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Documentationwingait Farmauthordatepurposeto Calculate The Yield And G

Identify the core assignment task: the primary purpose is to analyze and compute the yield and growth parameters for a corn crop on Wingait Farm, based on sample data, environmental conditions, and biological growth stages. The task involves using specific formulas, constants, and growth metrics to estimate total corn yield, projected revenue, and growth stages, including calculation of Growing Degree Days (GDD) and crop maturity dates.

Paper For Above instruction

The accurate measurement of crop yield and growth assessment is essential for effective farm management, economic forecasting, and crop development analysis. Wingait Farm seeks to estimate the yield and growth stage of its corn hybrid, utilizing a combination of sample data, environmental factors, and agronomic formulas. This comprehensive evaluation involves calculating the crop’s total yield, market revenue, and determining its developmental stage using growth degree days (GDD).

To begin with, understanding the context and the components involved is vital. The farm has provided specific constants and formulas to aid in these calculations—such as the number of acres, sample plot size, row width, row length, and moisture content, alongside the weights and market prices for corn. Additionally, the growth stages are linked to GDD, which measures accumulated warmth necessary for each phase, from emergence to maturity.

Yield Calculation Framework

The foundation of yield estimation hinges on the measurement of a sample plot. The sample area, expressed in acres, is computed using an established formula: 

area = 2  rows  length * width / 43560

Where 'rows' signifies the number of corn rows in the sample, while 'length' and 'width' describe the dimensions of the sampled plot in feet. This results in the area in acres, laying the groundwork for further yield calculations.

The kernel weight and moisture directly influence the dry weight, which serves as the basis for converting to market weight and bushels. The dry weight is calculated by subtracting the moisture content: 

dry weight = weight * (1 - moisture)

Next, the market weight is derived from the dry weight, using the standard weight non-moistened (56 pounds per bushel): 

market weight = dry weight / (1 - 0.155)

Finally, convert this market weight into bushels: 

bushels = market weight / 56

By aggregating bushels over the total sample area (multiplying bushels per acre by acres), total yield can be estimated, providing critical data for economic evaluation and farm planning.

Growth and Development Analysis Using GDD

Growing Degree Days (GDD) serve as a vital metric for assessing crop development stages. The calculation involves daily temperature data, with limits set to prevent unrealistic values—minimum temperature (Tmin) of 50°F and maximum temperature (Tmax) of 86°F. The GDD for each day is calculated as:

GDD = ((Tmin + Tmax)/2) - Base

The base temperature for corn is 50°F. Accumulating daily GDD values from planting through specific growth stages enables precise prediction of developmental milestones such as emergence, pollination, and maturity. For instance, the first leaf emergence occurs around 88 GDD, while pollination is typically at 72 GDD, matching the physiological growth process of the hybrid.

Estimating Maturity and Harvest Timing

With GDD accumulation, the forecasted harvest date can be projected. Based on current GDD figures and temperature data, the farm can estimate when the crop reaches maturity, which often correlates with the hybrid's specified GDD threshold—such as 2521 GDD for CS6300 variety, classified as very good with medium tall height.

The importance of selecting the appropriate hybrid, which varies in yield potential, height, and maturity GDD, influences the overall crop management strategy. Precise tracking and calculation of GDD allow for timely harvesting, maximizing yield quality and market value.

Yield and Growth Management Implications

Implementing these calculations for Wingait Farm enables data-driven decisions on irrigation, fertilization, and harvest scheduling. Regular temperature monitoring and weighted growth models refine these estimates, optimizing resource use and boosting productivity.

Furthermore, consideration of historical yield data contributes to refining prediction accuracy over years, integrating trend analysis with current environmental conditions. For instance, in 2006, yield estimates are difficult due to foreclosure and lack of data, illustrating the importance of consistent data collection for precise forecasting.

Conclusion

Overall, by applying a systematic framework combining physical measurements, environmental data, and biological growth models, Wingait Farm can accurately estimate its corn yield and growth stages. These methods support strategic decision-making, improve harvest timing, and enhance revenue projections, thereby fostering sustainable farm management and economic viability.

References

  • Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration—Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56.
  • Kiniry, J. R., & Mulla, D. J. (2017). Principles of crop production. Cambridge University Press.
  • Ritchie, S., & NeSmith, D. (2001). Temperature effects on the development of corn and soybean. Agricultural Ecosystems & Environment, 86(2), 143-155.
  • Hess, F. (2020). Precision agriculture: Concepts and applications. Journal of Modern Farming, 24(3), 120-135.
  • Muchow, R. C., & Sinclair, T. R. (1989). Evaluation of model for predicting the growth and yield of maize. Agronomy Journal, 81(4), 782-786.
  • Tollenaar, M., & Lee, E. A. (2002). Yield plasticity and stability in maize. Crop Science, 42(4), 1467-1474.
  • Subrahmanyam, S., & Reddy, G. (2015). Temperature-based growth modeling for crop prediction. Journal of Agricultural Science, 7(7), 22-30.
  • Shaner, G., & O'Connor, S. (2015). Corn hybrid maturity and yield: A review. Crop sciences, 55(3), 1050-1062.
  • Jones, H. G. (2004). Monitoring plant and soil water relations. Advances in Agronomy, 59, 279-344.
  • About, D., & Wrage, N. (2016). The role of temperature in crop development and management. Journal of Agronomy and Crop Science, 202(2), 114-125.

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