Optimization of carbon stock models to local conditions using farmers' soil tests: A case study with AMGv2 for a cereal plain in central France

Soil organic carbon (SOC) is associated with environmental benefits and crop productivity; therefore, monitoring of SOC stocks is important when assessing the sustainability of agricultural systems. This study applied a hybrid method and the results of on-farm soil tests to calibrate the AMGv2 model and adapt it to local conditions. The method was applied to soils in the Limagne plain (France), which shifted to cereal production since the 1960s. Based on analysing 988 soil test data, covering the period from 1954 to 2019, it was found that SOC stocks showed a significant decrease in the three main soil types of the study area. An optimization procedure estimated that the initial ratios of stable to total carbon fall in the ranges 0.42 and 0.46 for vertisols, 0.48 and 0.52 for calcisols, and 0.56 and 0.60 for fluvisols. Simulations using these values estimated that SOC stocks declined between 1960 and 2018 by between −31 and −17%, depending on soil type. The optimized model was used to forecast the evolution of SOC stocks up to 2050. These simulations showed a further decline in SOC stocks with continuation of current practices, even assuming a 15% increase in crop yields. They indicated that stopping straw exports would stabilize stocks, while a systematic introduction of cover crops would increase stocks about 3.8% over the period considered. It is concluded that this hybrid procedure can improve the adaptation of predictive models to local conditions.     

Comparison of Seven Digital Cameras for Digitizing Radiographs

The purpose of this study was to evaluate seven digital cameras for their ability to adequately capture quality radiographic images and their relative cost effectiveness. Radiographs of line pair (LP) phantoms (ranging from 0.6 to 16.6 LP/mm) and a 20-step contrast phantom were photographed at a near distance of 30.0 cm and a far distance (determined for each camera) at which a full 14 x 17 in film filled the viewfinder. Of the seven cameras tested, the Canon EOS-D30 consistently performed the best in LP/mm resolved (7.7 at close distance, 1.4 at far distance) and in contrast latitude (all 20 steps at both close and far distances). The Canon EOS-D30, determined to be the best camera used in this study for photographing radiographic images, is also the most expensive camera that was used. Two cameras, the Nikon Coolpix 995 and the Sony DSC-F707, were very close to the Canon in performance at considerably less cost. Certain inexpensive cameras may not be acceptable choices for teleradiology.     

IMAGING DIAGNOSIS: MAGNETIC RESONANCE IMAGING OF A CERVICAL WOODEN FOREIGN BODY IN A DOG

This article describes the discovery of a chronic cervical wooden foreign body ventral to the left transverse processes of the cranial cervical spine using magnetic resonance imaging (MRI) in a dog that presented with chronic neck pain and lameness. The dog did not exhibit dysphagia or chronic draining tracts, the most common signs of the presumed cause, that of a penetrating oropharyngeal foreign body. The foreign body itself was represented on MR images as an oval straight-edged core within an inflammatory tissue reaction. The wood was slightly hyperintense on T2- and isointense on T1-weighted images relative to muscle. Surrounding this was a more conspicuous contrast-enhancing reactive tissue rim that was hyperintense on all pulse sequences. Adjacent musculature also exhibited diffuse edema and contrast enhancement that extended around the left cervical vertebral transverse processes and local intervertebral nerve roots. The foreign body was found to be a wooden stick upon surgical removal. MRI is an excellent method for visualizing the inflammatory tissue reactions associated with soft-tissue foreign bodies because of its contrast resolution and depiction of anatomy in multiple imaging planes.     

Quantification of flight times of aerial treatments targeting invasive species: the interplay of helicopter or drone with bait-delivery systems,flight speed and bait form

BACKGROUND

Aerial treatments for invasive species management are now common, but we are unaware of any work published in the scientific literature quantifying how the interplay of numerous factors affects flight time and therefore operational costs. Here, we analyse aerial treatment data collected from two ant-eradication programmes, quantifying how the relationships between flight time and area are influenced by numerous aircraft/delivery system/bait/flight speed combinations.

RESULTS

For bait dispersal by helicopters, and when swath widths are equivalent, side-mounted Isolair was significantly more efficient than the simultaneous use of two underslung buckets, and use of two buckets was slightly but not significantly more efficient than one bucket. In this scenario, delivery by Isolair was, on average, 39.8% and 31.5% more efficient than the use of one or two buckets, respectively. However, when the swath width used with Isolair was halved to 10 m and flight speed was increased slightly, flight time was significantly greater compared to the other configurations. For bait dispersed by drone, flights conducted using an upgraded Flight Management System (FMS) and greater flight speed but smaller swath width were significantly more efficient than flights using the older FMS and lower flight speed. Over 10 and 50 ha the helicopter was 2.87 and 4.82 times more time efficient than the drone.

CONCLUSION

We encourage practitioners to publish data from their aerial treatments, and to try new methods, to accelerate improvements in efficiency and reduce the costs of aerial treatments. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.     

