Destructive Sampling Methods Research Articles

Differential performance of aqueous- and ethylic-Lugol's iodine stain to visualize anatomy in μCT-scanned vertebrates.

Museum specimens are an increasingly important tool for studying global biodiversity. With the advent of diffusible iodine-based contrast-enhanced computed tomography (diceCT), researchers can now visualize an organism's internal soft tissue anatomy without the need for physical dissection or other highly destructive sampling methods. However, there are many considerations when deciding which method of staining to use for diceCT to produce the best gray-scale contrast for facilitating downstream anatomical analyses. The general lack of direct comparisons among staining methodologies can make it difficult for researchers to determine which approaches are most appropriate for their study. Here, we compare the performance of ethylic-Lugol's iodine solution with aqueous-Lugol's staining solution across several vertebrate orders to assess differential imaging outcomes. We found that ethylic-Lugol's is better for visualizing muscle attachment to bone but provides overall lower contrast between soft tissue types. Comparatively, aqueous-based Lugol's provides high-contrast imaging among soft tissue types, although bone is more difficult to discern. We conclude that the choice of staining methodology largely depends on the type of anatomical data the researcher wishes to collect, and we provide a decision-based framework for assessing which staining methodology (ethylic or aqueous) is most appropriate for desired imaging results.

Incidence of Insect Pests of Carrot, Daucus carota L. and Their Natural Enemies with Special Reference to Carrot Fly, Melanagromyza sp.

Roving survey was conducted in major carrot growing districts of Karnataka, India during rabi/summer 2021-22 to record the incidence of different insect pests and their natural enemies in carrot at the pre-rooting stage and rooting stage. Observations were made from the three randomly selected spots in each field and from each spot, five plants were observed for incidence by looking at the symptoms inflicted by the different insect pests. Data on damage by agromyzid fly was registered by adopting destructive sampling method. Among the three districts surveyed, the highest mean % infestation of agromyzid fly during the pre-rooting stage was recorded in Gadag (22.40%) followed by Dharwad (19.63%) and Belagavi district (15.12%). During rooting stage, the highest mean % infestation of agromyzid fly was documented in Gadag (50.56%) followed by Belagavi district (48.09%) and Dharwad (46.56%). Gadag witnessed the highest % damage of the agromyzid fly during pre-rooting and rooting stages which might be due to year-round cultivation of carrot. Further, monocropping without crop rotation might have attributed to the highest incidence of insect pest of carrot. Sucking insect pests are of minor importance as compared to agromyzid fly, as it causes damage to edible taproot which is of economic importance in carrot cultivation.

Estimation of potato yield using a semi-mechanistic model developed by proximal remote sensing and environmental variables

Timely monitoring of potato yield status is crucial for guiding field management and ensuring the sustainability of food trade. In recent years, non-destructive remote sensing techniques have emerged as one of the most promising methods for crop yield monitoring. However, most existing potato yield estimation models have primarily been tested at specific growth stages within a single ecosystem, highlighting the urgent need for a model that can be applied across different ecological environments. In this study, we developed a semi-physical model based on hierarchical linear modeling (HLM) to automatically correct yield estimation based on remotely sensed vegetation indices using environmentally relevant variables such as net solar radiation at the surface (ssr), air temperature at 2 m above the surface (t2m), and soil water content in the root zone (swvl). The accuracy and transferability of the model were further evaluated by comparing it with aboveground destructive sampling methods, conventional empirical models, and machine learning approaches. The results demonstrated that site-specific relationships exist between aboveground agronomic traits parameters like biomass and leaf area index (LAI) with respect to yield estimation. Conventional linear statistical and machine learning methods faced challenges in transferring their constructed yield estimation models across regions and years. However, the HLM approach exhibited excellent generalization ability across all trial samples. In the middle and later stages of potato growth, the standardized LAI determining index proved to be the most effective vegetation for predicting yield, while ssr, sktmax, and shallow swvl were identified as key environmental variables affecting yield. By integrating these variables using HLM modeling techniques, we achieved optimal generalization performance (R2 = 0.57, RMSE = 7.65 t/ha, NRMSE = 25.02 % during tuber growth stage; R2 = 0.60, RMSE = 7.37 t/ha, NRMSE = 24.13 % during starch accumulation stage). This study underscores the importance of considering environmental factors when constructing a reliable model for estimating potato yield at different spatial and temporal scales using remote sensing technology.

