The influence of zinc and copper fertilizer application on zinc, copper and cadmium concentration in mixed pasture : a thesis presented in partial fulfilment of the requirements for the degree of Master of Applied Science in Soil Science at Massey University

There has been considerable debate about the accumulation of cadmium (Cd) in agricultural soils and its subsequent uptake by pasture plants due to phosphate fertilizer application. Ruminants grazing pastures absorb a small fraction of this Cd, and some of this is subsequently accumulated in the liver and kidney. Although tissue accumulation of Cd in grazing livestock is generally small (< 1 mg Cd kg-1 fresh tissue), but any reduction in plant uptake is beneficial in reducing such accumulation further, especially in the kidneys. Uptake of Cd by pasture may be affected by the concentration of other nutrient cations, such as zinc (Zn) and copper (Cu). In addition, since Zn and Cu are complexed by the same metal binding protein (metallothionein) as Cd, a change in the ratio of these nutrients in pasture may also reduce Cd accumulation rates by interfering with Cd accumulation. In order to assess the effects of Zn and Cu on Cd uptake by pasture, a field experiment was conducted, using three pairs of pasture plots with low ( 0.2 mg Cd kg-1) and high (0.6 mg Cd kg-1) background Cd status. Twelve sub-plots (l.44 m2) were laid out in each plot and increasing levels of Zn (0, 5, 15 and 40 kg ha-1) and Cu (0, 2, 5 and l0 kg ha-1) were added as ZnSO4. 7H2O and CuSO4.5H2O respectively. Pasture samples were collected at regular intervals and analysed for dry matter yield, botanical composition and Zn, Cu and Cd uptake. Soil samples were extracted with 0.01M CaCl2 and 0.lM HCl solution to measure the plant available Zn, Cu and Cd. It was found that the plots with a high background Cd status in the soil resulted in a higher Cd concentration in mixed pasture (0.22 mg Cd kg-1 DM) than those with a low background Cd status (0.10 mg Cd kg-1 DM) at the first harvest (after 73 days). The Cd concentration in the mixed pasture was higher during the summer (December) period than in the early spring (September). Application of Zn fertilizer increased the Zn concentration in pasture from 37 to 150 mg kg-1 DM at the first harvest. Excessive amounts of Zn lead to a decrease in DM yield. The growth of pasture was controlled principally by the amount of plant available Zn, which depended on the amount of both added Zn and added Cu. The effect of the added Cu was to increase the toxicity of the addd Zn. Application Cu fertilizer increased the Cu levels from 9 to 16 mg kg-1 DM at the first harvest. The Cu concentration in pasture continued to decrease with time following the addition of fertilizers. The legumes are more tolerant of Cu than grass. The Cu concentration in harvest 4 (after 159 days) ranged from 6.9 to 7.0 mg kg-1 DM in grass and 8.9 to 9.9 mg kg-1 DM in legumes. The Cd concentration in the pasture decreased with increasing Zn concentration in the pasture at the first harvest. The effect of Zn on Cd uptake was more pronounced on plots with a high background Cd status in the soil. The effect of Zn on Cd concentration depends on the external Zn concentration levels. There was no consistent effect of Cu concentration on Cd concentration. The effect of the addition of Cu and Zn in fertilizer was to lower the Cd:Cu and Cd:Zn ratios in the herbage. There was a good relationship between soil available Zn as extracted by 0.1M HCl and Zn concentration in the herbage. A similar observation was obtained for Cu. But there was no consistent relationship between 0.01M CaCl2 extractable Cd and the Cd concentration in pasture. The results indicated that pasture and soil analysis for Cd and Zn may provide useful guides to situations where Cd concentrations in pasture may be decreased by Zn applications

Response of Eggplant to Integrated Approaches for Sustainable Reclamation and Improvement of a Cheringa Hot Spot of Acid Sulfate Soil

