Destructive Sampling Research Articles

Combining canopy spectral reflectance and RGB images to estimate leaf chlorophyll content and grain yield in rice

Predicting rice grain yield using multi-source remotely sensed data is crucial for improving prediction accuracy, optimizing nitrogen management, and advancing precision agricultural development. However, the feasibility and reliability of using multi-source remotely sensed data to predict the grain yield remain unclear. Therefore, this study aimed to explore the possibility of providing rice leaf chlorophyll content (LCC) estimations and predictions of the grain yield using multi-source remotely sensed data. Two rice field experiments were conducted with various rice cultivars and nitrogen rates, and a field spectrometer and an unmanned aerial vehicle (UAV) equipped with a digital camera were employed to acquire the spectral reflectance and red–green–blue (RGB) images at the tillering, jointing, and full-heading stages. Destructive sampling was then conducted to measure the LCC and grain yield. The LCC was used as a bridge to develop remotely sensed prediction models for the grain yield. First, the linear relationship between grain yield and the LCC was determined at the tillering, jointing, and full-heading stages. A multiple linear regression (MLR) model was then developed to predict grain yield using multi-temporal LCCs at three growth stages. Second, multiple stepwise regression, support vector regression, and back propagation neural network were used to evaluate the estimation performance of spectral reflectance, RGB image data, and their combination for LCC. Third, the most accurate LCC estimation model was selected and coupled with the linear and MLR models of grain yield. The results showed that grain yield was significantly and positively related to the LCC at the tillering, jointing, and full-heading stages, and that the MLR model of grain yield achieved the best estimation accuracy using multi-temporal LCCs. The fusion models established by combining spectral reflectance and RGB image data improved LCC estimation accuracies. Using multi-growth stages, the most accurate predictions of grain yield were obtained from LCC estimation models (R2 = 0.698, RMSE = 0.742 t ha−1, rRMSE = 9.004 %) compared to those using single growth stages. Our study concluded that multi-source remotely sensed data fused from ground-based spectral reference and UAV-based RGB images can better predict and explain the grain yield for both single and multi-growth stages. This study provides a novel method of estimating the crop chlorophyll content and grain yield.

Spatial and agronomic stage distribution dynamics of maize stem borers (Lepidoptera) in the Agricultural development Zone B of Niger State, Nigeria

Maize farmers in Nigeria have been facing challenges as efforts towards the cultivation of maize is seriously hampered by insect pest infestation in maize fields, of which stem borers are a major culprit. The study was carried out to identify the species of stem borers present and their abundance in Agricultural development Zone B of Niger State. Farm sites located in Beji, Chanchaga, Paiko and Shanu, were selected and sampled for stem borers, during vegetative, reproductive and harvest stages of the field maize crops. Identification was done using standard taxonomic keys. The results indicated the presence of three species of stem borer belonging to three families. A species each from Noctuidae, Crambidae and Pyralidae consisting of Busseola fusca (n = 1639, 68.24%), Chilo partellus (n = 160, 6.66%) and Eldana saccharina (n = 603, 25.10%) respectively. Generally, all sites had two species of the stem borer (B. fusca and E. saccharina) present in all growth stages of the maize plant during the destructive sampling with the exception of Chilo partellus that was rarely found; occurring only in one site (Beji), all amounting to a total of 2402 stem borers collected during the study period. Among all the growth stages of the maize plants in all sampled sites the level of infestation varied significantly (p< 0.05), in an increasing order: vegetative stage < reproductive stage < harvest stage of the maize plants. The findings of this study suggest that there is a serious stem borer pest infestation in the maize farms of Agricultural development Zone B of Niger state that could pose threats to maize (Zea mays) production in the area; thus, requiring urgent interventions on the parts of all stakeholders.

Fast detection of moisture content and freshness for loquats using optical fiber spectroscopy.