Combining three-dimensional acoustic coring and a convolutional neural network to quantify species contributions to benthic ecosystems.

The seafloor is inhabited by a large number of benthic invertebrates, and their importance in mediating carbon mineralization and biogeochemical cycles is recognized. However, the majority of fauna live below the sediment surface, so most means of survey rely on destructive sampling methods that are limited to documenting species presence rather than event driven activity and functionally important aspects of species behaviour. We have developed and tested a laboratory-based three-dimensional acoustic coring system that is capable of non-invasively visualizing the presence and activity of invertebrates within the sediment matrix. Here, we present reconstructed three-dimensional acoustic images of the sediment profile, with strong backscatter revealing the presence and position of individual benthic organisms. These data were used to train a three-dimensional convolutional neural network model and, using a combination of data augmentation and data correction techniques, we were able to identify individual species with an 88% accuracy. Combining three-dimensional acoustic coring with deep learning forms an effective and non-invasive means of providing detailed mechanistic information of in situ species-sediment interactions, opening new opportunities to quantify species-specific contributions to ecosystems.

Modeling Forest Above-Ground Biomass of Teak (Tectona grandis L. F.) Using Field Measurement and Sentinel-2 Imagery

Over the last few decades, remote sensing has emerged as a dependable and cost-effective method for collecting precise data on forest biophysical parameters, aiding in sustainable forest management and global initiatives to combat climate change. This research aimed to develop a model for estimating the above-ground biomass (AGB) of Teak (Tectona grandis L. F.) by combining field measurements with Sentinel-2 earth observation data. The study took place in 36-year-old teak plantation areas within the Sagarnath Forest Development Project in Nepal’s Sarlahi district. Field measurements were conducted using a destructive systematic sampling method, employing 10 × 10 m2 sample plots, and the volume of logs was determined using Newton’s formula. A total of 30 sample plots were used for calibration, while 10 were utilized for validation purposes. The findings revealed that the average AGB per plot was 814 kg (equivalent to 81.4 t ha−1), with a minimum value of 716 kg (71.6 t ha−1) and a maximum value of 1,060 kg (106 t ha−1). The study utilized five independent variables, namely, the Red band, Green band, Blue band, near-infrared (NIR), and normalized difference vegetation index (NDVI) values from Sentinel-2 imagery data, to develop estimation models. Among the 12 models examined, model M10 proved to be the best fit for accurate AGB estimation (adjusted R2 = 0.9809, RMSE = 0.01269, AIC = −170, and p-value = < 8.39e−21). The equation of the best-fitted model was ln (AGB) = A + B × Red + C × Green + D × Blue2 + E × ln (NIR) + F × ln (NDVI), providing an accurate estimate of AGB. Model validation involved a t-test comparing the observed and calculated AGB values for ten sample plots, demonstrating no significant difference (p-value = 0.3662 > 0.05). This model has the potential to facilitate AGB biomass calculations and carbon stock estimates for teak plantations of similar age groups.

