Jia B.,Shihezi University |
He H.,Shihezi University |
Ma F.,Shihezi University |
Ma F.,Xinjiang Shida Sender Technology Co. |
And 6 more authors.
The Scientific World Journal | Year: 2014
The main objective of this study was to develop a nondestructive method for monitoring cotton growth and N status using a digital camera. Digital images were taken of the cotton canopies between emergence and full bloom. The green and red values were extracted from the digital images and then used to calculate canopy cover. The values of canopy cover were closely correlated with the normalized difference vegetation index and the ratio vegetation index and were measured using a GreenSeeker handheld sensor. Models were calibrated to describe the relationship between canopy cover and three growth properties of the cotton crop (i.e., aboveground total N content, LAI, and aboveground biomass). There were close, exponential relationships between canopy cover and three growth properties. And the relationships for estimating cotton aboveground total N content were most precise, the coefficients of determination (R 2) value was 0.978, and the root mean square error (RMSE) value was 1.479 g m-2. Moreover, the models were validated in three fields of high-yield cotton. The result indicated that the best relationship between canopy cover and aboveground total N content had an R 2 value of 0.926 and an RMSE value of 1.631 g m -2. In conclusion, as a near-ground remote assessment tool, digital cameras have good potential for monitoring cotton growth and N status. © 2014 Biao Jia et al.
Jia B.,Shihezi University |
Jia B.,Xinjiang Shida Sender Technology Co. |
He H.B.,Shihezi University |
He H.B.,Xinjiang Shida Sender Technology Co. |
And 12 more authors.
Journal of Animal and Plant Sciences | Year: 2014
The objective of this study was to develop an improved model for describing the accumulation of aboveground cotton biomass. The model input was RTEP, which was the normalized product of thermal effectiveness and photosynthetically active radiation. The model was calibrated using data from field plots with five N rates and two cotton cultivars. Model validation was conducted using data from three independent cotton fields. Eight nonlinear functions described cotton growth well (R>0.0.894, SD<0.05). The parameters of the functions were then compared and the results indicated that the Richards function best fit the nonlinear relationships in a biologically meaningful way. The equation was as follows: relative aboveground biomass accumulation (RAGBA) = 1.024/(1+e6.646-10.115RTEP)1/1.417 (r = 0.981, s = 0.043). Validation results indicated that the root mean square error was 0.659 t hm-2, the relative error was 5.337%, the coefficient of concordance was 0.988, and the coefficient of determination was 0.961. The second derivative of the optimized model showed that in cotton, the process of aboveground biomass accumulation could be divided into three phrases using two inflection points. When the accumulation rate of the aboveground biomass of cotton was at its maximum, the relative product of thermal effectiveness and PAR was 0.622, the maximum rate of the aboveground biomass accumulation was 2.299, and the aboveground biomass accumulation was 0.549. In conclusion, our study indicates that the product of thermal effectiveness and PAR is a valuable parameter for estimating aboveground biomass accumulation in cotton.