Destructive Sampling Research Articles

Non-destructive methods applied for in situ mechanical diagnosis: Florentine historical buildings in Pietra Serena

Uniaxial Compressive Strength (UCS) is a key indicator of the mechanical properties of a rock, particularly in the diagnostic and restoration phases when stones are employed as construction material. In historical buildings, it is important to determine UCS values without utilizing tests that involve destructive rock sampling. To estimate UCS, non-destructive techniques, such as ultrasonic pulse velocity (Vp) and Schmidt hammer tests (R), can be combined to provide a useful tool. However, for architectural elements with complex morphology, it is sometimes impossible to perform both ultrasonic and sclerometric test. Three different UCS predictive models, one using R only, one using Vp and one using both, have been developed through regression techniques on a dataset comprising 45 Pietra Serena sandstone samples with varying levels of decay. The models were tested for their performance through Cross-Validation techniques and were then applied to a dataset of Vp and R values measured in situ on Pietra Serena corbels of balconies belonging to historical buildings in Florence (Italy). The specific predictive models were applied to real case studies, and the UCS of the rocks used in historical buildings was calculated, enabling the definition of the mechanical characteristics of building elements belonging to an important cultural context, Florentine historical architecture, without using destructive testing.

In-field assessment of soil pH and mineralization of phosphorus and potassium following the application of composted winery solid waste in sandy loam Ferric Luvisol

Special attention on the plant nutrients mineralization rates is often required when organic fertilizers are used on croplands. This study described the patterns of phosphorus (P) and exchangeable potassium (K) released from winery solid waste (WSW) compost in sandy loam soil. Treatments consisted of equivalent rates of 0, 5, 10, 20 and 40 t ha-1 of compost-soil mixture in Ziplock bags buried on the field at 30 cm soil depth. Destructive sampling of treatments was conducted at 0, 7, 21, 42, 63, 84, 105 and 126 days after incubation (DAI) for laboratory analysis. The 40 t ha-1rate resulted in up to a 9.5% increase in soil pH while the contents of net mineralized P and K measured were significantly affected by compost rate and incubation period interaction. Over the 126 days of the incubation period that runs across summer and winter seasons, mineralized P ranged from -62 to 86 mg kg-1 whereas mineralized K varied between 41 and 2047 mg kg-1. Cumulative mineralized P and K contents ranged from 62 to 207 mg kg-1 and 1272 to 9206 mg kg-1, respectively with the highest amount obtained at the 40 t ha-1 compost rate. The high net P and K mineralized contents suggest that WSW compost may act as a P and K source. However, cautious use of WSW compost as a soil amendment is recommended to mitigate the potential risks of soil pH increases and other unintended consequences such as toxicity, nutrient imbalance, and possible P and K antagonistic effects. Keywords: Compost; Nutrient mineralization; Phyto-toxicity; Soil amendments

Planting hedgerows: Biomass carbon sequestration and contribution towards net-zero targets

Agroforestry practices, such as hedgerow planting, are widely encouraged for climate change mitigation and there is an urgent need to assess their contribution to national ‘net-zero’ targets. This study examined the impact that planting hedgerows at different rates could make to UK net-zero goals over the next 40 years, with a focus on 2050. We analysed the carbon (C) content of native hedgerow species and determined hedge aboveground biomass (AGB) C stock via destructive sampling of hedges of known ages. AGB C stocks ranged from 8.34 Mg C ha−1 in the youngest hedges, to 40.42 Mg C ha−1 in old ones. Knowing the age of the hedgerows, we calculated their annual average AGB C sequestration rate, which was highest in young hedges (2.09 Mg C ha−1 yr−1), and lowest in 39 year old mature, regularly trimmed hedgerows (0.86 Mg C ha−1 yr−1). We present a time series of the annual AGB C sequestration rate change between hedge age categories, which increases from 2.09 Mg C ha−1 yr−1 in the first 6 years after planting, to 2.26 Mg C ha−1 yr−1 in the next 6 years, and then decreases to 0.43 Mg C ha−1 yr−1 between years 13 and 40. Our results indicate that, if encouraged widely, hedgerow planting can be a valuable tool for atmospheric CO2 capture and storage, contributing towards net-zero targets. However, current planting rates (1778.8 km yr−1) are too low to reach the net-zero goal set by the UK Climate Change Committee of increasing hedgerow length by 40 % by 2050. An increased planting rate of 7148.1 km yr−1 will achieve this goal by 2050, and, over 40 years, store 3.41 Tg CO2 in hedge AGB, or 10.13 Tg CO2 in hedge total biomass and in the soil, annually offsetting 1.5 %–4.5 % of UK annual agricultural CO2 emissions.