Detection of the moisture content (MC) and freshness for loquats is crucial for achieving optimal taste and economic efficiency. Traditional methods for evaluating the MC and freshness of loquats have disadvantages such as destructive sampling and time-consuming. To investigate the feasibility of rapid and non-destructive detection of the MC and freshness for loquats, optical fiber spectroscopy in the range of 200-1000 nm was used in this study. The full spectra were pre-processed using standard normal variate method, and then, the effective wavelengths were selected using competitive adaptive weighting sampling (CARS) and random frog algorithms. Based on the selected effective wavelengths, prediction models for MC were developed using partial least squares regression (PLSR), multiple linear regression, extreme learning machine, and back-propagation neural network. Furthermore, freshness level discrimination models were established using simplified k nearest neighbor, support vector machine (SVM), and partial least squares discriminant analysis. Regarding the prediction models, the CARS-PLSR model performed relatively better than the other models for predicting the MC, with R 2 P and RPD values of 0.84 and 2.51, respectively. Additionally, the CARS-SVM model obtained superior discrimination performance, with 100% accuracy for both calibration and prediction sets. The results demonstrated that optical fiber spectroscopy technology is an effective tool to fast detect the MC and freshness for loquats.

Site Quality Models and Fuel Load Dynamic Equation Systems Disaggregated by Size Fractions and Vegetative States in Gorse and High Heath Shrublands in Galicia (NW Spain)

Compatible model systems were developed for estimating fuel load dynamics in Ulex europaeus (gorse) and in Erica australis (Spanish heath) dominated shrub communities at stand level. The models were based on intensive, detailed destructive field sampling and were fitted simultaneously to fulfill the additivity principle. The models enable, for the first time, estimation of the biomass dynamics of the total shrub layer, size fractions and vegetative stage, with reasonably good accuracy. The approach used addresses the high variability in shrub biomass estimates by using a site index (SI) based on biomass levels at a reference age of 10 years. Analysis of the effect of climatic variables on site index confirmed the preference of gorse for mild temperatures and the ability of high heath communities to tolerate a wider range of temperatures. In the gorse communities, SI tended to increase as summer rainfall and the mean temperature of the coldest month increased. However, in the heath communities, no relationships were observed between SI and any of the climatic variables analyzed. The study findings may be useful for assessing and monitoring fuel hazards, updating fuel mapping, planning and implementing fuel reduction treatments and predicting fire behavior, among other important ecological and biomass use-related applications.

Root growth dynamics and biomass input of four over-wintering herbaceous crops in boreal conditions

Root growth of winter wheat (Triticum aestivum L.), winter rye (Secale cereale L.), red clover (Trifolium pratense L.), and timothy (Phleum pratense L.) was recorded to evaluate the environmental potential of over-wintering crops in a Nordic agroecosystem. In a field experiment on Aquic Haplocryoll soil, root intensities (number of roots/area) were measured to 50 cm depth by minirhizotron microvideo camera technology over a two -year period (21 recording sessions). At anthesis, root biomass and morphological parameters were measured by destructive soil sampling and image analysis of washed roots. Winter cereal roots reached 50 cm depth as soon as the autumn of the seeding year. For both post-seeding years, timothy roots developed the most intensively in spring, while red clover had higher root intensity than timothy in late autumn. At anthesis, the crops were ranked timothy > red clover > winter wheat > winter rye according to root length density, surface area density, and biomass. Based on S:R ratios, red clover appears to offer the most intense carbon sink at 0–60 cm soil depth. Over-wintering crops had living roots in the subsoil both in late autumn and early spring, indicating potential to plant available nutrient uptake outside the growing season of annual crops.

Reliability of Noninvasive Sonic Tomography for Detection and Quantification of Internal Defects in Yoshino Cherry

Abstract Preventive maintenance through monitoring and early detection is important for trees that are structurally weak due to internal decay. Once internal decay has occurred, recovery of lost strength is irreversible. The decaying tree becomes vulnerable to external impacts, which may lead to breakage or windthrow. The Yoshino cherry (Cerasus×yedoensis (Matsum.) “Somei-yoshino”) accounts for a high proportion of landscape trees in Asia. Testing the reliability of noninvasive sonic tomography (SoT) to estimate internal defects could facilitate structural stability evaluation and proactive preventive management. We performed noninvasive SoT and invasive resistance microdrilling to detect and quantify internal defects in old and large Yoshino cherry trees. Generally, SoT reflected the areas and location of severe structural defects, showing a correlation with the resistance microdrilling results (R² = 0.542, p < .001). However, when cracks were present in disks, the SoT overestimated defects and errors mainly observed in the transition zone (green). Based on these findings, when evaluating the SoT, careful decisions are needed to distinguish between actual defects and broad sonic shadows. Furthermore, additional evidence from drilling resistance is required to differentiate between incipient decay and cracks. The results of this study provide valuable insights for enhancing the interpretation of sonic tomograms. Study Implications: We suggest defect boundaries in sonic tomograms should be redefined to improve the accuracy of interpretation for any new species being studied. Careful drilling resistance measurements should be taken, especially in transition zones (green areas in tomogram) and incipient decay zone (light brown areas). Noninvasive sonic tomograms only reflect acoustic properties of the tested cross-section of a tree and cannot be considered an actual representation of the internal conditions until the readings are calibrated to destructive sampling results.

