Effects of Temperature, Soil Water Status and Depth of Planting on Germination and Emergence of Maize (<i>Zea Mays</i>) Adapted to Semi-Arid Eastern Kenya

Itabari, J. K.; Gregory, P. J.; Jones, R. K.

doi:10.1017/s0014479700020913

Cited by 23 publications

(17 citation statements)

References 16 publications

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Section: Discussionmentioning

confidence: 99%

“…Emergence dates are related to planting dates, and the planting date is influenced by the soil temperature and soil moisture and varies widely by district and season (see Appendix A). For example, 33.6 • C is the optimal soil temperature for corn germination [54]. Thus, the planting date is delayed until the temperature condition is met in a specific field [55,56], which results in large variability in the length of the effective growing season in a particular region.…”

Section: Discussionmentioning

confidence: 99%

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Using MODIS Data to Predict Regional Corn Yields

et al. 2016

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A simple approach was developed to predict corn yields using the MoDerate Resolution Imaging Spectroradiometer (MODIS) data product from two geographically separate major corn crop production regions: Illinois, USA and Heilongjiang, China. The MOD09A1 data, which are eight-day interval surface reflectance data, were obtained from day of the year (DOY) 89 to 337 to calculate the leaf area index (LAI). The sum of the LAI from early in the season to a given date in the season (end of DOY (EOD)) was well fitted to a logistic function and represented seasonal changes in leaf area duration (LAD). A simple phenology model was derived to estimate the dates of emergence and maturity using the LAD-logistic function parameters b 1 and b 2 , which represented the rate of increase in LAI and the date of maximum LAI, respectively. The phenology model predicted emergence and maturity dates fairly well, with root mean square error (RMSE) values of 6.3 and 4.9 days for the validation dataset, respectively. Two simple linear regression models (Y P and Y F ) were established using LAD as the variable to predict corn yield. The yield model Y P used LAD from predicted emergence to maturity, and the yield model Y F used LAD for a predetermined period from DOY 89 to a particular EOD. When state/province corn yields for the validation dataset were predicted at DOY 321, near completion of the corn harvest, the Y P model, including the predicted phenology, performed much better than the Y F model, with RMSE values of 0.68 t/ha and 0.66 t/ha for Illinois and Heilongjiang, respectively. The Y P model showed similar or better performance, even for the much earlier pre-harvest yield prediction at DOY 257. In addition, the model performance showed no difference between the two study regions with very different climates and cultivation methods, including cultivar and irrigation management. These results suggested that the approach described in this paper has potential for application to relatively wide agroclimatic regions with different cultivation methods and for extension to the other crops. However, it needs to be examined further in tropical and sub-tropical regions, which are very different from the two study regions with respect to agroclimatic constraints and agrotechnologies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Using MODIS Data to Predict Regional Corn Yields

et al. 2016

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“…Planting date is influenced by soil temperature and soil moisture and varies widely by district and season (see Appendix A). For example, 33.6 °C is the optimal soil temperature for corn germination [45]. Thus, the planting date would be delayed until that condition is met in a specific field [46,47], which would result in a large variability in the length of the effective growing season in a particular region.…”

Section: Discussionmentioning

confidence: 99%

Using MODIS Data to Predict Regional Corn Yields

Ban

Kim

Park

et al. 2016

Preprint

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A simple approach was developed to predict corn yields using the MoDerate Resolution Imaging Spectroradiometer (MODIS) data product from two geographically separate major corn crop production regions: Illinois, USA and Heilongjiang, China. The MOD09A1 data product, which are 8-day interval surface reflectance data, were obtained from day of the year (DOY) 89 to 337 to calculate the leaf area index (LAI). The sum of the LAI from early in the season to a given date in the season [end of DOY (EOD)] was well fitted to a logistic function and represented seasonal changes in leaf area duration (LAD). A simple phenology model was derived to estimate the dates of emergence and maturity using the logistic function parameters b1 and b2, which represented the rate of increase in LAI and the date of maximum LAI, respectively. The phenology model predicted emergence and maturity dates fairly well, with root mean square error (RMSE) values of 6.3 and 4.9 days for the validation dataset, respectively. Two simple linear regression models (YP and YF) were established using LAD as the variable to predict corn yield; the yield model (YP) used LAD from predicted emergence to maturity, and the yield model (YF) used LAD for a predetermined period from DOY 89 to a particular EOD. When state/province corn yields for the validation dataset were predicted at DOY 321, near completion of the corn harvest, the YP model performed much better than the YF model, with RMSE values of 0.68 t/ha and 0.66 t/ha for Illinois and Heilongjiang, respectively. The YP model showed a similar or better performance, even for the much earlier yield prediction at DOY 257, compared to that of the YF model. In conclusion, the phenology and yield models were developed based only on logistic changes in remote sensing-derived LAD, and predicted phenological dates and corn yields with considerable accuracy and precision for the two regions selected for this study. However, these models must be examined for spatial portability in more diverse agro-climatic regions.

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“…The seed reserves may have been used faster because of higher water availability in the deep placement. Itabari et al (1993) planted corn in eastern Kenya at three depths (25, 75 and 125 mm) and at three planting dates. They observed the best performance in germination and emergence at a depth of 75 mm at the first and second planting date, and at 125 mm at the third planting date.…”

Section: Emergencementioning

confidence: 99%

Spatial assessment of the correlation of seeding depth with emergence and yield of corn

Knappenberger

Köller

2011

Precision Agric

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Germination conditions are determined by hydraulic, thermal and mechanical properties of the soils. In heterogeneous fields, the most favourable seeding depth varies spatially. To investigate the influence of seeding depth on emergence and grain yield of corn, corn was planted in depths of 40, 50, 60, 70, 80 and 90 mm in three experimental years (2006)(2007)(2008). The apparent soil electrical conductivity was measured with an EM38. The apparent electrical conductivity was used as a proxy for soil texture, top-soil thickness, effective root zone thickness, soil water content and soil structure. The spatial dependencies among emergence, yield and apparent electrical conductivity were considered by including spatial models into the statistical analysis. The results showed significant correlations of the apparent soil electrical conductivity, of the experimental year, and of the seeding depth with the emergence of corn. Deeper planted corn (80 or 90 mm) resulted in more emergence than shallow planted corn (?4.4% in 2006, ?1.2% in 2007 and ?1.5% in 2008). The emergence decreased with increasing apparent soil electrical conductivity values. The corn grain yield was significantly affected by the soil electrical conductivity, by emergence and by the experimental year. Increasing apparent soil electrical conductivity values were correlated with decreasing yield (from 7.5 to 3.4 Mg ha -1 in 2006, from 10.8 to 5.3 Mg ha -1 in 2007 and from 8.4 to 2.9 Mg ha -1 in 2008). Increasing emergence resulted in increasing yield.

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Effects of Temperature, Soil Water Status and Depth of Planting on Germination and Emergence of Maize (Zea Mays) Adapted to Semi-Arid Eastern Kenya

Cited by 23 publications

References 16 publications

Using MODIS Data to Predict Regional Corn Yields

Using MODIS Data to Predict Regional Corn Yields

Using MODIS Data to Predict Regional Corn Yields

Spatial assessment of the correlation of seeding depth with emergence and yield of corn

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