Watering Rates and Its Response to Cocoa (Theobroma cacao L.) Genotypes Growth Development

Cocoa genotypes exhibiting different responses to water and watering rates to growth parameters necessitate this study. Seedlings were raised from cocoa hybrids genotypes (CRIN Tc1 – 8) and F3 Amazon. The seeds after 4 days of sowing were subjected to 3 watering rates of 50 ml, 100 ml and 150 ml applied once every two days and arranged in a CRD design between December 2020 - May 2021 at Cocoa Research Institute of Nigeria (CRIN) Ibadan Nursery. Data collected include the percentage of germination at 2-4 weeks after sowing (WAS), growth parameter (plant height, girth, leaf area, number of leaves), and destructive sampling (fresh and dry weight of shoot weight, root weight and Taproot length) which were statistically analyzed with SAS tools. The watering rates had no significant effect on cocoa seedling emergence date. Least plant heights were observed when 50ml watering rate was applied on CRINTc-1 (25.00cm), CRINTc-2 (26.53cm), CRINTc-4 (24.10 cm), CRINTc-6 (27.52cm) and CRINTc-8 (28.75cm) at 20WAS. Stem girth, number of leaves and fresh root weight were not significantly enhanced by the watering levels on the cocoa genotypes. The result also reveals that the dry shoot weight was greater than the dry root weight of their corresponding treatments. The 3 watering levels had no considerable effect on the emergence of the cocoa genotypes but influenced the morphological traits, fresh and dry weight of the shoot and root parameters of the cocoa genotypes. 100 ml watering rate applied on the cocoa genotypes performed more appreciably followed by 150 ml.

Study on Early Screening of Ganoderma Disease Susceptibility/Tolerance in Pre-nursery Pot-tray System - Part 1

This paper presents a methodology for early screening of Ganoderma disease susceptibility/tolerance in pre-nursery pot-tray system. The controllable parameters for the screening method were tested in the experiment to optimise the data collected and standardise the methodology prior to broadening the scale of testing. Evaluation of the disease was based on external symptoms and included destructive sampling to assess internal symptoms at the end of the trial. From the initial experiment, the effect of the rubberwood block (RWB) inoculum size was considered to be not significant in relation to the pot-tray size and the estimated medium inoculum size of 4.5 x 3 x 3 cm was selected. Subsequently, during the second experiment, through differentiation of resistance and tolerance as distinctly separate traits, the screening method was able to distinguish highly susceptible and less susceptible progenies. Overall, the pot-tray screening method seems adequate in allowing breeders and researchers to characterise the planting material’s response towards Ganoderma infection. Keywords: Ganoderma disease, basal stem rot, early screening, pre-nursery, pot-tray

Estimation of the biomass and carbon in Cupressus lusitanica Mill. trees in Costa Rica

[Introduction]: Forest plantations are an important carbon sinks and reservoirs while providing other important environmental goods and services. [Objective]: In this research, we developed models to estimate the biomass and carbon content of Cupressus lusitanica Mill trees and its components or fractions, in forest plantations in Costa Rica. [Methodology]: Through the destructive sampling of 43 trees, a sample of each component was obtained to determine dry matter and carbon content. The models were built through linear regression analysis and ordinary least squares, using the normal diameter as the independent variable. Models were selected through the weighted sum of the calculated statistics and the graphical analysis of the residuals. [Results]: The coefficient of determination (R2) was greater than 83.8 % and the estimation error or bias was less than 7.2 %. The leaf and root fractions were more difficult to model, given their lower fit and higher error. The stem represents 61.7 % of total tree biomass, the branches 17.1 % and the roots 9.1 %. The aerial biomass expansion factor was 1.54 (1.3 and 1.24 for branches and foliage) and 1.12 for roots. [Conclusions]: Allometric models accurately predict biomass and carbon content, are easy to use, and become useful low-cost tools to quantify the ecologic and greenhouse gas emission mitigation functions of these forests.

