Prediction of water retention of soils from the humid tropics by the nonparametric k-nearest neighbor approach

Nonparametric approaches such as the k-nearest neighbor (k-NN) approach are considered attractive for pedotransfer modeling in hydrology; however, they have not been applied to predict water retention of highly weathered soils in the humid tropics. Therefore, the objectives of this study were: to apply the k-NN approach to predict soil water retention in a humid tropical region; to test its ability to predict soil water content at eight different matric potentials; to test the benefit of using more input attributes than most previous studies and their combinations; to discuss the importance of particular input attributes in the prediction of soil water retention at low, intermediate, and high matric potentials; and to compare this approach with two published tropical pedotransfer functions (PTFs) based on multiple linear regression (MLR). The overall estimation error ranges generated by the k-NN approach were statistically different but comparable to the two examined MLR PTFs. When the best combination of input variables (sand + silt + clay + bulk density + cation exchange capacity) was used, the overall error was remarkably low: 0.0360 to 0.0390 m(3) m(-3) in the dry and very wet ranges and 0.0490 to 0.0510 m(3) m(-3) in the intermediate range (i.e., -3 to -50 kPa) of the soil water retention curve. This k-NN variant can be considered as a competitive alternative to more classical, equation-based PTFs due to the accuracy of the water retention estimation and, as an added benefit, its flexibility to incorporate new data without the need to redevelop new equations. This is highly beneficial in developing countries where soil databases for agricultural planning are at present sparse, though slowly developing

Soil hydraulic properties of a Nitisol in Kabete, Kenya

Water relations are among the most important physical phenomena that affect the use of soils for agricultural, ecological, environmental, and engineering purposes. To formulate soil-water relationships, soil hydraulic properties are required as essential inputs. The most important hydraulic properties are the soil-water retention curve and the hydraulic conductivity. The objective of this study was to determine the soil hydraulic properties of a Nitisol, at Kabete Campus Field Station. Use of an internal drainage procedure to characterize the hydraulic properties and soil and water retention curves allowed for the establishment of the moisture and matric potential at field capacity and permanent wilting point. The Bt2 (84 -115) and Bt3 (115 - 143 cm) had the highest clay contents of 619 compared to Ap, AB and Bt1 horizons. The PWP was attained at soil moisture contents of 0.223, 0.284, 0277, 0.307 and 0.314 m3m-3 in the Ap, AB, Bt1, Bt2, and Bt3 horizons, respectively. Horizontal saturated hydraulic conductivity (Ksat) was high at 6.0 cm hr-1 in Ap horizon and decreased to 0.4 cm hr-1 in the subsurface horizon (Bt3). Ksat in the vertical direction was higher than horizontal and ranged from 8.3 cm hr-1 in surface layer to 0.6 cm hr-1 in Bt3 horizon, with exception of Bt1 and Bt2 where horizontal Ksat was greater than vertical. The Ap horizon also had the highest crop extractable water. Though the AB and Bt1 had the same water content at low matric suction, the variation was very wide as the SWRC approached saturation point. Bt1 and Bt2 also had similar water contents at suction range of – 7kPa after which Bt1, tended towards Bt3. Bt3 had the narrowest range of crop extractable water and thus was attributed to texture. The Bt3 retained the most amount of water at 0.314 m3m-3concluding that θPWP increased with depth. The total available water capacity between FC and PWP in the profile was 79.2 mm m-1. The study observed that the field capacity, crop available water contents and hydraulic conductivities were influenced positively by soil organic matter. The Van Genuchten parameters of air entry value (α) and pore size distribution (n) indicated that pore size distribution was not even in the AP and AB horizons. The field capacity was attained at higher matric potential at -5kPa for Bt1 while Bt2 and AP, AB, Bt2 and Bt3 was at -10kPa.The functional relationship, K(θ) = aθb that deals with water redistribution as a result of soil hydraulic properties and evaporative demand of the atmosphere was highly correlated to soil moisture content and texture with R2 values > 0.85