165 Does supplementation of beef cattle impact the agronomy performance of tall fescue and bermudagrass pastures?

Abstract In forage-based beef cattle systems, bermudagrass [Cynodon dactylon (L.) Pers.] and tall fescue (Festuca arundinacea Schreb.) stand as widely used forage species. The strategic supplementation of animals may reduce forage intake, thereby potentially affecting the agronomic performance of these grasses. Our objective was to evaluate the impact of beef cattle supplemental feeding on forage accumulation, forage mass, and nutritive value in tall fescue and bermudagrass pastures. Two studies were conducted where pregnant beef cows were supplemental fed 1% of BW on DM basis of corn silage daily (SUP) or received no supplemental feeding (free-choice mineral; NON). Trial 1: conducted on bermudagrass pastures (BERM) grazed from late May to early October. Experimental design for BERM was completely randomized design with 4 pastures per treatment (n = 3 cows/pasture; average 5 years of age; 550 ± 72 kg initial BW). Trial 2: conducted on fescue pastures (FES) with an experimental design of a 2 x 2 factorial arrangement. The two factors evaluated were fescue type [TOXIC (507 ppm ± 0.82 SEM ergot alkaloids; 92.5% endophyte infection)] or non-toxin-producing (NT; 5 ppm ± 2.50 SEM ergot alkaloids; 38.6% endophyte infection) and two levels of supplemental feeding (NON or SUP). There were 3 or 4 replications per fescue x supplemental feeding treatment (n = 15 pastures), stocked with 3 cows/pasture (average age 4.75 years; 558 ± 73 kg initial BW) and grazed from early May through November. Forage accumulation (FA) was estimated every 28 d using the paired-cage method (4 exclosure cages/pasture). On each exclusion period, the forage mass (FM) was estimated both inside and outside the cages through destructive sampling method and samples analyzed for forage quality. Pasture was experimental unit and repeated measures were included in analysis. Bermudagrass FM tended to be increased by 31% with SUP (P = 0.09; 774 kg/ha greater). Forage accumulation tended (P = 0.07) to be affected by feeding method x month. There was greater FA in June for SUP; however, there was greater FA for NON in July. SUP pastures had greater ADF (1% more; P = 0.01) and reduced TDN (1.5%; P < 0.01) than NON. Forage mass tended to be 10% greater for TOXIC pastures (P = 0.09; 409 kg/ha more forage mass). Supplemental feeding on fescue did not impact FM (P = 0.74). Forage accumulation was not impacted by fescue type, feeding method, nor the interaction (P > 0.30). Cows generally do not consume as much toxic fescue, thus more forage is available in pastures and supplemental feeding did not alter forage parameters. However, on a warm-season grass that is growing while supplemental feeding, forage availability was increased and maybe useful as method to increase stocking rates or as mitigation tool during drought.

Intercomparison of leaf area index observed by destructive sampling, plant canopy analyzer and tracing radiation and architecture of canopies in paddy fields

ABSTRACT Leaf area index (LAI) is a canopy structure parameter used to characterize vegetation growth. It is increasingly important to obtain continuous and accurate ground LAI measurement data for the validation of remote-sensing products. In this study, direct destructive sampling method and indirect optical measurement including LAI-2200 plant canopy analyser (PCA) and Tracing Radiation and Architecture of Canopies (TRAC) were used for long-term continuous observation of paddy fields in southern China, to explore the variation of LAI during the growth period of paddy, and to quantitatively analyse the errors of indirect optical measurement. The results show that (1) LAI and Plant area index (PAI) increased first and then decreased in the growing period. The proportion of green leaf area gradually decreased at reproductive growth stage (less than 50%). There was a linear relationship between the proportion of green leaf area and PAI at this stage, with a good correlation (R2 = 0.73). This linear relationship can be used to convert PAI into LAI more conveniently. (2) Effective PAI (PAIe) calculated by the two methods (2000 method and Lang method) from PCA showed good consistency between each other in the growing season (the maximum value of R2 is 0.99). With the increase of zenith angle, the consistency between 2000 method and Lang method decreases (R2 = 0.83). Multiple scattering has an effect on PCA measurement, and this effect is more obvious at high zenith angle. Lang method is more sensitive to the change of zenith angle. (3) Compared to the destructive values, both 2000 method and Lang method are able to compute accurate LAI using four ring sensors (4 R), while TRAC underestimates the LAI and PAI from destructive sampling method. This study provided a new idea for reducing the impact of ground reference data errors on LAI product validation.