Quantification of Humidity and Salt Detection in Historical Building Materials via Broadband Radar Measurement

For the investigation of moisture and salt content in historic masonry, destructive drilling samples followed by a gravimetric investigation is still the preferred method. In order to prevent the destructive intrusion into the building substance and to enable a large-area measurement, a nondestructive and easy-to-use measuring principle is needed. Previous systems for moisture measurement usually fail due to a strong dependence on contained salts. In this work, a ground penetrating radar (GPR) system was used to determine the frequency-dependent complex permittivity in the range between 1 and 3 GHz on salt-loaded samples of historical building materials. By choosing this frequency range, it was possible to determine the moisture in the samples independently of the salt content. In addition, it was possible to make a quantitative statement about the salt level. The applied method demonstrates that with ground penetrating radar measurements in the frequency range selected here, a salt-independent moisture determination can be carried out.

Automatic Branch-Leaf Segmentation and Leaf Phenotypic Parameter Estimation of Pear Trees Based on Three-Dimensional Point Clouds.

The leaf phenotypic traits of plants have a significant impact on the efficiency of canopy photosynthesis. However, traditional methods such as destructive sampling will hinder the continuous monitoring of plant growth, while manual measurements in the field are both time-consuming and laborious. Nondestructive and accurate measurements of leaf phenotypic parameters can be achieved through the use of 3D canopy models and object segmentation techniques. This paper proposed an automatic branch-leaf segmentation pipeline based on lidar point cloud and conducted the automatic measurement of leaf inclination angle, length, width, and area, using pear canopy as an example. Firstly, a three-dimensional model using a lidar point cloud was established using SCENE software. Next, 305 pear tree branches were manually divided into branch points and leaf points, and 45 branch samples were selected as test data. Leaf points were further marked as 572 leaf instances on these test data. The PointNet++ model was used, with 260 point clouds as training input to carry out semantic segmentation of branches and leaves. Using the leaf point clouds in the test dataset as input, a single leaf instance was extracted by means of a mean shift clustering algorithm. Finally, based on the single leaf point cloud, the leaf inclination angle was calculated by plane fitting, while the leaf length, width, and area were calculated by midrib fitting and triangulation. The semantic segmentation model was tested on 45 branches, with a mean Precisionsem, mean Recallsem, mean F1-score, and mean Intersection over Union (IoU) of branches and leaves of 0.93, 0.94, 0.93, and 0.88, respectively. For single leaf extraction, the Precisionins, Recallins, and mean coverage (mCoV) were 0.89, 0.92, and 0.87, respectively. Using the proposed method, the estimated leaf inclination, length, width, and area of pear leaves showed a high correlation with manual measurements, with correlation coefficients of 0.94 (root mean squared error: 4.44°), 0.94 (root mean squared error: 0.43 cm), 0.91 (root mean squared error: 0.39 cm), and 0.93 (root mean squared error: 5.21 cm2), respectively. These results demonstrate that the method can automatically and accurately measure the phenotypic parameters of pear leaves. This has great significance for monitoring pear tree growth, simulating canopy photosynthesis, and optimizing orchard management.

Distribution and Carbon Sequestration Potential of Cola laurifolia Mast.: A Dominant Native Riparian Species along Permanent Rivers in Sub-Saharan Africa