Assessing Carbon Dioxide (CO2) Absorption Potential of Forests Around Landslides Along the Trans Palopo-Toraja Highways

Forests are crucial in absorbing carbon dioxide (CO2) from the atmosphere through photosynthesis and storing the carbon in its biomass. This research aimed to assess the potential for CO2 absorption of forests around landslides along the Trans Palopo-Toraja highways. This research employed a destructive sampling to measure understorey and litter biomass. Furthermore, it measured the biomass of understorey plants and litter with a destructive method. The 12 measurement plots were purposively placed on the landslide points to represent various vegetation conditions. The results revealed the presence of 77 plant species, totaling 554 individuals on the measurement plots. Plot 4 and Plot 1 showed the highest and lowest carbon absorption potential at 790.39 tons/ha and 199.63 tons/ha, respectively. The estimated average absorption of 12 plots was 461.75 tons/ha. Tree-level vegetation had the highest carbon absorption value due to its bigger diameter and CO2 absorption capacity than saplings and poles.

Are high-throughput root phenotyping platforms suitable for informing root system architecture models with genotype-specific parameters? An evaluation based on the root model ArchiSimple and a small panel of wheat cultivars.

Given the difficulties in accessing plant roots in situ, high-throughput root phenotyping (HTRP) platforms under controlled conditions have been developed to meet the growing demand for characterizing root system architecture (RSA) for genetic analyses. However, a proper evaluation of their capacity to provide the same estimates for strictly identical root traits across platforms has never been achieved. In this study, we performed such an evaluation based on six major parameters of the RSA model ArchiSimple, using a diversity panel of 14 bread wheat cultivars in two HTRP platforms that had different growth media and non-destructive imaging systems together with a conventional set-up that had a solid growth medium and destructive sampling. Significant effects of the experimental set-up were found for all the parameters and no significant correlations across the diversity panel among the three set-ups could be detected. Differences in temperature, irradiance, and/or the medium in which the plants were growing might partly explain both the differences in the parameter values across the experiments as well as the genotype × set-up interactions. Furthermore, the values and the rankings across genotypes of only a subset of parameters were conserved between contrasting growth stages. As the parameters chosen for our analysis are root traits that have strong impacts on RSA and are close to parameters used in a majority of RSA models, our results highlight the need to carefully consider both developmental and environmental drivers in root phenomics studies.

Relationship between chloride ingress and concrete cover quality of inland structures exposed to deicing salts

The apparent diffusion coefficient of chloride ions is used to evaluate the durability of existing concrete structures against chloride attack. The coefficient requires in situ measurements of chloride profiles, which use destructive sampling techniques, such as core drilling. In this study, the relationship between the apparent diffusion coefficient of chloride ions obtained from destructive tests and the air-permeability coefficient obtained from a non-nodestructive Torrent method in the existing road bridges exposed to deicing salts was analyzed. The obtained relationship was different from the common trends obtained from laboratory chloride-immersion tests and in situ measurements in marine/coastal concrete structures. Additional cyclic wetting and drying tests in a laboratory revealed the effects of environmental differences on the apparent diffusion coefficient of chloride ions. A higher ratio of the drying period, in the case of deicing salts, reduced the apparent diffusion coefficient.

The development of non-destructive sampling methods of parchment skins for genetic species identification.

