See more from this Division: S04 Soil Fertility & Plant Nutrition

See more from this Session: Tools for Improving Nitrogen Management

##### Abstract:

Binary logistic models were
developed to spatially predict the probability of NK fertilizer application
classes in a date palm-arid region (20 000 ha) using soil salinity (EC_{e},
dS/m), profile residual nitrate (NO_{3}-N_{p}, mg/kg), the
soil-test values for soil surface Fe_{s} (mg/kg) and Mn_{s}
(mg/kg), and the total applied quantity of irrigation water (Q_{iw,} m^{3}/tree).
The NK fertilizer application classes were assigned based on a total of 67
field trials implemented at sites of wide range of soil fertility during two growing
seasons. The experiments had the same design with 16 factorial combinations of
N and K while P was kept constant with a total number of 200 date palms per
field trial. The combination of n independent site-variables taken r at a time method
was developed to estimate the target number of total regression degrees of
freedom. Only six of the 24 site-variables (X) were found to be statistically
significant in influencing the probability of NK responses. The probability of response
(Y=1 means response and Y=0 no response) to the levels of N-application and
major-N-application were expressed by the logistic models: Logit (Y=1|N= 0.5, 1,
2, or 4 |X) = 2.950 – 0.017 Q_{iw} + 1.066 EC_{es}
+ 0.5 Fe_{s} and logit (Y=1| N = 4 |X)
= -1.583 –
0.132 NO_{3}-N_{p} + 0.785 EC_{ep}, respectively. The models for K-application and major-K-application
were: logit (Y=1|K= 1, 2, or 4 |X) = 2.581 + 0.018 Q_{iw}
- 0.090 NO_{3}-N_{p} and logit (Y=1|K
= 4|X) = 0.453 + 0.568 EC_{es} – 0.456 Mn_{s}, respectively. These
logistic models were combined in a geographic information system (GIS) to
derive soil NK fertilizer application class map using the data sets of the
significant site-variables.

See more from this Division: S04 Soil Fertility & Plant Nutrition

See more from this Session: Tools for Improving Nitrogen Management