School of Agriculture, Food and Ecosystem Sciences - Theses

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Start date: 1956 × Type: Masters Research thesis × Subject: crops and nitrogen × Subject: soils ×

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    Hand-held decision support tool for estimating nitrogen requirements for food production and environment protection
    Qamese, Semi ( 2008)
    Concentration of nitrogen (N) in plants reflects the supply of N in the root medium, and crop biophysical variables increase as internal concentration of N in plants increases. This is useful in assessing how crop biophysical variables can be improved through proper fertiliser application rate. The ability to successfully determine N deficiencies in crops and soil is highly dependent on visual symptom examinations, plant analysis, and soil analysis. Using these methods, information on N deficiencies or excess in the soil will be made available to farmers by the end of the cropping season. Therefore correcting N deficiency or efficiency at this stage is a waste as crops are harvested or are in a condition where interference is of no value. Accordingly, there is a need for improved methods, and available technology could be applied. Chinese cabbages responded significantly (p<0.05) at urea rate of 100 kg ha-1 compared to rates lower or higher than 100 kg ha-1. Relatively, water stress are insignificant (p>0.05) in its response to chinese cabbage growth, however N and water stress together have a significant impact on growth. In addition, crops irrigated every 2 days have higher variables mean than crops irrigated every 5 days. This study found that N rate of 100 kg ha-1 with irrigation every 2 days performed better than other treatments measured. The N uptake pattern was very rapid between 200 and 400 degree days after transplanting and fertiliser application is recommended at this stage. Fertiliser rate should be minimised before and after this phase as crops N uptake is reduced. SAVIgreen VF had a positively good correlations with Leaf Area index (LAI) (r2 = 0.91, p<0.001), above ground biomass (r2 = 0.86, p<0.001) and N uptake (r2 = 0.75, p<0.001) in the first glasshouse experiment. Similarly, the second glasshouse experiment supported these findings where VF correlated positively to LAI (r2 = 0.84, p<0.001), above ground biomass ((r2 = 0.73, p<0.001) and N uptake (r2 = 0.69, p<0.001). VF correlated negatively with soil total mineral N in the first and second glasshouse experiment, r2 = 0.31, p<0.001 and r2 = 0.61, p<0.001 respectively. In addition above ground biomass (r2 = 0.62, p<0.001) and N uptake (r2 = 0.60, p<0.001) in the field also indicated better correlation with VF, however plant N was poorly correlated (r2 = 0.0, p>0.05). These correlations were greatly reduced when crops were exposed to N and water stress. VF was reduced at lower N rates (0 and 50 kg ha-1) and at higher N rates (200 and 400kg ha-1) with irrigation every 5 days. Optimum VF (0.2 – 0.4) is obtained at 100 kg ha-1. Consequently VF is a good predictor for LAI, above ground biomass and N uptake probably because canopy photosynthetic capacity increases with increasing N concentration only to an optimum level. As determined from this research, higher concentration of N in leaves was found when N rate of 400 kg ha-1was applied. The crops become N saturated and VF was greatly reduced, hence crops may have not optimally photosynthesised. Knowledge of N accumulation in the soil, leaching can be determined from levels of N accumulated in plant tissues. Therefore digital camera an invaluable remote sensing tool is cheaper and appropriate for estimating responses of VF to crops biophysical variables to determine N requirements and, hence, it may aid in predicting N losses to the environment. Modeling using Denitrification Decomposition (DNDC) model indicated that crop N uptake (90 kg ha-1) did not meet crop N demand (120 kg ha-1), even though soil N mineralisation from organic and inorganic pool of N (900 kg N ha-1 yr-1) and anthropogenic activities were very efficient. Much of N was lost through leaching (approximately 100 kg ha-1 yr-1), gas emissions (close to 400 kg ha-1 yr-1) and weeds (approximately 300 kg ha-1 yr-1). These N loses may contribute to environmental pollution. Therefore predicting crops biophysical and chemical variables using VF coupled with modeling improves knowledge on N dynamics in soil-plant-environment systems.