Parchment, the skins of animals prepared for use as writing surfaces, offers a valuable source of genetic information. Many have clearly defined provenance, allowing for the genetic findings to be evaluated in temporal and spatial context. While these documents can yield evidence of the animal sources, the DNA contained within these aged skins is often damaged and fragmented. Previously, genetic studies targeting parchment have used destructive sampling techniques and so the development and validation of non-destructive sampling methods would expand opportunities and facilitate testing of more precious documents, especially those with historical significance. Here we present genetic data obtained by non-destructive sampling of eight parchments spanning the 15th century to the modern day. We define a workflow for enriching the mitochondrial genome (mtGenome), generating next-generation sequencing reads to permit species identification, and providing interpretation guidance. Using sample replication, comparisons to destructively sampled controls, and by establishing authentication criteria, we were able to confidently assign full/near full mtGenome sequences to 56.3% of non-destructively sampled parchments, each with greater than 90% of the mtGenome reference covered. Six of eight parchments passed all four established thresholds with at least one non-destructive sample, highlighting promise for future studies.

Improvement of pasture biomass modelling using high-resolution satellite imagery and machine learning

Robust quantification of vegetative biomass using satellite imagery using one or more forms of machine learning (ML) has hitherto been hindered by the extent and quality of training data. Here, we showcase how ML predictive models demonstrably improve when additional training data is used. We collated field datasets of pasture biomass obtained via destructive sampling, ‘C-Dax’ reflective measurements and rising plate meters (RPM) from ten livestock farms across four States in Australia. Remotely sensed data from the Sentinel-2 constellation was used to retrieve aboveground biomass using a novel machine learning paradigm hereafter termed “SPECTRA-FOR” (Spectral Pasture Estimation using Combined Techniques of Random-forest Algorithm for Features Optimisation and Retrieval). Using this framework, we show that the low temporal resolution of Sentinel-2 in high latitude regions with persistent cloud cover leads to extensive gaps between cloud-free images, hindering model performance and, thus, contemporaneous ability to forecast real-time pasture biomass. By leveraging the spectral consistency between Sentinel-2 and Planet Lab SuperDove to overcome this limitation, we used ten spectral bands of Sentinel-2, four bands of Sentinel-2 as a proxy for pre-2022 SuperDove (referred to as synthetic SuperDove or SSD), and the actual SuperDove (ASD), given that SuperDove imagery has a higher resolution and more frequent passage compared with Sentinel-2. Using their respective bands as input features to SPECRA-FOR, model performance for the ten bands of Sentinel-2 were R2 = 0.87, root mean squared error (RMSE) of 439 kg DM/ha and mean absolute error (MAE) of 255 kg DM/ha, while that for SSD increased to an R2 of 0.92, RMSE of 346 kg DM/ha and MAE = 208 kg DM/ha. The study revealed the importance of robust data mining, imagery harmonisation and model validation for accurate real-time modelling of pasture biomass with ML.

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.

Surveillance of pathogenic bacteria on a food matrix using machine-learning-enabled paper chromogenic arrays

Global food systems can benefit significantly from continuous monitoring of microbial food safety, a task for which tedious operations, destructive sampling, and the inability to monitor multiple pathogens remain challenging. This study reports significant improvements to a paper chromogenic array sensor - machine learning (PCA-ML) methodology sensing concentrations of volatile organic compounds (VOCs) emitted on a species-specific basis by pathogens by streamlining dye selection, sensor fabrication, database construction, and machine learning and validation. This approach enables noncontact, time-dependent, simultaneous monitoring of multiple pathogens (Listeria monocytogenes, Salmonella, and E. coli O157:H7) at levels as low as 1 log CFU/g with over 90% accuracy. The report provides theoretical and practical frameworks demonstrating that chromogenic response, including limits of detection, depends on time integrals of VOC concentrations. The paper also discusses the potential for implementing PCA-ML in the food supply chain for different food matrices and pathogens, with species- and strain-specific identification.

Assessing the Contributions of Nypa fruticans wumb in Climate Change Mitigation by Accounting for the Above Ground Carbon Stock within the Douala-Edea National Park, Littoral Region, Cameroon

Aim: Nypa fruticans Wurmb is a palm species found in mangrove ecosystems with moderate salt load which circumscribes well to the actual areas of its occurrence in the gradient from seawater habitats to inland sites. This study aimed at carrying out a baseline assessment of above ground carbon stock in stands of Nypa fruticans wurmb.
 Methods: Representative samples of seedlings, juveniles, adults and mature stands were harvested through a destructive sampling technique. These samples were air dried and further oven dried in the laboratory at 105°C until a constant mass was attained. The biomass was then converted to per carbon stock using the published carbon concentration of 0.5 for above-ground biomass.
 Results: Nypa fruticans wurmb has an average estimated carbon per mature frond of 0.77 kg, followed by the adult fronts with average estimated carbon per frond of 0.24 kg, the juvenile records an average carbon per juvenile frond of 0.11 kg and the seedlings with average estimated carbon per seedling frond of 0.02 kg.
 Conclusion: Nypa fruticans wurmb have a relatively small amount of biomass and carbon stock as compared to other woody mangrove species. 
 Recommendations: It is recommended that, carbon in the roots of this palm should be evaluated to complete the required information on this subject. The spread of this palm should be checked due to its negative impact on native mangroves and the indirect adverse effects it poses to climate change mitigation.