The continuously increasing interest in carbon market call for adequate approaches to assess and monitor the growth and carbon of tree species. Species-specific models for estimating aboveground biomass (AGB) are the accurate means of quantifying species’ carbon pools. This study aimed at developing allometric equation for Cola laurifolia Mast., a dominant and multi-purpose riparian species along the Mouhoun River in Burkina Faso. The study first used a destructive sampling approach on thirty trees individuals of different diameter classes after collecting their dendrometric data. Explanatory parameters used to build the models were tree diameter at breast height (Dbh), basal diameter at 20 cm (D20), height (H), and mean crown diameter (MCd). Model development involved looking at different forms of models and different compartments of the tree (leaves, branches, stems and total above ground biomass). Subsequently, field inventory data were collected in protected and communal areas along three zones of the Mouhoun river (upstream, midstream and downstream) to assess and compare the carbon stock in the different areas and also characterize their population (assessment of regeneration status of the species). The results showed that the log-log linear model was the best-fitted form for the three tree compartments (i.e., leaf, branch, stem) and the total AGB, and incorporated Dbh and H as predictors. The total AGB model was more accurate with the highest goodness of fit (high R2) low residual standard error (RSE) (R²=0.92; RSE=0.28) as compared to the three component models. Nevertheless, all the allometric equations established for the prediction of leaves, stem, branches and total aboveground biomass were statically significant (p≤0.0001). The study also showed that the population structure of the species reflects a low regeneration potential along the studied river zones (i.e., upstream, intermediate and downstream zones), calling for initiatives to address the issue. The carbon stock was found to be 56.40 kg C tree-1 and 9.24 Mg C. ha-1. The density of C. laurifolia was higher in downstream zone, and consequently the carbon stock was higher in these areas. The study also compared the outputs from existing generalized allometric models to our newly developed specific-equation and found that they overestimate or underestimate the carbon stock of C. laurifolia. The results confirm the value of species-specific model which therefore calls for more effort to develop such models for all dominant species for greater accuracy in AGB estimations at scale.

Bacterial transfer and biofilm formation in needleless connectors in a clinically simulated in vitro catheter model.

Although needleless connectors (NCs) are widely used in clinical practice, they carry significant risk of bloodstream infection (BSI). In this study, we quantified differences in bacterial transfer and biofilm formation between various NCs. Prospective, clinically simulated in vitro experimental study. We tested 20 NCs in a 5-day clinical simulation of Staphylococcus aureus inoculations onto NC septum surfaces, which were then flushed with saline and cultured for bacterial transfer. Biofilm formation was measured through destructive sampling of the connector-catheter system. Moreover, 8 NC design factors were evaluated for their influence on bacterial transfer and biofilm formation. This study was designed without a disinfection protocol to ascertain the intrinsic risk of each NC. Clave Neutron and MicroClave had the lowest overall mean log density of bacteria in the flush compared to other NCs (P < .05), except there were no statistically significant differences between Clave Neutron, Microclave, SafeTouch, and SafeAccess (P ≥ .05). The amount of biofilm in the NC was positively associated with bacteria in the flush (P < .0005). Among 8 design factors, flow path was most important, with the internal cannula associated with a statistically significant 1 log reduction (LR) in bacteria in the flush (R2 = 49%) and 0.5-2 LR in the connector (R2 = 34%). All factors together best explained bacteria in the flush (R2 = 65%) and biofilm in the connector (R2 = 48%). Bacterial transfer and biofilm formation in the connector-catheter system varied statistically significantly between the 20 NCs, suggesting that NC choice can lower the risk of developing catheter-related BSIs.

Baseline corticosterone levels in spadefoot toads reflect alternate larval diets one year later

Early-life environmental variation can influence later-life physiology, such as the regulation of glucocorticoids. However, characterizing the effects of environmental factors on hormone regulation can be hampered when assessing animals that are small and require destructive sampling to collect blood. Using spadefoot toads (genus Spea), we evaluated whether waterborne corticosterone (CORT) measures could be used as a proxy for plasma CORT measures, detect stress-induced levels of CORT, and detect larval diet-induced changes in CORT regulation after metamorphosed individuals were maintained for 1 year under common garden conditions. We found that waterborne CORT measures were correlated with plasma CORT measures and could be used to detect stress-induced levels of CORT. Further, larval diet type significantly influenced baseline plasma CORT levels 1-year post-metamorphosis: adults that had consumed live prey as larvae had higher plasma CORT levels than adults that had consumed detritus as larvae. However, waterborne measures failed to reflect these differences, possibly due to low sample size. Our study demonstrates the utility of the waterborne hormone assay in assessing variation in baseline and stress-induced CORT levels in adult spadefoots. However, resolving more subtle differences that arise through developmental plasticity will require larger samples sizes when using the waterborne assay.

