Spatial variability of bulk soil electrical conductivity in a Malaysian paddy field: key to soil management

On-the-go EC sensor is a useful tool in mapping the apparent soil electrical conductivity (EC_a) to identify areas of contrasting soil properties. In non-saline soils, EC_a is a substitute measurement for soil texture. It is directly related to both water holding capacity and cation exchange capacity (CEC), which are key ingredients of productivity. This sensor measures the EC_a across a field quickly and gives detailed soil features (1-s interval) with few operators. Hence, a dense sampling is possible and therefore a high resolution EC_a map can be produced. This paper presents experiences in acquiring detailed EC_a information that is correlated to other soil properties for precision farming of rice. The study was conducted on a 9 ha rice plot in MARDI Seberang Prai Station, Penang. The VerisEC3100 was pulled across the field in a series of parallel transects spaced about 15 m apart. The study showed that shallow and deep EC_a had high correlation and shallow EC_a had significant correlation to P. Deep EC_a had significant correlation to P, K and yield. Regression equations showed that N and P could be estimated by shallow EC_a but, pH, K and yield were better estimated by deep EC_a. This study was able to draw some basic ideas of nutrient zone management according to precision farming technique.     

Preliminary Study of Variable Rate Application – Organic Liquid Fertilizer by Using SPAD Chlorophyll Meter on System of Rice Intensification (SRI) Cultivation

Knowing actual nutrient requirements for rice plants is crucial in supplying the correct amount of fertilizer, especially nitrogen since nitrogen is one of the most key limiting nutrients in rice cultivation. A preliminary study of variable rate application (VRA) on System of Rice Intensification (SRI) planting by using organic foliar fertilizer was carried out to determine the effectiveness of precision fertilization. Foliar fertilizer was formulated based on actual nitrogen needed by rice plants using Soil-Plant Analyses Development (SPAD) chlorophyll meter measurements. The experiment was laid out in a randomized complete block design (RCBD) with four treatments (50% fixed rate, 100% fixed rate, 150% fixed rate and VRA) and four replications for each treatment. Plant growth performances (plant heights, number of tillers, number of panicle and flower) and yield performances (grain yield, number of grain, 1000-grain weight and number of spikelets) were recorded during the study. The results showed that foliar application of VRA resulted in significantly higher yield performances; grain yield (13.65 g), number of grain (807.50), 1000-grain weight (16.79 g), and number of spikelets (7.50) compared to uniform fertilizer applications. VRA treatments had the highest SPAD readings at every planting stage during the experiment; however, a high nitrogen supply was needed during the mid-tillering stages (35 DAT) compared to other fertilizer rates. Besides, VRA application shows the most savings in term of total nitrogen supply (415 µg) compared to uniform rates application; 50% fixed rate (400 µg), 100% fixed rate (810 µg), and 150% fixed rate (1210 µg).     

Development of an early warning system for brown planthopper (BPH) (<Emphasis Type="Italic">Nilaparvata lugens</Emphasis>) in rice farming using multispectral remote sensing

The spread of rice pests such as BPH in tropical areas is one of the best-known yield lost factors. Remote sensing can support precision farming practices for determining the location of spreads and using pesticide in the right place. In a specifically conducive environment like high temperature and heavy rainfall, BPH population will increase. To address this issue, detection of sheath blight in rice farming was examined by using SPOT-5 images. Also, the extraction of weather data derived from Landsat images for comparing with the BPH infestation was undertaken. Results showed that all the indices that recognize infected plants are significant at α = 0.01. Examination of the association between the disease indices indicated that band 3 (near infrared) and band 4 (mid infrared) in SPOT-5 images have a relatively high correlation for detecting diseased part from healthy ones. The selected indices declared better association for detecting healthy plants from diseased ones. Image investigations revealed that BPH were existing at the higher limits of tolerable temperatures when in the form of nymphs. With the knowledge that the late growth stage of plants has more severe BPH attacks, the results stated that BPH outbreak is particularly obvious in the north-west corner and middle regions of the maps and it is more likely to happen in specified ranges of temperature and RH, i.e. 29 °C <T< 32 °C, and 88 % <RH< 93 %.     

Pedo-transfer function for saturated hydraulic conductivity of lowland paddy soils

In paddy field, soil saturated hydraulic conductivity (K _s) plays as an important component in the calculation of irrigation requirement of the water balance equation and also for irrigation efficiency. Several laboratory and field methods can be used to determine K _s. Laboratory and field determinations are usually time consuming, expensive and labour intensive. Pedo-transfer functions (PTF) serve to translate the basic information found in the soil survey into a form useful for broader applications through empirical regression of functional relationships, such as simulation modelling. Since PTFs have not been applied to paddy soils in the study area, a lot of field measurements will require high labour input to determine K _s hence high cost. This study attempts to seek a simplified method for determining K _s values based on common existing soil properties through PTF technique. Soil samples (n = 408 samples) were collected randomly depending on the soil series within the 2,300 ha Sawah Sempadan rice cultivation area. Both field work and laboratory work were carried out. The samples were then analysed for the following properties: dry bulk density (D _b), soil particle percentage (Sand-S, Silt-Si and Clay-C), organic matter (OM) and geometric mean diameter (GMD). The measured K _s values were obtained by using the falling head method. The parameters were then used as inputs for developing a K _s model by regression analysis using Statistical Analysis System (SAS) package. Stepwise regression analysis was applied to determine the best fit model based on R ² and significant level. The results of the study showed that there is a high spatial variability of the saturated hydraulic conductivity in the paddy area. The best regression model for estimating K _s was based on C, D _b, OM and GMD with the dependent variable (K _s) in a form of natural logarithm. The model inputs introduced by stepwise regression are commonly available therefore, this model is useful to replace the conventional method.     

Relationship between apparent electrical conductivity and soil physical properties in a Malaysian paddy field

Site-specific crop management, well-established in some developed countries, is now being considered in developing countries such as Malaysia. The apparent electrical conductivity (EC_a) of the soil can be used as an indirect indicator of a number of soil physical properties and even crop yield. Commercially available EC_a sensors can efficiently develop the spatially dense data sets desirable in describing within-field spatial soil variability for precision farming. The main purpose of this study was to generate a variability map of soil EC_a within a Malaysian paddy field using a VerisEC sensor. The EC_a values were then compared with some soil variables within classes after delineation. Measured parameters were mapped using the kriging technique and their correlation with soil EC_a was determined. The study showed that the VerisEC can determine soil spatial variability, and can acquire soil EC_a information quickly. Spatial variability of shallow and deep EC_a showed the same patterns. Estimation of soil properties based on EC_a varied from one soil parameter to another and all could be estimated better by deep EC_a. Cross-validation results showed that shallow and deep EC_a, and also bulk density, gave more accurate estimates compared with other variables.