Compatibility of the fungus Beauveria bassiana and Trichoplusia ni SNPV against the cabbage looper Trichoplusia ni: crop plant matters.

Microbial insecticides are an important weapon in insect pest management, but their use is still relatively limited. One approach for increasing their efficacy and use could be to combine different pathogens to increase pest mortality. However, little is known about whether increasing pathogen diversity will improve pest management. Here, we investigated the compatibility of two pathogens for the management of the cabbage looper, Trichoplusia ni, T. ni nucleopolyhedrovirus (TniSNPV) and the entomopathogenic fungus Beauveria bassiana, on two crops, tomato and broccoli. The pathogens were applied to individual plants using ultra low volume sprays, alone or in combination, either synchronously or asynchronously. Healthy third-instar T. ni larvae were introduced to the plants before application and collected by destructive sampling 24 h after the last pathogen application. Combined applications did not result in an increase in larval mortality compared to TniSNPV alone, although mortality was generally high. B. bassiana was considerably less effective on broccoli compared to tomato. In both the combined treatments, virus-induced mortality was approximately 50% lower when applied together with the fungus, while fungus-induced mortality was not affected by the virus, even when the virus was introduced 24 h before the fungus. While our results suggest that applying this combination of entomopathogens would not be beneficial for pest management, this study illustrates the need to consider the target crop as an important driver of the efficacy of both single and mixed pathogen applications in the field. © 2024 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

AgriCarbon-EO v1.0.1: large-scale and high-resolution simulation of carbon fluxes by assimilation of Sentinel-2 and Landsat-8 reflectances using a Bayesian approach

Abstract. Soil organic carbon storage is a well-identified climate change mitigation solution. Quantification of the soil carbon storage in cropland for agricultural policy and offset carbon markets using in situ sampling would be excessively costly, especially at the intrafield scale. For this reason, comprehensive monitoring, reporting, and verification (MRV) of soil carbon and its explanatory variables at a large scale need to rely on hybrid approaches that combine remote sensing and modelling tools to provide the carbon budget components with their associated uncertainties at intrafield scale. Here, we present AgriCarbon-EO v1.0.1: an end-to-end processing chain that enables the estimation of carbon budget components for major and cover crops at intrafield resolution (10 m) and regional extents (e.g. 10 000 km2) by assimilating remote sensing data (e.g. Sentinel-2 and Landsat8) in a physically based radiative transfer (PROSAIL) and agronomic models (SAFYE-CO2). The data assimilation in AgriCarbon-EO is based on a novel Bayesian approach that combines normalized importance sampling and look-up table generation. This approach propagates the uncertainties across the processing chain from the reflectances to the output variables. After a presentation of the chain, we demonstrate the accuracy of the estimates of AgriCarbon-EO through an application over winter wheat in the southwest of France during the cropping seasons from 2017 to 2019. We validate the outputs with flux tower data for net ecosystem exchange, biomass destructive samples, and combined harvester yield maps. Our results show that the scalability and uncertainty estimates proposed by the approach do not hinder the accuracy of the estimates (net ecosystem exchange, NEE: RMSE =1.68–2.38 gC m−2, R2=0.87–0.77; biomass: RMSE =11.34 g m−2, R2=0.94). We also show the added value of intrafield simulations for the carbon components through scenario testing of pixel and field simulations (biomass: bias =-47 g m−2, −39 % variability). Our overall analysis shows satisfying accuracy, but it also points out the need to represent more soil processes and include synthetic aperture radar data that would enable a larger coverage of AgriCarbon-EO. The paper's findings confirm the suitability of the choices made in building AgriCarbon-EO as a hybrid solution for an MRV scheme to diagnose agro-ecosystem carbon fluxes.

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.

