There is an increasing evidence that smallholder farms contribute substantially to food production globally, yet spatially explicit data on agricultural field sizes are currently lacking. Automated ...field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used. However, both have limitations, for example, automatic field size delineation using remote sensing has not yet been implemented at a global scale while the spatial resolution is very coarse when using census data. This paper demonstrates a unique approach to quantifying and mapping agricultural field size globally using crowdsourcing. A campaign was run in June 2017, where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo‐Wiki application. During the campaign, participants collected field size data for 130 K unique locations around the globe. Using this sample, we have produced the most accurate global field size map to date and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental, and national levels. The results show that smallholder farms occupy up to 40% of agricultural areas globally, which means that, potentially, there are many more smallholder farms in comparison with the two different current global estimates of 12% and 24%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modeling, comparative studies of agricultural dynamics across different contexts, for training and validation of remote sensing field size delineation, and potential contributions to the Sustainable Development Goal of Ending hunger, achieve food security and improved nutrition and promote sustainable agriculture.
This paper demonstrates a unique approach to quantifying and mapping agricultural field size globally using crowdsourcing. A campaign was run in June 2017 where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo‐Wiki application. The results show that smallholder farms occupy up to 40% of agricultural areas globally, which means that, potentially, there are many more smallholder farms in comparison with the two different current global estimates of 12% and 24%. The global field size map and the crowdsourced data set are openly available.
For many environmental applications, an accurate spatial mapping of land cover is a major concern. Currently, land cover products derived from satellite data are expected to offer a fast and ...inexpensive way of mapping large areas. However, the quality of these products may also largely depend on the area under study. As a result, it is common that various products disagree with each other, and the assessment of their respective quality still relies on ground validation datasets. Recently, crowdsourced data have been suggested as an alternate source of information that might help overcome this problem. However, crowdsourced data still remain largely discarded in scientific studies due to their inherent poor quality assurance. The aim of this paper is to present an efficient methodology that allows the user to code information brought by crowdsourced data even if no prior quality estimation is at hand and possibly to fuse this information with existing land cover products in order to improve their accuracy. It is first suggested that information brought by volunteers can be coded as a set of inequality constraints about the probabilities of the various land use classes at the visited places. This in turn allows estimating optimal probabilities based on a maximum entropy principle and to proceed afterwards with a spatial interpolation of these volunteers' information. Finally, a Bayesian data fusion approach can be used for fusing multiple volunteers' contributions with a remotely-sensed land cover product. This methodology is illustrated in this paper by focusing on the mapping of croplands in Ethiopia, where the aim is to improve the mapping of cropland as coming out from a land cover product with mitigated performances. It is shown how crowdsourced information can seriously improve the quality of the final product. The corresponding results also suggest that a prior assessing of remotely-sensed data quality can seriously improve the benefit of crowdsourcing campaigns, so that both sources of information need to be accounted together in order to optimize the sampling efforts.
Land cover mapping plays an important role for a wide spectrum of applications that are ranging from climate modeling to food security. However, it is a common case that several and partially ...conflicting land cover products are available at the same time over a same area, where each product suffers from specific limitations and lack of accuracy. In order to take advantage of the best features of each product while at the same time attenuating their respective weaknesses, this paper is proposing a methodology that allows the user to combine these products together based on a general framework involving maximum entropy/minimum divergence principles, Bayesian data fusion and Bayesian updating. First, information brought by each land cover product is coded in terms of inequality constraints so that a first estimation of their quality can be computed based on a maximum entropy/minimum divergence principle. Information from these various land cover products can then be fused afterwards in a Bayesian framework, leading to a single map with an associated measure of uncertainty. Finally, it is shown how the additional information brought by control data can help improving this fused map through a Bayesian updating procedure. The first part of the paper is briefly presenting the most important theoretical results, while the second part is illustrating the use of this suggested approach for a specific area in Belgium, where five different land cover products are at hand. The benefits and limitations of this approach are finally discussed by the light of the results for this case study.