The application of basic slag (BS20 and BS30: basic slag 20 and 30 t ha-1) and aggregate size (A20 and A30: aggregate sizes of soil less than 20 and 20-30 mm) and different techniques (Tech 1: pyrite at top, jarosite at middle, and top soil at the bottom of ridge; Tech 2: top soil at top, pyrite at middle, and jarosite layer at the bottom of ridge) exerted significant (p≤0.05) positive effects on the growth and yield of eggplants cultivated under field condition and the effects varied not only with the kinds and amounts of amending materials but also with the techniques applied. The soil showed a silty clay loam texture, initial pH value of 4.1, pyrite content of 55 g kg-1, base saturation of 47%, ECe value of 3.6 dS m-1, high exchangeable Fe3+ and Al3+ contents of 1.47 and 5.29 cmolc kg-1, respectively. The pH value of the average soil data obtained from all the treatments during fruit set (95 days after transplantation) of eggplants was found to be increased in pH by 1.2 units higher compared with the control (i.e. initial pH value). The contents of P, K, Ca and Mg in the average soil data during fruit set were found to be increased (IOC = increased over control) by 41 to 127% IOC, while the contents of Al3+, Fe3+, Na+, Cl- and SO4 2- in the soil were found to be decreased by 28 to 92% IOC. The different treatments on eggplants grown under the modified-plain-ridge-ditch techniques in the Cheringa acid sulfate soil significantly (0≤0.05) increased the fresh yield of eggplants, and the increment was more pronounced with Tech 2. The maximum yield of 17.8 t ha-1 of eggplant for Tech 1 and 20.1 t ha-1 for Tech 2 were recorded by the application of BS30 in the soils of smaller aggregates (A20) at the ridges of Tech 2, followed by the A30BS30 treatments in both the techniques. The lowest quantity of 1.7 t ha-1 yield was recorded by the control treatment. The eggplants grown in the ridges of both the techniques exhibited the best responses on N, P, K, Ca and Mg contents in eggplant tissues during fruit set. As expected, the lowest contents of these nutrients in the eggplants were recorded in the control treatment. Sulfur content of the eggplants grown in the control plots was 3.6 g kg-1 and was in the range of adequate S content (4 g kg-1). However, the S contents in the eggplants grown in different treatments were significantly (p≤0.01) lower compared with the adequate level. The effectiveness of the treatments for the reclamation of the soil in relation to the growth of eggplants was: Tech 2 > Tech 1, BS30 > BS20, and A20 > A30. The results suggest that the physicochemical properties of the soil, and the growth, yield and nutrition of eggplants were strikingly improved by the application of flash leaching followed by BS30 and A20 treatments in the ridges of Tech 2, and are regarded as the best reclamation measures for this acid sulfate soil

Time-Domain Data Fusion Using Weighted Evidence and Dempster–Shafer Combination Rule: Application in Object Classification

To apply data fusion in time-domain based on Dempster–Shafer (DS) combination rule, an 8-step algorithm with novel entropy function is proposed. The 8-step algorithm is applied to time-domain to achieve the sequential combination of time-domain data. Simulation results showed that this method is successful in capturing the changes (dynamic behavior) in time-domain object classification. This method also showed better anti-disturbing ability and transition property compared to other methods available in the literature. As an example, a convolution neural network (CNN) is trained to classify three different types of weeds. Precision and recall from confusion matrix of the CNN are used to update basic probability assignment (BPA) which captures the classification uncertainty. Real data of classified weeds from a single sensor is used test time-domain data fusion. The proposed method is successful in filtering noise (reduce sudden changes—smoother curves) and fusing conflicting information from the video feed. Performance of the algorithm can be adjusted between robustness and fast-response using a tuning parameter which is number of time-steps(ts)

Paradox Elimination in Dempster–Shafer Combination Rule with Novel Entropy Function: Application in Decision-Level Multi-Sensor Fusion

Multi-sensor data fusion technology in an important tool in building decision-making applications. Modified Dempster–Shafer (DS) evidence theory can handle conflicting sensor inputs and can be applied without any prior information. As a result, DS-based information fusion is very popular in decision-making applications, but original DS theory produces counterintuitive results when combining highly conflicting evidences from multiple sensors. An effective algorithm offering fusion of highly conflicting information in spatial domain is not widely reported in the literature. In this paper, a successful fusion algorithm is proposed which addresses these limitations of the original Dempster–Shafer (DS) framework. A novel entropy function is proposed based on Shannon entropy, which is better at capturing uncertainties compared to Shannon and Deng entropy. An 8-step algorithm has been developed which can eliminate the inherent paradoxes of classical DS theory. Multiple examples are presented to show that the proposed method is effective in handling conflicting information in spatial domain. Simulation results showed that the proposed algorithm has competitive convergence rate and accuracy compared to other methods presented in the literature