Estimating rice crop (Oryza sativa L.) parameters during the 'Yala' season in Sri Lanka using UAV multispectral indices

Remote estimation of aboveground biomass (AGB), plant height (PH), and rice yield (RY) before the actual harvest is vital for the early management of rice crops. Traditionally, on-ground spot measurements and destructive sampling methods were conducted across the field which is problematic due to poor temporal and spatial representation, time consumption, delayed crop management and inefficient input application. Thus, this study evaluates the advanced aerial multispectral imagery technique to explore the association of image-derived vegetation indices (VIs) with the ground measured AGB, PH and RY at crop season “Yala” 2021 in the controlled field and extend the relationship with the commercial farmer field data. The experiment was carried out at a controlled rice field and then at a commercial farmer field cultivated with three-month crop duration rice varieties as Bg 300 and Bg 374, respectively. Multispectral images of the field were acquired by an Unmanned Ariel Vehicle (UAV, DJI P4-multispectral, Shenzhen, China) at 25m flying height during the booting stage of the rice plant. Then VIs was derived using QGIS (version 3.16) software and analyzed to select the best combination of VIs explaining the ground measured parameters using partial least squares regression (PLS-R). According to the results, a strong agreement between growth parameters during the booting stage at 25m flight height was proven. When comparing the relationship at the commercial farmer field, the estimated AGB showed strong accuracy (R2 = 0.62, RMSE = 0.055 kg/m2) with the observed AGB using VIs (GRVI and SR). Estimated PH showed weak accuracy (R2 = 0.04, RMSE = 19.33 cm) with the observed PH using (CI red edge, TCARI and NPCI) and estimated RY showed strong accuracy (R2 = 0.75, RMSE = 817.8 kg/ha) with observed RY using VIs (TVI, SR and MSR). Therefore, this study proves an association of VIs with rice crop parameters AGB and RY in rice cultivation that can be used to measure non-destructively prior to harvest during “Yala” in Sri Lanka.

Developing stem taper of Shorea robusta in the far-western Terai of Nepal

Tree taper functions expressed in terms of height and diameter at breast height (BDH) can provide accurate and timely information on current growing stock. Taper equations are very important for calculation of growing stock in forest inventories, however, in the context of Nepal, these are still unavailable at the required level. The study aimed to develop taper equations, so that those can be used to predict the diameter anywhere along the stem, and estimate tree volumes at desired sections. The input data was a destructive sampling method of 81 sample trees of Shorea robusta distributed in ten locations of the far western Terai of Nepal (Kailali and Kanchanpur districts).
 Using two independent B-Spline and 5th-degree polynomial taper models, the upper stem diameters were imputed. Both models were applied on the whole dataset, irrespective of DBH size, to get common fitted taper models. Later, the same models were tested for three different DBH classes to compare and evaluate the best-fit model. 
 Taper models, developed under the B-spline polynomial model of 3rd degree, were found to be highly dependent on the tree (DBH) sizes. Thus, better models could be developed by classifying the whole dataset based on different DBH classes. On the other hand, developed models under the 5th degree polynomial taper model did not exhibit their dependency on the tree (DBH) sizes and a better model was found for the unclassified datasets, i.e. for all DBH sizes.

Estimation of grain filling rate of winter wheat using leaf chlorophyll and LAI extracted from UAV images

