Growth and Development of a Malaysian Dipterocarp Forest After Harvest

The success of the Selective Management System (SMS) in managing" the hill dipterocarp forests of Peninsular Malaysia depends, among others, on the types of trees in the residual stand and the ability of these trees to grow and form the next crop. Specific information on growth and development of the residual stand is urgently required to evaluate the management system and its suitability in different forest types. In this study, data are analysed from logged over stands in the Lebir Forest Reserve, Peninsular Malaysia, which has been subjected to three harvesting intensities (HIs). The data consists of nine measurements covering a 14-year (1978-1991) period collected from nine permanent sample plots of size 200X200 m design under the systematic line sampling method. The plots were harvested first in 1977. Stocking, basal area and dbh growth of most species groups and HIs trees over 5 cm and over 15 cm dbh after harvest were significantly different (p<0.01) between the hill and lowland forests. The 5-15 cm dbh trees constituted more than 70 % of the total stocking and 20 % of total basal area. The stocking by dbh classes followed an inverse J-shape curve. The residual stand was dominated by non-dipterocarps. The potentially marketable (PM) and non-marketable species together accounted for more than 60% of total stocking and 58 % of total basal area in the both forests

Evaluation of Naked Barley Landraces for Agro-morphological Traits

Naked barley (Hordeum vulgare var. nudum L.) is a traditional, culturally important, climate-resilient winter cereal crop of Nepal. Evaluation of the naked barely genotypes for yield and disease is fundamental for their efficient utilization in plant breeding schemes and effective conservation programs. Therefore, to identify high yielding and yellow rust resistant landraces of naked barley for hilly and mountainous agro-ecosystem, twenty naked barley landraces collected from different locations of Nepal, were evaluated in randomized complete block design (RCBD) with three replications during winter season of 2016 and 2017 at Khumaltar, Lalitpur, Nepal. Combined analysis of variances revealed that NGRC04902 (3.46 t/ha), NGRC00886 (3.28 t/ha), NGRC02309 (3.21 t/ha) and NGRC06026 (3.10 t/ha) were the high yielding landraces and statistically at par with the released variety 'Solu Uwa' (3.15 t/ha). The landraces namely NGRC00837 (ACI Value: 1.86) was found resistant to yellow rust diseases. Landraces NGRC06034 (131.7 days) and NGRC02363 (130.8 days) were found early maturing and NGRC02306 (94.36 cm) was found dwarf landraces among tested genotypes. These landraces having higher yield and better resistance to yellow rust need to be deployed to farmers' field to diversify the varietal options and used in resistant breeding program to improve the productivity of naked barley for Nepalese farmers

Estimation of <i>Vs</i> profile using its natural frequency and Rayleigh-wave dispersion characteristics

International audienceThe evaluation of the natural frequency of random Vs profiles before analyzing the fundamental Rayleigh-wave dispersion characteristics is proposed in this paper. The inclusion of this parameter optimizes the effectiveness of random inverse searching to estimate Vs profiles. To demonstrate this method, a numerical test was performed using the "experimental" Rayleigh-wave dispersion curve obtained for a fictitious TEST site

Open and Closed Loop System Characteristics of a Tractor and an Implement Dynamic Model

Accurate guidance of towed implements is important for performing agricultural field operations and for gaining the ultimate benefit from an agricultural automatic guidance system. The study of open and closed loop system responses can be helpful in the design of practical guidance controllers. A dynamic model of a tractor and a towed implement system was developed. Open loop analysis of the kinematic and dynamic models revealed that the dynamic model was essential for capturing the higher order dynamics of the tractor and implement system at higher operating velocities. In addition, a higher fidelity dynamic model was also developed by incorporating steering dynamics and tire relaxation length dynamics. Closed loop system characteristics were studied by using a linear quadratic regulator (LQR) controller. The tractor position and heading and implement heading states along with respective rate states were fed back to close the loop. The higher fidelity closed loop system used a practical range of steering angles and rates to keep the response within nominal off-road vehicle guidance controller design specifications in the forward velocity range of 0.5 m/s to 10 m/s (1.8 km/h to 36 km/h). These simulation studies provided understanding about the characteristics of the tractor and towed implement system and showed promise in assisting in the development of automatic guidance controllers

