Use of Palm Oil Mill Effluent (Pome) and Peat to Reduce Ammonia Volatilisation from Fertiliser Urea

Ammonia (NH3) volatilization is a major pathway of nitrogen loss which limits the efficiency of urea as a fertilizer when surface-applied to soils. High pH and low cation exchange capacity in soils have been identified as the principal causes ofNH3 volatilization from urea. The several approaches proposed to correct such inefficiency in urea, thus far, were fundamentally based upon delay of urea dissolution and impedance of urea hydrolysis. An attempt was made to establish a preferred environment within the urea-soil reaction zone (microsite) using palm oil mill efiluent (POME) and peat. Both POME and peat are organic matter-rich, and contain humic substances across their respective organic matrix. Humic substances have been shown to interact with ammoniacal compounds and urea. As such, a study was engaged to explore the effects of POME and peat, and their respective humic derivatives on NH3 volatilization from urea surface-applied to two Malaysian soils of contrasting pH values. The organic materials and their humic derivatives were separately matrixed with urea into pelletised form and evaluated under laboratory regimes for % NH3 volatilization, pH change and NHt + -N recovery. Estimation ofNH3 volatilization was carried out using a closed-dynamic air-flow system. Detennination of the chemical and physical attributes of soils and materials, and measurement of the parameters studied were done using standard procedures. Characterisation of the POME- and peat-derived humic substances was performed using chemical and spectral methods. Results showed that reduction in NH3 volatilisation by peat-treated urea was more pronounced than that of POME in both soils. Such reduction was accompanied by a corresponding increase in NH4+ recovery and decrease in pH particularly at the microsite. The use of differing matrixing ratios did not yield significant variation in the performance of matrixing agents. Acidification of POME and peat resulted in impedance of urea movement from micro site to outersite

Selected Precision Agriculture studies in oil palm: a 10-year summary

Precision Agriculture (PA) embodies a holistic field management strategy that allows adjustment of crop input use and cultivation methods, including seed, fertilizer, pesticide and water application, variety selection, planting, tillage and harvesting, to match varying soil, crop and other field attributes. PA involves mapping and analyzing field variability, and linking such variability to management actions. This contrasts with conventional agriculture that is based on uniform treatment(s) across a field. Oil palm is an excellent candidate for PA implementation simply because it consumes a large amount of chemical and physical inputs. However, a major constraint in implementing PA strategies on a detailed scale for oil palm is the typically large plantation size. The agronomic challenge of increasing oil palm yield productivity hinges on three primary issues: 1) fertilization, 2) cropping practices such as planting density, ground cover, pruning and drainage, and 3) pest and disease management. This paper presents a 10-year Abstract of selected PA studies carried out in Malaysian and Indonesian oil palm plantations. These studies are: spatial variability of soil fertility across topography, removal of spatial effects to improve interpretation of data from fertilizer trials, development of a non-destructive oil yield and oil quality estimation protocol, stand density assessment using remote sensing, spatial variability of soil organic carbon across different crop ages, spatial variability of orange spotting disease, discriminating between potassium deficiency and orange spotting disease symptoms using remote sensing, estimating fresh fruit bunch yields using remote sensing, and estimating palm oil quality and yield using proximal sensing

An expert integrative approach for sediment load simulation in a tropical watershed.