Drivers of recent forest cover change in southern South America are linked to climate and CO2

Landscape Ecology - Widespread changes in forest structure and distribution have been documented in northern Patagonia over the past century. We employed LPJ-GUESS, a dynamic global...     

Small-scale heterogeneity in carbon dioxide, nitrous oxide and methane production from aggregates of a cultivated sandy-loam soil

Spatial variability in carbon dioxide (CO₂), nitrous oxide (N₂O) and methane (CH₄) emissions from soil is related to the distribution of microsites where these gases are produced. Porous soil aggregates may possess aerobic and anaerobic microsites, depending on the water content of pores. The purpose of this study was to determine how production of CO₂, N₂O and CH₄ was affected by aggregate size and soil water content. An air-dry sandy loam soil was sieved to generate three aggregate fractions (<0.25 mm, 0.25–2 mm and 2–6 mm) and bulk soil (<2 mm). Aggregate fractions and bulk soil were moistened (60% water-filled pore space, WFPS) and pre-incubated to restore microbial activity, then gradually dried or moistened to 20%, 40%, 60% or 80% WFPS and incubated at 25 °C for 48 h. Soil respiration peaked at 40% WFPS, presumably because this was the optimum level for heterotrophic microorganisms, and at 80% WFPS, which corresponded to the peak N₂O production. More CO₂ was produced by microaggregates (<0.25 mm) than macroaggregate (>0.25 mm) fractions. Incubation of aggregate fractions and soil at 80% WFPS with acetylene (10 Pa and 10 kPa) and without acetylene showed that denitrification was responsible for 95% of N₂O production from microaggregates, while nitrification accounted for 97–99% of the N₂O produced by macroaggregates and bulk soil. This suggests that oxygen (O₂) diffusion into and around microaggregates was constrained, whereas macroaggregates remained aerobic at 80% WFPS. Methane consumption and production were measured in aggregates, reaching 1.1–6.4 ng CH₄–C kg⁻¹ soil h⁻¹ as aggregate fractions and soil became wetter. For the sandy-loam soil studied, we conclude that nitrification in aerobic microsites contributed importantly to total N₂O production, even when the soil water content permitted denitrification and CH₄ production in anaerobic microsites. The relevance of these findings to microbial processes controlling N₂O production at the field scale remains to be confirmed.     

Using unsupervised learning techniques to assess interactions among complex traits in soybeans

Soybean yield components and agronomic traits are connected through physiological pathways that impose tradeoffs through genetic and environmental constraints. Our primary aim is to assess the interdependence of soybean traits by using unsupervised machine learning techniques to divide phenotypic associations into environmental and genetic associations. This study was performed on large scale, jointly analyzing 14 quantitative traits in a large multi-parental population designed for genetic studies. We collected phenotypes from 2012 to 2015 from a soybean nested association panel with 40 families of approximately 140 individuals each. Pearson and Spearman correlations measured phenotypic associations. A multivariate mixed linear model provided genotypic and environmental correlations. To evaluate relationships among traits, the study used principal component and undirected graphical models from phenotypic, genotypic, and environmental correlation matrices. Results indicate that high phenotypic correlation occurs when traits display both genetic and environmental correlations. In genetic terms, length of reproductive period, node number, and canopy coverage play important roles in determining yield potential. Optimal grain yield production occurs when the growing environment favors faster canopy closure and extended reproductive length. Environmental associations found among yield components give insight into the nature of yield component compensation. The use of unsupervised learning methods provides a good framework for investigating interactions among various quantitative traits and defining target traits for breeding.     

Identifying and exploring significant genomic regions associated with soybean yield,seed fatty acids,protein and oil

Soybean [Glycine max (L.) Merrill] yield and seed fatty acids, protein, and oil content are important traits for which an improved understanding of significant genomic regions would be useful. To accomplish this, a soybean population consisting of 203 F5 derived recombinant inbred lines (RILs) was developed and genotyped with 11,633 polymorphic single nucleotide polymorphisms (SNPs). Each RIL was grown in a single plot at Knoxville, TN in 2010; followed by replicated, multi-location field trials in 2013 and 2014. The data from 2010, 2013, and 2014 were analyzed together in order to detect quantitative trait loci (QTL) for these traits, and 30 total QTLs were detected. Five QTLs are candidates for confirmed status and one QTL is a candidate for positional confirmation. Many of the genes with mutations in close proximity to the fatty acid QTLs are involved in biological processes for fatty acids and/or lipids and could be considered possible candidate genes. Similarly, genes with mutations in genomic regions near yield, protein, and oil QTLs were plentiful and may contribute to the variation observed in these traits. Except for yield and stearic acid, each trait displayed pleiotropic effects with other traits in this study. Notable are the pleiotropic effects for oleic and linolenic acid on chromosomes 9, 13, and 19. Overall, the findings from this research contribute new information to the genetic understanding of soybean yield and seed fatty acids, protein and oil content. This understanding will be useful in making trait improvements.