Using Spatial Uncertainty of Prior Measurements to Design Adaptive Sampling of Elevation Data

Field sampling can be a major expense for planning within-field management in precision agriculture. An efficient sampling strategy should address knowledge gaps, rather than exhaustively collect redundant data. Modification of existing schemes is possible by incorporating prior knowledge of spatial patterns within the field. In this study, spatial uncertainty of prior digital elevation model (DEM) estimates was used to locate adaptive re-survey regions in the field. An agricultural vehicle equipped with RTK-DGPS was driven across a 2.3 ha field area to measure the field elevation in a continuous fashion. A geostatistical simulation technique was used to simulate field DEMs using measurements with different pass intervals and to quantitatively assess the spatial uncertainty of the DEM estimates. The high-uncertainty regions for each DEM were classified using image segmentation methods, and an adaptive re-survey was performed on those regions. The addition of adaptive re-surveying substantially reduced the time required to resample and resulted in DEMs with lower error. For the widest sampling pass width, the RMSE of 0.46 m of the DEM produced from an initial coarse sampling survey was reduced to 0.25 m after an adaptive re-survey, which was close to that (0.22 m) of the DEM produced with an all-field re-survey. The estimated sampling time for the adaptive re-survey was less than 50% of that for all-field re-survey. These results indicate that spatial uncertainty models are useful in an adaptive sampling design to help reduce sampling cost while maintaining the accuracy of the measurements. The method is general and thus not limited to elevation data but can be extended to other spatially variable field data

Distributed Virtual Reality Simulation Assisted Steering Controller Design for Off Road Vehicle and Implement Tracking

For virtual reality simulation of off--road vehicles, real--time simulation must be achieved in spite of the heavy computational load from 3D graphics generation and numerical analysis of the dynamic model. In this work, a distributed architecture was developed for off--road vehicle and implement dynamic model and 3D graphics visualization to distribute the overall computational load of the system across two or more machines. This architecture consists of three major components: a dynamic model simulator, a virtual reality simulator for 3D graphics, and an interface to the controller hardware elements. Several off--road vehicle dynamics models have been developed with varying degrees of fidelity, as well as automatic guidance controller models and an interface to automatic guidance hardware. A towed implement model and an implement tracking steering controller developed. The performance of an implement position and heading feedback controller was similar to that of a tractor position and heading feedback controller. These models provide understanding into the behavior of automatically guided tractor--implement systems. In addition, the simulation and visualization system was effectively used to examine the practical limitations that the designed controller may face and to design the controller gains to adjust for those limitations

Parameter Sensitivity Analysis of a Tractor and Single Axle Grain Cart Dynamic System Model

Tractor and towed implement system models have become increasingly important for model-based guidance controller design, virtual prototyping, and operator-and-hardware-in-loop simulation. Various tractor and towed implement models have been proposed in the literature which contain uncertain or time-varying parameters. Sensitivity analysis was used to identify the effect of system parameter variation on system responses and to identify the most critical system parameters. Sensitivity analysis was performed with respect to three tire cornering stiffness parameters, three tire relaxation length parameters, and two implement inertial parameters. Overall, the system was most sensitive to the tire cornering stiffness parameters and least sensitive to the implement inertial parameters. In general, the variation in the input parameters and the system state variables were related in a non-linear fashion. With the nominal parameter values for a MFWD tractor, a single axle grain cart, and corn stubble surface conditions, a 10% variation in cornering stiffness parameters caused a 5% average variation in the system responses whereas an 80% change in cornering stiffness parameters caused an 80% average variation at 4.5 m/s forward velocity. If a 10% average variation in system responses is acceptable, the cornering stiffness parameters and implement inertial parameters must be estimated within 20% and 30% of actual/nominal values respectively. The relaxation length parameters have to be within 75% of the nominal values