The arable farmer as the assessor of within-field soil variation

Feasible, fast and reliable methods of mapping within-field variation are required for precision agriculture. Within precision agriculture research much emphasis has been put on technology, whereas the knowledge that farmers have and ways to explore it have received little attention. This research characterizes and examines the spatial knowledge arable farmers have of their fields and explores whether it is a suitable starting point to map the within-field variation of soil properties. A case study was performed in the Hoeksche Waard, the Netherlands, at four arable farms. A combination of semi-structured interviews and fieldwork was used to map spatially explicit knowledge of within-field variation. At each farm, a field was divided into internally homogeneous units as directed by the farmer, the soil of the units was sampled and the data were analysed statistically. The results show that the farmers have considerable spatial knowledge of their fields. Furthermore, they apply this knowledge intuitively during various field management activities such as fertilizer application, soil tillage and herbicide application. The sample data on soil organic matter content, clay content and fertility show that in general the farmers’ knowledge formed a suitable starting point for mapping within-field variation in the soil. Therefore, it should also be considered as an important information source for highly automated precision agriculture systems

A computational toy model for shallow landslides: Molecular Dynamics approach

The aim of this paper is to propose a 2D computational algorithm for modeling of the trigger and the propagation of shallow landslides caused by rainfall. We used a Molecular Dynamics (MD) inspired model, similar to discrete element method (DEM), that is suitable to model granular material and to observe the trajectory of single particle, so to identify its dynamical properties. We consider that the triggering of shallow landslides is caused by the decrease of the static friction along the sliding surface due to water infiltration by rainfall. Thence the triggering is caused by two following conditions: (a) a threshold speed of the particles and (b) a condition on the static friction, between particles and slope surface, based on the Mohr-Coulomb failure criterion. The latter static condition is used in the geotechnical model to estimate the possibility of landslide triggering. Finally the interaction force between particles is defined trough a potential that, in the absence of experimental data, we have modeled as the Lennard-Jones 2-1 potential. In the model the viscosity is also introduced and for a large range of values of the model's parameters, we observe a characteristic velocity pattern, with acceleration increments, typical of real landslides. The results of simulations are quite promising: the energy and the time triggering distributions of local avalanches shows a power law distribution, analogous to the observed Gutenberg-Richter and Omori power law distributions for earthquakes. Finally it is possible to apply the method of the inverse surface displacement velocity [Fukuzono 1985] for predicting the failure time

A Comparative Study of Different Algorithms used to Predict the Crop, its Yield and Price

India is considered an agricultural land and many people have agriculture as their occupation. So India is in dire need of having Crop and its yield as well as its price prediction. Based on soil pH, Rainfall, humidity, temperature, and various factors we can predict the result. Our system will try to predict and recommend crop by considering some soil and atmospheric parameters. So there are various algorithms and techniques that can be taken into consideration like Decision tree Regressor, Random Forest, Particle Swarm Optimization (PSO)-Back Propagation (BP) Neural Network Model, K- Nearest Neighbor (KNN). After comparing the algorithms our aim is to find the best suitable algorithms for prediction which will lead us to find a proper crop according to a given set of factors

Modeling impacts of farming management alternatives on CO2, CH4, and N2O emissions: A case study for water management of rice agriculture of China