A simple concept for estimating deadwood carbon in forests

Deadwood in forests is becoming an important topic globally for forest management and carbon markets. While deadwood volume is now routinely assessed by forest inventories and can be even monitored using remote sensing, data about deadwood carbon and density depending on the extent of decay is scarce. We propose a simple concept for estimating deadwood carbon using data from a pilot study in selected coniferous and broadleaf forests covering different climate zones in Austria. We measured deadwood volume and decay classes and calculated deadwood density and carbon fraction from destructive field samples. We found that decay reduces deadwood density significantly, while carbon fraction increases moderately with decay. Partitioning the explained variation confirmed that size and site parameters are more important for explaining deadwood carbon fraction than for deadwood density. These patterns were consistent for coniferous and broadleaf tree species. We also show how to estimate deadwood carbon stocks using standard forest inventory measurements or country statistics, without information on decay classes. We found differences in decay classes of standing versus lying deadwood, and our proposed method allows considering this in large-scale deadwood carbon assessments. We highlight research gaps and missing deadwood information, to provide more accurate carbon assessments of deadwood.

Forage quantity and protein concentration changes across a forest-savanna gradient with management implications for white-tailed deer

White-tailed deer (Odocoileus virginianus) hunting is an important economic activity associated with the management of forests and rangelands in the USA, with over $12.9 billion dollars of related annual expenditures. Reducing tree cover through thinning and prescribed fire both have the potential to increase the quantity and quality of deer forage. We evaluated the long-term impacts of eight different combinations of fire return intervals and tree harvest on forage productivity and protein content of the forage. Based on management regime, study units ranged from savanna to closed-canopy forest. Aboveground net primary production (ANPP) of six functional groups (grass, panicum, forb, legume, woody, sedge) of understory vegetation was measured in October 2019 and 2020 using destructive sampling. Samples for foliar crude protein (CP) concentration were collected in spring, summer, and fall of 2020. Total understory ANPP ranged from 2.9 to 466.3 g m−2 and was up to 566% greater in savanna systems maintained by frequent fire (return interval of three years or less) than in non-burned forest treatments. Annual burning resulted in ANPP dominated by herbaceous plants composed mostly of fire-tolerant grasses (e.g., Andropogon gerardii, Schizachyrium scoparium). Longer fire return intervals or no fire resulted in roughly equal ANPP from understory woody and herbaceous species. Crude protein concentrations were up to 45.7% greater in the woodland and forest units than in the savanna units for seven of the eleven species sampled. The greater CP in the forests was most noticeable in the summer when deer needs for quality forage are substantial. Increased protein concentrations of understory species in the forests, but greater ANPP in the savannas indicate that managing for a mix of savanna and woodland could be ideal for balancing forage quantity with increased forage protein.

Allometric equation for Raphia laurentii De Wild, the commonest palm in the central Congo peatlands.

The world's largest tropical peatland lies in the central Congo Basin. Raphia laurentii De Wild, the most abundant palm in these peatlands, forms dominant to mono-dominant stands across approximately 45% of the peatland area. R. laurentii is a trunkless palm with fronds up to 20 m long. Owing to its morphology, there is currently no allometric equation which can be applied to R. laurentii. Therefore it is currently excluded from aboveground biomass (AGB) estimates for the Congo Basin peatlands. Here we develop allometric equations for R. laurentii, by destructively sampling 90 individuals in a peat swamp forest, in the Republic of the Congo. Prior to destructive sampling, stem base diameter, petiole mean diameter, the sum of petiole diameters, total palm height, and number of palm fronds were measured. After destructive sampling, each individual was separated into stem, sheath, petiole, rachis, and leaflet categories, then dried and weighed. We found that palm fronds represented at least 77% of the total AGB in R. laurentii and that the sum of petiole diameters was the best single predictor variable of AGB. The best overall allometric equation, however, combined the sum of petiole diameters (SDp), total palm height (H), and tissue density (TD): AGB = Exp(-2.691 + 1.425 × ln(SDp) + 0.695 × ln(H) + 0.395 × ln(TD)). We applied one of our allometric equations to data from two nearby 1-hectare forest plots, one dominated by R. laurentii, where R. laurentii accounted for 41% of the total forest AGB (with hardwood tree AGB estimated using the Chave et al. 2014 allometric equation), and one dominated by hardwood species, where R. laurentii accounted for 8% of total AGB. Across the entire region we estimate that R. laurentii stores around 2 million tonnes of carbon aboveground. The inclusion of R. laurentii in AGB estimates, will drastically improve overall AGB, and therefore carbon stock estimates for the Congo Basin peatlands.