Estimation of Optimal Biomass Removal Rate Based on Tolerable Soil Erosion for Single-Pass Crop Grain and Biomass Harvesting System

As the demand for biomass feedstocks grows, it is likely that agricultural residue will be removed in a way that compromises soil sustainability due to increased soil erosion, depletion of organic matter, and deterioration of soil physical characteristics. Since soil erosion from agricultural fields depends on several factors including soil type, field terrain, and cropping practices, the amount of biomass that can be removed while maintaining soil tilth varies substantially over space and time. The RUSLE2 soil erosion model, which takes into account these spatio-temporal variations, was used to estimate tolerable agricultural biomass removal rates at field scales for a single-pass crop grain and biomass harvesting system. Soil type, field topography, climate data, management practices, and conservation practices were stored in individual databases on a state or county basis. Geographic position of the field was used as a spatial key to access the databases to select site-specific information such as soil, topography, and management related parameters. These parameters along with actual grain yield were provided as inputs to the RUSLE2 model to calculate yearly soil loss per unit area of the field. An iterative technique was then used to determine site-specific tolerable biomass removal rates that keep the soil loss below the soil loss thresholds (T) of the field. The tolerable removal rates varied substantially with field terrain, crop management practices, and soil type. At a location in a field in Winnebago county, Iowa, with ~1% slope and conventional tillage practices, up to 98% of the 11 Mg ha-1 total above-ground biomass was available for collection with negligible soil loss. There was no biomass available to remove with conventional tillage practices on steep slopes, as in a field in Crawford county, Iowa, with a 12.6% slope. If no-till crop practices were adopted, up to 70% of the total above-ground biomass could be collected at the same location with 12.6% slope. In the case of a soybean-corn rotation with no-till practices, about 98% of total biomass was available for removal at the locations in the Winnebago field with low slopes, whereas 77% of total biomass was available at a location in the Crawford field with a 7.5% slope. Tolerable removal rates varied substantially over an agricultural field, which showed the importance of site-specific removal rate estimation. These removal rates can be useful in developing recommended rates for producers to use during a single-pass crop grain and biomass harvesting operation. However, this study only considered the soil erosion tolerance level in estimating biomass removal rates. Before providing the final recommendation to end users, further investigations will be necessary to study the potential effects of continuous biomass removal on organic matter content and other biophysical properties of the soil

Assessing the Effects of DEM Uncertainty on Erosion Rate Estimation in an Agricultural Field

The slope length and steepness (LS) factor is one of the factors in the Revised Universal Soil Loss Equation (RUSLE) needed to estimate average annual erosion rate. The LS factor is often derived from digital elevation models (DEM). DEM errors and uncertainty could affect LS factor estimation and consequently erosion rate estimation. However, DEM uncertainties are not always accounted for, and the effects are not always evaluated in erosion rate estimation. This study compared the erosion rate estimation of a 62.81 ha agricultural crop area using a 7.5 min USGS DEM and DEMs developed using real-time kinematic differential GPS (RTK-DGPS) and dual-frequency DGPS (DF-DGPS) field surveys. Spatial estimation and uncertainty analysis was carried out using sequential Gaussian simulation (SGS). A total of 50 equiprobable DEM realizations were produced using SGS to assess DEM uncertainty and quantify its effect on erosion rate estimation. DEM uncertainty substantially affected the resulting erosion rate estimation. The uncertainty of the average annual erosion rate estimates across the study field was represented using 95% confidence intervals (CI). For the DF-DGPS and USGS DEMs, the percentages of the field area that have erosion rate CIs greater than 11.21 Mg ha-1 year-1 (5 tons acre-1 year-1) were 81% and 85%, respectively, which were substantially larger than that of the RTK DEM (0.41%). The average annual erosion rate map produced using a USGS DEM contained artifacts and underestimated the erosion rate estimation in many areas of the field. The results suggested that higher-accuracy DEMs generated using RTK-DGPS measurements are more appropriate for erosion rate estimation in an agricultural field. Knowledge of DEM uncertainty and its effect on the erosion rate estimation was useful to better judge the reliability of erosion rate estimates