Since the early 1980s, water management of rice paddies in China has changed substantially, with midseason drainage gradually replacing continuous flooding. This has provided an opportunity to estimate how a management alternative impacts greenhouse gas emissions at a large regional scale. We integrated a process-based model, DNDC, with a GIS database of paddy area, soil properties, and management factors. We simulated soil carbon sequestration (or net CO2 emission) and CH4 and N2O emissions from China\u27s rice paddies (30 million ha), based on 1990 climate and management conditions, with two water management scenarios: continuous flooding and midseason drainage. The results indicated that this change in water management has reduced aggregate CH4 emissions about 40%, or 5 Tg CH4 yr−1, roughly 5–10% of total global methane emissions from rice paddies. The mitigating effect of midseason drainage on CH4 flux was highly uneven across the country; the highest flux reductions (\u3e200 kg CH4-C ha−1 yr−1) were in Hainan, Sichuan, Hubei, and Guangdong provinces, with warmer weather and multiple-cropping rice systems. The smallest flux reductions (\u3c25 kg CH4-C ha−1 yr−1) occurred in Tianjin, Hebei, Ningxia, Liaoning, and Gansu Provinces, with relatively cool weather and single cropping systems. Shifting water management from continuous flooding to midseason drainage increased N2O emissions from Chinese rice paddies by 0.15 Tg N yr−1 (∼50% increase). This offset a large fraction of the greenhouse gas radiative forcing benefit gained by the decrease in CH4 emissions. Midseason drainage-induced N2O fluxes were high (\u3e8.0 kg N/ha) in Jilin, Liaoning, Heilongjiang, and Xinjiang provinces, where the paddy soils contained relatively high organic matter. Shifting water management from continuous flooding to midseason drainage reduced total net CO2emissions by 0.65 Tg CO2-C yr−1, which made a relatively small contribution to the net climate impact due to the low radiative potential of CO2. The change in water management had very different effects on net greenhouse gas mitigation when implemented across climatic zones, soil types, or cropping systems. Maximum CH4 reductions and minimum N2O increases were obtained when the mid-season draining was applied to rice paddies with warm weather, high soil clay content, and low soil organic matter content, for example, Sichuan, Hubei, Hunan, Guangdong, Guangxi, Anhui, and Jiangsu provinces, which have 60% of China\u27s rice paddies and produce 65% of China\u27s rice harvest

Giving credit to reforestation for water quality benefits.

While there is a general belief that reforesting marginal, often unprofitable, croplands can result in water quality benefits, to date there have been very few studies that have attempted to quantify the magnitude of the reductions in nutrient (N and P) and sediment export. In order to determine the magnitude of a credit for water quality trading, there is a need to develop quantitative approaches to estimate the benefits from forest planting in terms of load reductions. Here we first evaluate the availability of marginal croplands (i.e. those with low infiltration capacity and high slopes) within a large section of the Ohio River Basin (ORB) to assess the magnitude of the land that could be reforested. Next, we employ the Nutrient Tracking Tool (NTT) to study the reduction in N, P and sediment losses from converting corn or corn/soy rotations to forested lands, first in a case study and then for a large region within the ORB. We find that after reforestation, N losses can decrease by 40 to 80 kg/ha-yr (95-97% reduction), while P losses decrease by 1 to 4 kg/ha-yr (96-99% reduction). There is a significant influence of local conditions (soils, previous crop management practices, meteorology), which can be considered with NTT and must be taken into consideration for specific projects. There is also considerable interannual and monthly variability, which highlights the need to take the longer view into account in nutrient credit considerations for water quality trading, as well as in monitoring programs. Overall, there is the potential for avoiding 60 million kg N and 2 million kg P from reaching the streams and rivers of the northern ORB as a result of conversion of marginal farmland to tree planting, which is on the order of 12% decrease for TN and 5% for TP, for the entire basin. Accounting for attenuation, this represents a significant fraction of the goal of the USEPA Gulf of Mexico Hypoxia Task Force to reduce TN and TP reaching the dead zone in the Gulf of Mexico, the second largest dead zone in the world. More broadly, the potential for targeted forest planting to reduce nutrient loading demonstrated in this study suggests further consideration of this approach for managing water quality in waterways throughout the world. The study was conducted using computational models and there is a need to evaluate the results with empirical observations

TRADEOFFS BETWEEN RURAL DEVELOPMENT POLICIES AND FOREST PROTECTION: SPATIALLY-EXPLICIT MODELING IN THE CENTRAL HIGHLANDS OF VIETNAM

Alleviating rural poverty remains an important objective of development policy in many areas of the world. However, traditional means of increasing rural livelihoods such as increased investments in agricultural intensification measures can have disastrous impacts on natural resources such as forests by greatly increasing incentives for clearing. This paper contains a spatially-explicit model of land use in the Dak Lak province in the Central Highlands of Vietnam. Land use is modeled using a reduced-form multinomial logit model, and policy simulations are conducted. These simulations demonstrate that the adoption of yield-increasing inputs requires concomitant forest protection policies, both in terms of forest area and spatial configuration.International Development,