Prediction of highly non-linear behaviour of suspended sediment flow in rivers is of prime importance in environmental studies and watershed management. In this study, the predictive performance of artificial neural network (ANN) integrated with genetic algorithm (GA) was assessed. GA was used to optimize the parameters and architecture of the ANN. Five simulation scenarios (S1–S5) were developed using daily time series of suspended sediment discharge, water discharge, precipitation and reservoir level. The scenario S1 was composed of only water discharge input. The scenarios S2–S4 were composed of water discharge input and precipitation records at different stations. The inputs water discharge, precipitation and reservoir level formed the last scenario S5. Assessment metrics such as normalized mean square error, correlation coefficient, Nash–Sutcliffe efficiency and trend accuracy were used to evaluate the performance of ANN–GA on the simulation scenarios. Based on error analysis, differences between various scenarios in terms of error metrics were trivial, especially during the testing process. Meanwhile, S1 and S3 showed better accuracy in predicting the trend of sediment load time series, as compared to other scenarios. Based on error and sensitivity analyses, S1 with the Nash–Sutcliff efficiency and correlation coefficient of 0.56 and 0.81, respectively, was chosen as the most appropriate scenario. All networks showed a weak robustness in estimating large magnitudes of sediment load, mostly attributable to scarcity of large observed values in the training data-set. This weakness was also originated from different non-linear relationships governing the process of sediment detachment and final sediment load by a high storm event, as compared to those by low or medium storm events. Furthermore, there was an obvious sediment load overestimation in the 2008 exemplars due to a high level of daily water discharge and the outlined generalization rules. Nevertheless, ANN–GA showed reliable performance for sediment load simulation in the studied watershed

Spatial variability of pineapple yields on tropical peat.

The spatial variability of pineapple yields from a one-hectare field located on a tropical peat was quantified. In situ yield measurements were recorded based on 0.6 × 25-m rectangular grids. A total of 60 geo-referenced yield records were obtained. Recording points were spaced 8 × 18 m. Yield data were subjected to semivariogram and kriging analyses. The average pineapple yield was 93.3 kg per grid with a CV of 13.7%. The spatial structure of pineapple yields was fitted using an exponential function with a total variation (sill) of 137.6, a random variation (nugget) of 49.3, and an effective range of 38.1 m. Based on the nugget to sill ratio, pineapple yields showed a moderate spatial dependence. A map comprising measured and interpolated yield values showed that 31% of the field had yields close to the field average, 36% had yields above the average, and 33% with yields below the average. These results suggest that site-specific management of pineapple is necessary

Estimating oil palm yields using vegetation indices derived from quickbird

A single-date archived QuickBird satellite imagery and oil palm yield data collected over a 12-year time series were used to generate empirical oil palm yield models under Malaysian conditions. Vegetation indices and yield data were subject to correlation analysis, followed by regression modelling and model validation using standard metrics. Results showed a strong positive correlation between vegetation indices and oil palm yields, across different planting periods. Among vegetation indices, RVI showed the best correlation with oil palm yield. Empirical models were found to be significant for the 1990-2002 and the 1998-1999 planting periods. Models built using RVI and MSAVI showed a strong fit between estimated yield and observed yield. In the 1998-1999 planting period, however, only RVI and GNDVI showed reliable strength in yield estimation. Overall, findings of this study suggest that selected QuickBird-derived vegetation indices can be used to estimate oil palm yields with reliable accuracy

Analytical attributes of Humic Acids derived from tropical-based resources

Humic acids (HA) are widely researched and exploited compounds due to their exceptional chemical and physical properties. However, information on tropical-based HA is still lacking. This attempted to characterize HA derived from tropical peat and POME using chemical and physical methods. Elemental analysis of the HA showed that C ranged between 48.94 and 57.87%, H between 4.90 and 8.26%, N between 1.93 and 8.05% and 0 between 30.96 and 40.85%. The functional group analysis indicated that peat-derived HA were more reactive than those derived from POME. Optical density and spectral examinations revealed that the HA had varying degrees of aromaticity. Generally, data obtained from the HAs studied (particularly from peat) agreed closely with those reported elsewhere

Estimation of red tip disease severity in pineapple using a non-contact sensor approach.

