerations for combining variable-density planting to optimize seed costs and weed suppression
Figure 2. Schematic diagram representing the workflow process of the area planting optimization model. The graph on the bottom left corresponds to low-density planting yields of maize (red circles, solid line, y = 288.5 − 2.07x), cotton (gray triangles, dashed line, y = 176 − 1.58x), and soybean (blue squares, dotted line, y = 86.5 − 0.70x) in g seed−1.
Data: More than 1000 genotypes are required to derive robust relationships between yield, yield stability and physiological parameters: a computational study on wheat crop
<p>APSIM-Wheat <strong>(</strong><a href="">www.apsim.info</a><strong>)</strong> was used to simulate a data set (for details, see Casadebaig<em> et al.</em>, 2016) with 9100 virtual genotypes (<em>N</em><sub>gen</sub>= 9100) grown under 9000 environments (<em>N</em><sub>env</sub>=9000). In short, virtual genotypes were created by varying the value of 90 independent physiological parameters in a range of ±20% from the reference cultivar <em>Hartog</em>. Environments in the dataset contain historical climate data of 125 years (1889-2013) in four locations (Emerald, Narrabri, Yanco and Merredin) in Australia, in combination with two CO<sub><sup>2</sup></sub> levels (380 and 555 ppm), three nitrogen levels (low: 50%, control: 100% and high fertilization: 100% plus 50 kg‧ha<sup>-1</sup>) and three sowing dates (early, control and late).</p>
Dataset for "Cover crop inclusion and residue retention improves soybean production and physiology in drought conditions"
<p>Data and code for "Cover crop inclusion and residue retention improves soybean production and physiology in drought conditions"</p> <p><strong>CONTEXT: </strong>Soybean (<em>Glycine max</em> (L.) Merr.) planting has increased in central and western North Dakota despite frequent drought occurrences that limit productivity. Soybean plants need high photosynthetic and transpiration rates to be productive, but they also need high water use efficiency when water is limited. Retaining crop residues and including cover crops in crop rotations are management strategies that could improve soybean drought resilience in the northern Great Plains. </p> <p><strong>OBJECTIVE</strong>: We aimed to examine how a management practice that included cover crops and residue retention impacts agronomic, ecosystem water and carbon dioxide flux, and canopy-scale physiological attributes of soybeans in the northern Great Plains under drought conditions. </p> <p><strong>METHODS</strong>: We compared two soybean fields over two years with business-as-usual and aspirational management that included residue retention and cover crops during a drought year. This comparison was based on yield, aboveground biomass, Phenocam images, and fluxes from eddy covariance and ancillary measurements. These measurements were used to derive meteorological, physical, and physiological attributes with the ‘big leaf’ framework. </p> <p><strong>RESULTS: </strong>Soybean yields were 29% higher under drought conditions in the field managed in a system that included cover crops and residue retention. This yield increase was caused by extending the maturity phenophase by 5 days, increasing agronomic and intrinsic water use efficiency by 27% and 33%, respectively, increasing water uptake, and increasing the rubisco-limited photosynthetic capacity (V<sub>cmax25</sub>) by 42%.</p> <p><strong>CONCLUSIONS:</strong> The inclusion of cover crops and residue retention into a cropping system improved soybean productivity because of differences in water use, phenology timing, and photosynthetic capacity.</p> <p><strong>IMPLICATIONS:</strong> These results suggest that farmers can improve soybean productivity and yield stability by incorporating cover crops and residue retention into their management practices because these practices allow soybean plants to shift to a more aggressive water uptake strategy.</p> <p><strong>Code:</strong></p> <p><strong>1_phenocam.rmd: </strong>Code to download Phenocam data and identify phenophase transition dates.</p> <p><strong>2_Daily_CO2_Water_Fluxes.Rmd: </strong>Code to analyze daily carbon and water fluxes (Figure 1, 2 3 and Table 2).</p> <p><strong>3_Inferring_LAI_and_Height.Rmd: </strong>Code to calculate the predicted LAI and height for each day. The output is used in the big-leaf framework.</p> <p><strong>4_Big_Leaf.Rmd: </strong>Code for the big-leaf ecophysiology estimates (Figure 4, 5 and 6; Table 3 and 4).</p> <p><strong>4_Data_Dictionary_Vairables:</strong> Code to identify the data dictionary variables. </p> <p> </p>
Distillation of crop models to learn plant physiology theories using machine learning
<p>Full Paper available for download at LINK</p>
Gene expression analysis provides insight into the physiology of the important staple food crop cassava
GEO Series GSE82279. Manihot esculenta. 32 samples. Type: Expression profiling by high throughput sequencing.
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