Imaging Sensor-Based High-Throughput Measurement of Biomass Using Machine Learning Models in Rice

Phenomics technologies have advanced rapidly in the recent past for precision phenotyping of diverse crop plants. High-throughput phenotyping using imaging sensors has been proven to fetch more informative data from a large population of genotypes than the traditional destructive phenotyping methodologies. It provides accurate, high-dimensional phenome-wide big data at an ultra-super spatial and temporal resolution. Biomass is an important plant phenotypic trait that can reflect the agronomic performance of crop plants in terms of growth and yield. Several image-derived features such as area, projected shoot area, projected shoot area with height constant, estimated bio-volume, etc., and machine learning models (single or multivariate analysis) are reported in the literature for use in the non-invasive prediction of biomass in diverse crop plants. However, no studies have reported the best suitable image-derived features for accurate biomass prediction, particularly for fully grown rice plants (70DAS). In this present study, we analyzed a subset of rice recombinant inbred lines (RILs) which were developed from a cross between rice varieties BVD109 × IR20 and grown in sufficient (control) and deficient soil nitrogen (N stress) conditions. Images of plants were acquired using three different sensors (RGB, IR, and NIR) just before destructive plant sampling for the quantitative estimation of fresh (FW) and dry weight (DW). A total of 67 image-derived traits were extracted and classified into four groups, viz., geometric-, color-, IR- and NIR-related traits. We identified a multimodal trait feature, the ratio of PSA and NIR grey intensity as estimated from RGB and NIR sensors, as a novel trait for predicting biomass in rice. Among the 16 machine learning models tested for predicting biomass, the Bayesian regularized neural network (BRNN) model showed the maximum predictive power (R2 = 0.96 and 0.95 for FW and DW of biomass, respectively) with the lowest prediction error (RMSE and bias value) in both control and N stress environments. Thus, biomass can be accurately predicted by measuring novel image-based parameters and neural network-based machine learning models in rice.

A novel transfer learning framework for sorghum biomass prediction using UAV-based remote sensing data and genetic markers.

Yield for biofuel crops is measured in terms of biomass, so measurements throughout the growing season are crucial in breeding programs, yet traditionally time- and labor-consuming since they involve destructive sampling. Modern remote sensing platforms, such as unmanned aerial vehicles (UAVs), can carry multiple sensors and collect numerous phenotypic traits with efficient, non-invasive field surveys. However, modeling the complex relationships between the observed phenotypic traits and biomass remains a challenging task, as the ground reference data are very limited for each genotype in the breeding experiment. In this study, a Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) model is proposed for sorghum biomass prediction. The architecture is designed to exploit the time series remote sensing and weather data, as well as static genotypic information. As a large number of features have been derived from the remote sensing data, feature importance analysis is conducted to identify and remove redundant features. A strategy to extract representative information from high-dimensional genetic markers is proposed. To enhance generalization and minimize the need for ground reference data, transfer learning strategies are proposed for selecting the most informative training samples from the target domain. Consequently, a pre-trained model can be refined with limited training samples. Field experiments were conducted over a sorghum breeding trial planted in multiple years with more than 600 testcross hybrids. The results show that the proposed LSTM-based RNN model can achieve high accuracies for single year prediction. Further, with the proposed transfer learning strategies, a pre-trained model can be refined with limited training samples from the target domain and predict biomass with an accuracy comparable to that from a trained-from-scratch model for both multiple experiments within a given year and across multiple years.

Evaluation of product bioequivalence when subject blood volume necessitates limited sampling strategies.

In a traditional blood level bioequivalence (BE) study, every subject provides drug concentrations at each blood sampling time. However, this approach is not suitable for animals whose blood volume limits or prohibits multiple sample collections. In our previous research, we presented an approach that can be applied to studies using a destructive sampling design where each animal provides only 1 blood sample that is then incorporated into a composite profile. Another situation we sometimes face is that of when the animals can contribute more than one sample but are still limited in the number of blood draws (e.g., 3) such that a complete profile per animal is not feasible. Unlike the destructive sampling situation, we cannot combine all blood samples into a single "composite" profile and ignore the correlation of values obtained from the same subject. To avoid the complexities associated with needing to include a covariance component among experimental units into the statistical model, we propose an approach whereby study subjects are randomly assigned to housing unit (e.g., cage or pen) and then randomly assigned to a sampling schedule within each housing unit. In doing so, housing unit rather than the individual subject serves as the experimental unit. This article provides an assessment of this alternative approach to assess product BE when only a limited number of samples can be obtained per study subject.