The grain filling rate (GFR) plays a pivotal role in assessing winter wheat grain development and guiding crop variety selection and cultivation strategies. Presently, GFR monitoring primarily relies on destructive sampling methods, which are impractical for swift assessments in extensive agricultural fields. Our study introduces a novel approach for estimating winter wheat grain filling rate (GFR) using leaf chlorophyll content (LCC) and Leaf Area Index (LAI) extracted from UAV imagery. Initially, we established a quantitative relationship between GFR and LAI as well as LCC in winter wheat. Subsequently, UAV multispectral imagery was employed to track the time-series dynamics of LAI and LCC during the grain-filling stage. Ultimately, we constructed an estimation model for winter wheat GFR and achieved plot-scale GFR mapping through UAV-based imaging technology. To identify sensitive vegetation index combinations for LAI and chlorophyll content (SPAD), we employed adaptive re-weighted sampling and continuous projection algorithms, respectively. Following this, we employed partial least squares and random forest algorithms to develop LAI and SPAD estimation models, which were then compared to full-band regression. Finally, GFR estimation was based on the conversion models of LAI and SPAD to GFR. Our findings revealed that, despite variations in water supply, there were no significant differences in GFR trends among the irrigated treatments. However, in the nitrogenous fertilizer application treatments, GFR initially declined with increased nitrogenous fertilizer supply, reaching its peak between 21 and 25 days post-flowering, during which period the differences in GFR among the various nitrogenous fertilizer application treatments were not significant. As the reproductive period advanced, higher nitrogenous fertilizer treatments maintained a higher GFR for a more extended period and experienced a slower decline compared to the lower nitrogenous fertilizer treatments. LAI validation yielded an R2 of 0.84, a root mean square error (RMSE) of 0.39, and a normalized root mean square error (NRMSE) of 10.9%. SPAD validation resulted in R2, RMSE, and NRMSE values of 0.79%, 1.30%, and 12.5%, respectively. The GFR estimation achieved an R2 of 0.77, an RMSE of 0.32 mg/d, and an NRMSE of 11.3%. Mapping the GFR distribution in the field, coupled with water and fertilizer management, supports UAVs in monitoring crop growth and refining field-level water and fertilizer decisions.

Estimation of Aboveground Carbon Stock in The Pertamina Hulu Rokan (PHR) Forest in Pinggir District, Bengkalis Regency, Riau Province

Extreme climate change affects human life. One of the causes of climate change is the increase of CO2 gas in the air. Forests can absorb CO2 from the air and store it in the form of tree biomass through the process of photosynthesis. One of the forests that can absorb and store carbon is Pertamina Hulu Rokan (PHR) forest, therefore it is necessary to conduct research on estimating the carbon content of PHR Forest area. The methods to be used in this research are non-destructive sampling and destructive sampling methods. The method with harvesting is only for seedlings, undergrowth and litter, while for saplings, poles, trees and necromass with the method without harvesting. Aboveground carbon storage in PHR forest was found to be 95.37 tonnes/ha. The largest carbon stocks were from trees (68.96 tonnes/ha), poles (13.06 tonnes/ha), necromas (5.70 tonnes/ha), litter (3.72 tonnes/ha), saplings (3.54 tonnes/ha) and the smallest carbon stocks were seedlings and understorey (0.39 tonnes/ha).

Species-Specific Allometric Equations, Biomass Expansion Factor, and Wood Density of Native Tree Species in the Dry Afromontane Forest of Ethiopia

A forest is a storehouse of carbon released from different sources when the activities of sustainable forest management, planting, and rehabilitation exist. However, few allometric equations are present to determine its contribution to carbon reduction. The target of the study was to develop species-specific allometric equations and establish a database for biomass expansion factor and wood density for five tree species grown in the dry Afromontane forest of Ethiopia. A direct or destructive sampling method was used on 62 trees from different diameter classes. The diameter at breast height and the total height of selected trees ranged from 7 to 48 cm and 6.7 to 23.4 m, respectively. Trees were felled and divided into various biomass sections. Stem and big branch discs were sampled to determine the wood density and volume of the trees. Sample wood and foliage were oven-dried for three days and two days at 105°C and 70°C, respectively, to get their dry weight. Total above-ground biomass was regressed using diameter at breast height, total height, wood density, and average crown diameter as independent variables. R software version 4.0.1 was used to fit the biomass equations. The best biomass models were determined to have lower AIC and RSE and highest adj. R2. The biomass expansion factor and wood density of five tree species ranged from 1.19 to 1.40 and 0.53 to 0.74 g/cm−3, respectively. Species-specific allometric equations were better than both mixed species and pan tropical models for the assessment of above-ground biomass in the Chilimo dry Afromontane forest of Ethiopia.

Hyperspectral remote sensing for tobacco quality estimation, yield prediction, and stress detection: A review of applications and methods.

