Small Scale Spatial Variability of Apparent Electrical Conductivity within a Paddy Field

Quick variability description is an important component for zone management practices. Precision farming requires topping up of only the nutrients that are lacking in the soil to attain the highest yield with the least input. The apparent soil electrical conductivity (ECa) sensor is a useful tool in mapping to identify areas of contrasting soil properties. In nonsaline soils, ECa 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 ECa across a field quickly and gives detailed soil features (one-second interval) with few operators. Hence, a dense sampling is possible and therefore a high-resolution ECa map can be produced. This study aims to characterize the variability of soil ECa within a Malaysian paddy field with respect to the spatial and seasonal variability. The study was conducted at Block C, Sawah Sempadan, Selangor, Malaysia, for three continuous seasons. Soil ECa was collected after harvesting period. The results showed that deep ECa visualized the pattern of the former river routes clearly as continuous lines (about 45 m width) at the northern and central regions of the study area. This exploration has shown different maps with higher contrast as compared to the existing soil series map for the study area. Seasonal variability test showed that the ECa that was acquired during rainy season (collected after harvest in December to January) has the highest value as compared to another season

Rotary tillage effects on some selected physical properties of fine textured soil in wetland rice cultivation in Malaysia

The aim of this study was to investigate the effects of short-term repeated passes tillage operations on bulk density, soil penetration resistance, soil porosity and the moisture of a clay loam soil of Malaysia. A field experiment for three seasons was conducted at Sungai Burong Tanjung Karang Kuala Selangor, Malaysia to study treatments consisting of (I) no-tillage NT (II) first tillage FT (III) second tillage (ST), (IV) third tillage (TT) operations. The soil bulk density, soil penetrometer resistance, pore distribution, and moisture content characteristics were determined before and after for each of the three tillage. The penetration resistance was determined at the depths of 0–80 cm while the soil moisture was determined on the surface (0–20 cm). These properties were determined directly before and after tillage operations. All the tillage operations were significantly different in their effects on soil bulk density and soil penetration resistance. The soil bulk density decreased with the degree of soil manipulation after first and third tillage and increased after second tillage, with NT having the highest mean bulk density 1.04, 0.95 and 1.03 g/cm3 while TT having the least 0.84, 0.83 and 0.72 g/cm3 for 1st, 2nd and 3rd season respectively. The soil penetration resistance decreased due to tillage operation, with NT also having the highest resistance of 1.69 MPa and 1.44 Mpa in hardpan during 1st and 2nd season and the lowest PR was 0.09, 0.17 and 0.21 Mpa at TT in 1st, 2nd and 3rd season. Highest mean porosity was 0.68 in 2nd season at TT and the lowest mean porosity was 0.36 in 3rd season at NT. The lowest volumetric moisture content was at ST 0.26 and 0.27 in 1st and 2nd season at ST, and the highest was at TT 0.56, 0.57 and 0.68 at TT in 1st, 2nd and 3rd season respectively. The soil particle density was increased after three tillage operation. The highest increase (23.73%) was noted in FT 2nd season and the minimum was in TT in 1 st season (6.04%) while it decreased in ST during the three seasons

Yield components of sweet corn (Zea mays) and some soil physical properties towards different tillage methods and plant population

A field experiment was conducted to investigate the effects of three tillage systems and three corn plant populations on selected soil physical properties and yield components of sweet corn for Serdang soil series (Typic Paleodults) at the research farm of University Putra Malaysia (UPM) in Malaysia. The experimental design with three replications was a 2×3 factorial treatments based on randomized complete block design (RCBD) for soil analysis and a 3×3 factorial treatments based on RCBD for yield and yield components analysis of sweet corn (Zea mays). The three tillage systems were moldboard plow followed by once disc harrowing (MPD), disc plow followed by once disc harrowing (DPD) and rotary cultivator (RC) only, as control on soil physical properties at two depths of 0-10 cm and 20-30 cm; and also their effects on yield and selected yield components of sweet corn at three seeding rates or seed spacing of 20, 30 and 40 cm. The results showed that the measured soil physical properties were homogeneous at three plots and the two depths. Although WI (water infiltration) was higher and resistance to penetration (RP) was lower in RC plot at the upper layer, this condition had no influence on crop yield. The highest and minimum value of crop yield at given seed spacing occurred in DPD plot and MPD and RC plots, respectively. Interaction effects of the two factors, tillage and planting density were found to be significant on row length of kernels on cob corn, yield of sweet corn and total weight of dry matter

Geoelectrical parameters for the estimation of hydrogeological properties

Excessive groundwater extraction could cause environmental degradation such as surface water depletion, saltwater intrusion, and many more. Therefore, groundwater should be extracted in sustainable way to avert the harmful consequences. An accurate amount of sustainable groundwater yield can be obtained through the groundwater flow model that has low uncertainty. It is important to incorporate the actual hydrogeological properties into groundwater flow modeling to reduce the uncertainty. The purpose of this study is to estimate hydrogeological properties, namely, hydraulic conductivity (K) and transmissivity (T), by combining the electrical resistivity (ER) and induced polarization (IP) methods into an analytical equation. This study used an analytical equation that relates the geoelectrical parameters to the hydrogeological properties. The ER and IP methods were applied to improve the accuracy of geoelectrical parameters using the ABEM Lund Imaging system. The developed analytical equation was compared with other studies for verification. The results showed that the analytical equation model developed in this study had the lowest error compared to that of other published analytical equation models. Therefore, the combination of the ER and IP methods with a new proposed constant value for the analytical equation increased the accuracy of hydrogeological properties

Spatial soil analysis using geostatistical analysis and map Algebra

Evaluating soil spatial variability through sampling is an important step in precision farming processes that aids farmers to make informed decisions on the spread of agricultural inputs. Manual sampling is essential in ascertaining soil physical characteristics and could be used to monitor the chemical components like macronutrient nitrogen (N), phosphorus (P), and potassium (K). This type of sampling however could be costly and time consuming in macronutrient sampling. In order to show the ability of manual sampling to capture the essence of variability in the agricultural fields with enough number of samples and therefore, helping the precision farming process, we conducted an experiment on different designs of random, systematic, stratified random, stratified systematic, and different sizes of samples. The experiment was carried out on the geostatistical surfaces (base maps) created from a set of data which belonged to a rice plantation in Malaysia. A krigged map for each of these schemes was created and compared with the N, P, and K base maps. The results showed that the systematic and stratified systematic schemes were the most accurate sampling schemes in terms of estimation of mean. However, both stratified schemes were not successful to create the standard deviation of populations. Concerning the standard error of mean when the schemes were used in linear mixed effect modeling grouped by the sample size, stratified samples could produce lower standard error (except for stratified random sample of P). In terms of reproducing the original spatial variability, only systematic sampling scheme could create better accuracy in most cases. The result also revealed that the most important property of a sampling scheme in the study area is representativeness of samples, and the number of samples does not play an important role in accuracy and map making. Therefore, the data could be equally valid for the precision farmin