Nondestructive Evaluation of Cotton Genotypes for Resistance to Reniform Nematode

Identification of new sources of reniform nematode (Rotylenchulus reniformis Linford and Oliveira) resistance in cotton is critical to expanding host plant resistance to manage this important pathogen. Phenotyping plants in early breeding generations without destructive sampling would be useful for introgression of nematode resistance from exotic germplasm resources; therefore, a rapid, nondestructive method was developed to assess host plant resistance to the reniform nematode based on the number of females infecting the roots. In one set of experiments, the root system was cut off at 0, 1, 2.5, or 5 cm below the soil line and used to assess the number of females infecting this portion of the root system. Resistance could be accurately determined while leaving up to 5 cm of roots with the shoot. In a second set of experiments, the rate of plant recovery and reproductive development was evaluated using a combination of root retention (0, 1, 2.5 cm, or all root) and shoot retention (leaves at top two nodes, leaves at bottom two nodes, no leaves, all leaves) treatments. Plants more rapidly recovered using a treatment combination in which the top leaves and 2.5 cm roots were kept. This combination performed similarly to plants with neither shoots nor roots modified and was harvested 20 days sooner compared to some other treatment combinations.

Establishing the Baltic origins of archaeological amber beads from Dura-Europos (Syria) using non-destructive DRIFTS

Sourcing archaeological amber has hitherto been limited by a reliance on chemical techniques that require some degree of destructive sampling. The majority of amber artifacts are friable, weakened after millennia spent unprotected from environmental stressors, and as such are especially vulnerable during analysis or even sampling. Here, we assess the capability of an entirely nondestructive analytical technique — Diffuse Reflectance Infrared Spectroscopy (DRIFTS) — to scientifically identify archaeological Baltic amber. Using a reference collection of geological amber specimens, our direct comparison of DRIFTS spectra to those collected via conventional transmission FTIR, which necessitates destructive sampling, demonstrates the validity of the new technique. Thirteen beads from the archaeological site of Dura-Europos in Syria were subsequently analyzed using DRIFTS alone. Five of the beads showed spectral features indicative of Baltic amber, whereas the others were too degraded to yield diagnostically measurable spectra. Our study thus confirms the capability of the DRIFTS technique to analyze whole, untreated amber artifacts when their integrity is of high concern. Furthermore, our results indicate a previously unestablished connection between Dura-Europos and northern Europe during the first two centuries CE through long-distance exchange networks stemming from the Mediterranean basin.

Anaerobic Composting of Farmyard Manure as Affected by Chicken Manure, Urea and Waste Water

the objectives of this study were to investigate the stage ofmaturity and to determine nutrient contents of farmyard manure (FYM)composted with 2% chicken manure (CHM), 1% urea, and 25% wastewater, during the summer seasons of 1997 and 1998. The treatments werereplicated thrice in a completely randomized design. The total number ofexperimental units was repeated eight times for destructive sampling at 15days intervals for a period of four months. The treatments were buried inauger holes (90-cm depth) and samples were collected and chemicallyanalysed. The analysis included crude fibre, cellulose, starch, calcium,magnesium, sodium, nitrogen, phosphorus and potassium contents. Thestage of maturity of FYM was measured by the biodegradation of crudefibre, cellulose and starch contents which decreased significantly (P≤0.005). The rate of crude fibre decomposition was highest in ureafollowed by CHM and waste water. The rate of cellulose decompositionof both urea and waste water treatments was significantly higher than theCHM and control treatments. Starch disappeared in the 12th week in CHMtreatment and in the 14th week in waste water treatment. CHM treatmentwas superior in calcium release followed by waste water and ureatreatments. The lowest sodium was detected in waste water treatmentfollowed by both CHM and urea. Waste water, however, showed thehighest nitrogen and phosphorus contents compared to the othertreatments. Potassium content was the highest in CHM followed by ureaand waste water. In conclusion, the best compost ripening time was afterthree months when its nutritive value was the highest and the physicalproperties were best