Tobacco is an important economic crop and the main raw material of cigarette products. Nowadays, with the increasing consumer demand for high-quality cigarettes, the requirements for their main raw materials are also varying. In general, tobacco quality is primarily determined by the exterior quality, inherent quality, chemical compositions, and physical properties. All these aspects are formed during the growing season and are vulnerable to many environmental factors, such as climate, geography, irrigation, fertilization, diseases and pests, etc. Therefore, there is a great demand for tobacco growth monitoring and near real-time quality evaluation. Herein, hyperspectral remote sensing (HRS) is increasingly being considered as a cost-effective alternative to traditional destructive field sampling methods and laboratory trials to determine various agronomic parameters of tobacco with the assistance of diverse hyperspectral vegetation indices and machine learning algorithms. In light of this, we conduct a comprehensive review of the HRS applications in tobacco production management. In this review, we briefly sketch the principles of HRS and commonly used data acquisition system platforms. We detail the specific applications and methodologies for tobacco quality estimation, yield prediction, and stress detection. Finally, we discuss the major challenges and future opportunities for potential application prospects. We hope that this review could provide interested researchers, practitioners, or readers with a basic understanding of current HRS applications in tobacco production management, and give some guidelines for practical works.

Study of the Relationship between Biomass and Fractional Green Canopy Cover of Two Forage Crops Using Canopeo®

Rapid and accurate estimation of biomass is crucial for the assessment of the crop nutrition status and the improvement of crop management strategies. The study was conducted in Setif province (Northeast of Algeria). Canopy cover was calculated using Canopeo® application, at stem elongation, booting, and heading stages for triticale, and V5, R1, and R3 for lentil. The results indicate a high correlation between canopy cover and triticale biomass, at the stem elongation stage with R2 = 0.98 and 0.89 under no till and conventional till, respectively. The RMSE was 0.1 and 0.06t/ha, under the two modes. The lowest correlation is seen at heading stage with a value of 0.21 and RMSE of 0.65t/ha, under no till mode. For lentil, the high correlations between are observed at R3 (early pod), and R1 (early bloom) stages, with a coefficients of 0.85 and 0.98, under zero till and conventional respectively. The RMSE was 0.17t/ha. This smartphone application is suitable for assessing triticale and lentil biomass; it is rapid, easy, and could replace the destructive sampling method.

Growth Performances of Cocoa (<em>Theobroma Cacao</em>) Varieties under Mature Oil Palm with Palm Oil Mill Effluent as Growing Media

Oil palm (Elaeis guineensis) is a leading edible oil-producing plant established in tropical countries which cause many negative impacts. Poor productivity of monoculture plantations and land and aquatic pollution caused by haphazard release of Palm Oil Mill Effluent (POME) are critical concerns. As a remedy, oil palm can be intercropped with cocoa by using POME as a potting media. In this study, we evaluated the growth of two different cocoa varieties with POME as the potting media under 20 years old oil palm shade. Two cocoa varieties (SCA6 x ICS6, and NA32 x ICS1) and three potting media (topsoil only, 40% topsoil with 60% POME, and 60% topsoil with 40% POME) were factorially combined in a randomized complete block design with three replicates under 40% shade, Faculty of Agriculture, University of Ruhuna. Growth parameters of the cocoa seedlings such as plant height, number of leaves, chlorophyll value, stem girth, root and shoot dry weights, root length, number of lateral roots per plant were measured using destructive and non-destructive sampling methods. The potting media consist of 40% topsoil with 60% POME showed significantly the highest plant height, number of leaves, chlorophyll value, stem girth, fresh and dry weight of shoot and roots and length of roots. Hence, potting media with 40% topsoil with 60% POME was recognized as the best potting media for the growth and development of cocoa seedlings of both varieties. Growth of cocoa seedling was well compatible under oil palm shade at full light condition with the use of POME as the potting media.