Red tip disease on pineapple (Ananas comosus) was first recognized about 20 years ago in a commercial pineapple stand located in Simpang Renggam, Johor, Peninsular Malaysia. Since its discovery, there has been no confirmation on the causal agent of red tip disease. The epidemiology of red tip disease is still not fully understood. However, based on disease symptoms and field transmission mode, red tip disease seems to be strongly associated with viral infection. The aim of this work was to assess the feasibility of using an optical sensor to estimate red tip disease severity. This work was performed in a commercial pineapple plantation located in Simpang Renggam, Johor. Four observation plots bearing pineapple variety SR36 were demarcated based on crop growth stage. Each plot comprised a total of eighty corresponding measurements of percent Disease Severity (% DS) and Normalized Difference Vegetation Index (NDVI). Our data showed a strong correlation between % DS and NDVI. The 7- and 11-month plantings registered a correlation coefficient (r) of -0.83 and -0.88, respectively. The negative correlation infers that NDVI increases when disease severity is low. This is expected since healthy leaves reflect more near-infrared light and less visible light which results in a higher NDVI. The regression of NDVI on % DS for the 7-month planting was explained by: % DS = 181.6 - 185.6*NDVI. Meanwhile, the regression of NDVI on % DS for the 11-month planting was explained by: % DS = 213.2 - 219.8*NDVI. The linear fit between measured % DS and estimated % DS from the 7-month and 11-month plantings was relatively strong. This work has demonstrated that NDVI is a reliable predictor of % DS in pineapple

Spectral reflectance and physiological studies of cocoa leaves in response to macronutrient deficiency

In cocoa (Theobroma cacao), macronutrients of nitrogen (N), phosphorus (P) and potassium (K) are the essential elements that may drastically affect growth, appearance and most importantly, yield. However, detection of macronutrients can be slow while nutrient analysis can be time consuming. Adaptation of hyperspectral analysis techniques along with physiological study for the determination of specific nutrient stress in cocoa could allow early detection and precision in fertilization. The objective of the study was to investigate the prediction possibility of N and K deficiency in cocoa seedlings using a spectroradiometer. Spectral reflectance of fully expanded cocoa leaves from 350 to 1000 nm; growth (height, girth, leaf area) and physiological studies (photosynthesis, transpiration rate, chlorophyll content) were measured at monthly intervals for 3 months after treatment. A total of 72 seedlings (3 treatments x 4 replication x 6 sampling = 72 seedlings) with treatments (T1: 15-N: 15-P: 15-K; T2: 0-N: 15-P: 15-K; T3: 15-N: 15-P: 0-K) were arranged in a factorial randomized complete block design replicated four times in a greenhouse. Multispectral reflectance showed that leaves with no N (T2) had the highest reflectance peak at about 550 nm as absorption of incident light by chlorophyll decreased. This was further supported by T2 cocoa seedlings with significantly lowest readings in growth, net photosynthesis, transpiration rate and chlorophyll content (P≤0.05). Next, cocoa seedlings with no N and K involved response to nutrient stress by showing a shift in the red edge with greater reflectance at 675-750 nm. This is because healthy cocoa seedlings with complete ratio of NPK absorbed light as energy for photosynthesis and reflect near infrared light by bouncing off from the mesophyll layer. Overall, reflectance measurements could be a powerful non-destructive technique to decide on fertilizer application and timely correction of nutrient deficiencies before irreversible stress or damage occurs

Determination and mapping of calcium and magnesium contents using geostatistical techniques in oil palm plantation related to basal stem rot disease

The basal stem rot (BSR) disease has been reported as the most destructive disease of oil palms in Southeast Asia. Adequate contents of nutrient in soil and leaf helps improve the plant health and its productivity. This study aims to determine the spatial variability of calcium (Ca) and magnesium (Mg) in soil and leaf collected in BSR infected oil palm plantation. The exchangeable calcium (Ca ex) and magnesium (Mg ex) in soil were found low in the study area ranged from 0.03-0.50% and 0.06- 0.35%, respectively. The Ca and Mg content in leaf were also low ranged from 0.09-0.60%, and 0.03-1.87%, respectively. The Ca ex in soil of both blocks showed a negative significant correlation with the disease at p<0.01. However, only Ca content in leaves of one study site (Block 2) showed a negative significant correlations with the disease (p<0.05). The generated map and significant correlations revealed that unbalanced nutrient content occurred in the study area