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.
Reference crop evapotranspiration (ET o), used to determine actual crop evapotranspiration, is often estimated from pan evaporation (EP) data. However, uncertainties in the relationship between ET o and EP often result in unreliable estimate of crop evapotranspiration. This study investigated the relationship between measured and estimated crop evapotranspirations, ET m and ET e, respectively, at tillering (9-30 days after transplanting, DAT) and midgrowth (51-72 DAT) stages of a rice variety. ET m was measured with a Marriott Tube-type Micro-lysimeter (hereafter referred to Micro-lysimeter) in a ponded rice field and ET e was estimated from EP, which was measured by employing the US Weather Bureau Class 'A' Evaporation Pan (hereafter referred to Class A Evaporation Pan). A strong linear relation (r 2 = 0.89) at the tillering stage and a weak relation (r 2 = 0.48) at the mid-growth stage were obtained between ET m and EP. The slope of this plot provided a pan-crop factor (K p K c), which was 0.81 at the tillering stage and 0.79 at the mid-growth stage. The ET e versus ET m relationship was also strongly linear (r 2 = 0.90) at the tillering stage but weakly linear (r 2 = 0.50) at the mid-growth stage. The pan-based method thus provided reliable estimates of evapotranspiration during the tillering stage of rice.
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.
Problem statement: Understanding the relationships between rice yield and soil properties such as bulk electrical conductivity is of critical importance in precision farming. The apparent Electrical Conductivity of soil (ECa) is influenced by a combination of physico-chemical properties including soluble salts, clay content and mineralogy, soil water content, bulk density, organic matter and soil temperature. Accordingly, ECa is considered as the most reliable and frequently used tools in precision farming research for the spatio-temporal characterization of edaphic and anthropogenic properties that influence crop yield. Many researchers have found positive correlation of ECa to crop yield such as corn and soy bean but not rice paddies. This study discussed on the relationship between ECa and rice yield for best practice management on paddy field. Approach: The analyses had used two reliable methods in six selected paddy lots at Sawah Sempadan, Selangor, Malaysia. Stepwise Linear Regression (SLR) and Boundary Line Analysis (BLA) techniques were used. External factors such as weather conditions, disease outbreaks, labor shortage and other factors were not considered in the data analysis and interpretation. Results: The results indicate that deep ECa (ECad) is significantly related to rice yield with R2 = 0.1246 and R2 = 0.4156 from SLR and BLA analyses, respectively. Conclusion: Results of this study can benefit farmers and researchers to understand the influence of ECa to the crop productivity
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