Growth Response of Red Oaks to Climatic Conditions in the Lower Mississippi Alluvial Valley: Implications for Bottomland Hardwood Restoration with a Changing Climate

Bottomland hardwood forests (BHFs) offer a wide range of ecosystem services that are of high environmental and socioeconomic value. Yet, nearly 70% of BHFs in the southern United States have been lost during the past 100 years primarily due to land use change including agricultural expansion, calling for restoration efforts. We estimated the statistical relationship of the annual radial growth rate of three red oak species with climatic conditions and tree age using the tree ring data collected from a BHF plantation in the Arkansas Delta region. These species were Cherry bark oak (Quercus pagoda), Shumard oak (Quercus shumardii), and Nuttall oak (Quercus texana). The destructive sampling method was employed to obtain tree growth data and the cross-dating method was used for tree age determination. A log-linear regression model was estimated to uncover the statistical relationship between annual tree ring growth rate and climatic conditions. We identified the most critical time windows of climate variables that affect the growth of these trees. We found that the average temperature in October of the previous year and the minimum temperature between December of the previous year and January of the current year were positively associated with the radial growth rate in the current year although the maximum temperature from January to August and total precipitation from April to July of the current year were negatively correlated with the growth rate. Compared to Cherry bark and Shumard oaks, Nuttall oak was less sensitive to a rise in the minimum temperature between December and January. The projected climate change is likely to create slightly more favorable overall climatic conditions for these oak species in the region. Our findings suggest that these three red oak species are well suited for the study region for restoring BHFs, especially with a changing climate.

Monitoring mites in orchards: absence or non-detection?

Successful integrated pest management (IPM) programs rely on effective and efficient methods of monitoring pest and beneficial species, including mites. Several methods have been proposed for monitoring mites in Australian orchards, mainly based on examining leaves in situ. During the 2021–2022 growing season we flagged 1–2 pear trees Pyrus communis in each of 192 panels (a panel being trees in a 14 m length of a row) in a pear orchard at Tatura, Victoria, Australia. For each flagged tree we examined mite species occurrence and prevalence using three methods, (1) non-destructive visual examination of 10 leaves per panel using a 10x hand lens (current industry standard), (2) destructive lab-based examination of 4 leaves per flagged tree(s) using a stereo microscope, and (3) destructive lab-based examination of 2 leaves per flagged tree(s) using a stereo microscope. The presence and prevalence of pest mites and the previously released predatory mite Neoseiulus californicus were recorded. Throughout the season we observed various pest mites in the orchard including Tetranychus urticae, Panonychus ulmi, Eriophyes pyri, and Bryobia rubrioculus and the predatory mite N. californicus. However, the number of species detected and the prevalence of detected species on leaves were dependant on sampling method used. Both destructive sampling methods, method (2) and method (3), performed better than the current non-destructive industry standard in terms of species detection and prevalence. The largest differences were seen during the early stages of infestation for mite species other than E. pyri. Early detection of certain mite species is important because it can provide a more accurate estimate of the starting point for Cumulative Leaf Infested Days (CLIDs) calculations, resulting in better informed management decisions. Importantly, detection and prevalence results were similar for methods (2) and (3) despite the greatly reduced sampling effort (time required) for method (3), and overall sampling effort for method (3) was similar to that required for method (1). Therefore, in terms of both accuracy and sampling effort, method (3) outperformed the other two methods we trialled. An obvious limitation of method (3) when compared to current practice is the requirement for a stereo microscope and access to a laboratory or similar facility. Therefore, the method may be more useful for appropriately trained consultants who monitor pests in orchards on behalf of growers. Another limitation with our study was that it was limited to a single growing season in one pear orchard. We therefore recommend further research to validate our observations, and to support recommendations for growers and consultants.

