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

Farm-Scale Soil Carbon Auditing

The soil system is recognized as a significant terrestrial sink of carbon. Estimates for the top meter of soil in the world range between 1,200 and 2,500 petagrams for organic C (Batjes 1996; Lal 2004). The reliable assessment and monitoring of soil carbon stocks is of key importance for soil conservation and in mitigation strategies for increased atmospheric carbon (Stockmann et al. 2013). Carbon credits are the heart of a cap-and-trade scheme, by offering a way to quantify carbon sequestered from the atmosphere; carbon credits gain a monetary value to offset a given amount of carbon dioxide releases (Paustian et al. 2009). The agricultural industry worldwide has the capacity to capture and store carbon emissions in soil (Paustian et al. 2000). However, there is still a debate on how soil can benefit for the offsets in the carbon economy because there is no good and efficient way of measuring soil carbon storage with appropriate statistical confidence (Post et al. 2001; Smith 2004b). A scheme that can measure and monitor soil carbon storage on a farm, which is crucial to the participation of the agricultural sector in the carbon economy, is essential