“APPROXIMATE” WIGGLE-MATCH DATING APPLIED TO EARLY AMERICAN MUSEUM OBJECTS

ABSTRACT Wiggle-match dating of tree-ring sequences is particularly promising for achieving high-resolution dating across periods with reversals and plateaus in the calibration curve, such as the entire post-Columbian period of North American history. Here we describe a modified procedure for wiggle-match dating that facilitates precise dating of wooden museum objects while minimizing damage due to destructive sampling. We present two case studies, a dugout canoe and wooden trough, both expected to date to the 18th–19th century. (1) Tree rings were counted and sampled for dating from exposed, rough cross-sections in the wood, with no or minimal surface preparation, to preserve these fragile objects; (2) dating focused on the innermost and outermost portions of the sequences; and (3) due to the crude counting and sampling procedures, the wiggle-match was approximated using a simple ordered Sequence, with gaps defined as Intervals. In both cases, the outermost rings were dated with precision of 30 years or better, demonstrating the potential of wiggle-match dating for post-European Contact canoes and other similar objects.

Two‐phase anaerobic codigestion of a cassava starch‐based biopolymer with papaya waste

AbstractThe objective of this work was to evaluate anaerobic digestion processes of mixtures of cassava starch‐based polymer (CSP) and papaya fruit pulp waste (PPW) at different ratios, with physical division of digestion stages. Acidogenic phase (phase 1) was conducted under temperature of 45°C and hydraulic retention time of 96 h. Monitoring reactors with destructive samples were subjected to the same conditions in order to assess the initial degradation profile of the substrates. The methanogenic phase (phase 2) was conducted under temperature of 37°C and the hydraulic retention time was defined as equal to the time the biogas production ceased. The results showed that the CSP/PPW mixtures presented interesting synergism and high biogas production potential under the conditions studied. The treatment with CSP/PPW at the ratio of 75/25 showed the best digestion performances in the phase 1. The highest CSP to PPW ratios showed satisfactory results for accumulated biogas productions (5955.58 mL) and for maximum biogas production rate (25.64 mL gVS−1 day−1) in the phase 2.

Enhancing estimation of cover crop biomass using field-based high-throughput phenotyping and machine learning models.

Incorporating cover crops into cropping systems offers numerous potential benefits, including the reduction of soil erosion, suppression of weeds, decreased nitrogen requirements for subsequent crops, and increased carbon sequestration. The aboveground biomass (AGB) of cover crops strongly influences their performance in delivering these benefits. Despite the significance of AGB, a comprehensive field-based high-throughput phenotyping study to quantify AGB of multiple cover crops in the U.S. Midwest has not been found. This study presents a two-year field experiment carried out in Eastern Nebraska, USA, to estimate AGB of five different cover crop species [canola (Brassica napus L.), rye (Secale cereale L.), triticale (Triticale × Triticosecale L.), vetch (Vicia sativa L.), and wheat (Triticum aestivum L.)] using high-throughput phenotyping and Machine Learning (ML) models. Destructive AGB sampling was performed three times during each spring season in 2022 and 2023. An array of morphological, spectral, thermal, and environmental features from the sensors were utilized as feature inputs of ML models. Moderately strong linear correlations between AGB and the selected features were observed. Four ML models, namely Random Forests Regression (RFR), Support Vector Regression (SVR), Partial Least Squares Regression (PLSR), and Artificial Neural Network (ANN), were investigated. Among the four models, PLSR achieved the highest Coefficient of Determination (R2) of 0.84 and the lowest Root Mean Squared Error (RMSE) of 892 kg/ha (Normalized RMSE (NRMSE) = 8.87%), indicating that PLSR could be the most appropriate method for estimating AGB of multiple cover crop species. Feature importance analysis ranked spectral features like Normalized Difference Red Edge (NDRE), Solar-induced Fluorescence (SIF), Spectral Reflectance at 485 nm (R485), and Normalized Difference Vegetation Index (NDVI) as top model features using PLSR. When utilizing fewer feature inputs, ANN exhibited better prediction performance compared to other models. Using morphological and spectral parameters as input features alone led to a R2 of 0.80 and 0.77 for AGB prediction using ANN, respectively. This study demonstrated the feasibility of high-throughput phenotyping and ML techniques for accurately estimating AGB of multiple cover crop species. Further enhancement of model performance could be achieved through additional destructive sampling conducted across multiple locations and years.