Emission and co2 sequestration from land use and land cover change according to spatial planning in banyuasin regency, province of south sumatra

The aim of this study was to determine the carbon balance, emission, and CO2 sequestration due to land use and land cover change in Banyuasin Regency from 2004-2014. The methods applied in this study included determination of carbon balance using the matrix of land use change and land cover change, calculation of carbon for each carbon pool by allometric and destructive sampling method, calculation of organic C through the method of Walkley and Black, and method of stock difference for analyzing carbon stock change, CO2 emission and sequestration. Result of this study showed that carbon balance in Banyuasin Regency from 2004-2014 was negative with carbon loss of 22.033.277 tons and average annual carbon loss of 2.203.327 ton. Moreover, CO2 sequestration, emission, and net emission reached 29.298.966 tons, 118.044.141 tons, and 88.745.175 tons, respectively. Average net emission of CO2 from carbon pool of above-ground, necromass, litter, and below-ground was 7 tons ha-1 year-1, while CO2 emission from organic C in soil was 0,61 ton ha-1 year-1. It is expected that changes in peat swamp forest, secondary mangrove forest, primary mangrove forest, and secondary peat swamp forest majorly contributed to CO2 emission in Banyuasin Regency.

Non-invasive Estimation of Foliar Nitrogen Concentration Using Spectral Characteristics of Menthol Mint (Mentha arvensis L.).

Menthol mint (Mentha arvensis L., Family: Lamiaceae), popularly known as corn mint or Japanese mint, is an important industrial crop that is widely grown for its valued essential oil. Nitrogen (N) is an important macro-nutrient and an essential factor for optimizing the yield and quality of crops. Hence, rapid and accurate estimation of the N content is crucial for nutrient diagnosis in plants and to make precise N fertilizer recommendations. Generally, N concentration is estimated by destructive sampling methods; however, an indirect assessment may be possible based on spectral characteristics. This study aimed to compare the foliar N concentration based on non-destructive (reflectance) and destructive (laboratory analyses) methods in menthol mint. Foliar N concentration was measured through the Kjeldahl method and reflectance by Miniature Leaf Spectrometer C-710 (CID Bio-Science). Using reflectance data, several vegetation indices (VIs), that is, normalized difference red edge (NDRE), red edge normalized difference vegetation index (reNDVI), simple ratio (SR), green–red vegetation index (GRVI), canopy chlorophyll content index (CCCI), photochemical reflectance index (PRI), green chlorophyll index (CIGreen), red edge chlorophyll index (CIRed Edge), canopy chlorophyll index (CCI), normalized pigment chlorophyll ratio index (NPCI), and structure insensitive pigment index (SIPI), were developed to determine the foliar N concentration. The highest correlation (r) between VIs and foliar N concentrations was achieved by NDRE (0.89), followed by reNDVI (0.84), SR (0.83), GRVI (0.78), and CCCI (0.76). Among the VIs, the NDRE index has been found to be the most accurate index that can precisely predict the foliar N concentration (R2 = 0.79, RMSE = 0.18). In summary, the N deficiencies faced by the crop during its growth period can be detected effectively by calculating NDRE and reNDVI, which can be used as indicators for recommending precise management strategies for the application of nitrogenous fertilizers.

Applications of a Hyperspectral Imaging System Used to Estimate Wheat Grain Protein: A Review.

Recent research advances in wheat have focused not only on increasing grain yields, but also on establishing higher grain quality. Wheat quality is primarily determined by the grain protein content (GPC) and composition, and both of these are affected by nitrogen (N) levels in the plant as it develops during the growing season. Hyperspectral remote sensing is gradually becoming recognized as an economical alternative to traditional destructive field sampling methods and laboratory testing as a means of determining the N status within wheat. Currently, hyperspectral vegetation indices (VIs) and linear nonparametric regression are the primary tools for monitoring the N status of wheat. Machine learning algorithms have been increasingly applied to model the nonlinear relationship between spectral data and wheat N status. This study is a comprehensive review of available N-related hyperspectral VIs and aims to inform the selection of VIs under field conditions. The combination of feature mining and machine learning algorithms is discussed as an application of hyperspectral imaging systems. We discuss the major challenges and future directions for evaluating and assessing wheat N status. Finally, we suggest that the underlying mechanism of protein formation in wheat grains as determined by using hyperspectral imaging systems needs to be further investigated. This overview provides theoretical and technical support to promote applications of hyperspectral imaging systems in wheat N status assessments; in addition, it can be applied to help monitor and evaluate food and nutrition security.