Categorical data play an important role in a wide variety of spatial applications, while modeling and predicting this type of statistical variable has proved to be complex in many cases. Among other ...possible approaches, the Bayesian maximum entropy methodology has been developed and advocated for this goal and has been successfully applied in various spatial prediction problems. This approach aims at building a multivariate probability table from bivariate probability functions used as constraints that need to be fulfilled, in order to compute a posterior conditional distribution that accounts for hard or soft information sources. In this paper, our goal is to generalize further the theoretical results in order to account for a much wider type of information source, such as probability inequalities. We first show how the maximum entropy principle can be implemented efficiently using a linear iterative approximation based on a minimum norm criterion, where the minimum norm solution is obtained at each step from simple matrix operations that converges to the requested maximum entropy solution. Based on this result, we show then how the maximum entropy problem can be related to the more general minimum divergence problem, which might involve equality and inequality constraints and which can be solved based on iterated minimum norm solutions. This allows us to account for a much larger panel of information types, where more qualitative information, such as probability inequalities can be used. When combined with a Bayesian data fusion approach, this approach deals with the case of potentially conflicting information that is available. Although the theoretical results presented in this paper can be applied to any study (spatial or non-spatial) involving categorical data in general, the results are illustrated in a spatial context where the goal is to predict at best the occurrence of cultivated land in Ethiopia based on crowdsourced information. The results emphasize the benefit of the methodology, which integrates conflicting information and provides a spatially exhaustive map of these occurrence classes over the whole country.
Soil drainage classes spatial mapping is of great interest since drainage has direct effects on crop productivity and hydrological modelling. However, the prediction of this categorical variable ...often requires a laborious and expensive sampling over large areas. There is thus a need for a methodology that is able to combine several sources of information to improve the prediction. Bayesian maximum entropy (BME) has become a complete framework in the context of space–time prediction. This method proposes solutions to combine several sources of data no matter what the nature of information is. However, the various attempts that were made for adapting the BME methodology to categorical variables and mixed random fields faced some limitations, as a high computational burden. The main objective of this paper is to overcome this limitation by generalizing the Bayesian data fusion (BDF) theoretical framework to categorical variables, which is a simplification of the BME method through the conditional independence hypothesis. The BDF methodology for categorical variables is first described and then applied to a practical case study: the estimation of soil drainage classes using a soil map and point observations around Mechelen (Belgium). The BDF approach is compared to BME along with more classical approaches, as indicator cokriging (ICK) and logistic regression. Estimators are compared using various indicators, namely the percentage of correctly classified locations and the average highest probability. Although BDF methodology for categorical variables is a simplification of BME approach, both methods lead to very close results and have strong advantages compared to ICK and logistic regression.
The abundance of spatial and space–time data in many research fields has led to an increasing interest in the analytics of spatial data information. This development has renewed the attention to ...predictive spatial methodologies and advancing geostatistical tools. In this context, the present work reviews a series of cross-discipline studies that utilize multiple monitoring sources, and promote applied approaches in spatial and spatiotemporal modeling to improve our understanding of uncertainty. As multi-sourced information gives birth to new aspects of uncertainty, we explore emerging patterns in dealing with uncertainty in sources across structured, unstructured, and incomplete spatial data. We also illustrate how additional forms of information, such as secondary data and physical models, can further support and benefit research in the characterization and modeling of natural attributes.
The objective of this study was to investigate the genetic relationship between body condition score (BCS) and reproduction traits for first-parity Canadian Ayrshire and Holstein cows. Body condition ...scores were collected by field staff several times over the lactation in herds from Québec, and reproduction records (including both fertility and calving traits) were extracted from the official database used for the Canadian genetic evaluation of those herds. For each breed, six 2-trait animal models were run; they included random regressions that allowed the estimation of genetic correlations between BCS over the lactation and reproduction traits that are measured as a single lactation record. Analyses were undertaken on data from 108 Ayrshire herds and 342 Holstein herds. Average daily heritabilities of BCS were close to 0.13 for both breeds; these relatively low estimates might be explained by the high variability among herds and BCS evaluators. Genetic correlations between BCS and interval fertility traits (days from calving to first service, days from first service to conception, and days open) were negative and ranged between −0.77 and −0.58 for Ayrshire and between −0.31 and −0.03 for Holstein. Genetic correlations between BCS and 56-d nonreturn rate at first insemination were positive and moderate. The trends of these genetic correlations over the lactation suggest that a genetically low BCS in early lactation would increase the number of days that the primiparous cow was not pregnant and would decrease the chances of the primiparous cow to conceive at first service. Genetic correlations between BCS and calving traits were generally the strongest at calving and decreased with increasing days in milk. The correlation between BCS at calving and maternal calving ease was 0.21 for Holstein and 0.31 for Ayrshire and emphasized the relationship between fat cows around calving and dystocia. Genetic correlations between calving traits and BCS during the subsequent lactation were moderate and favorable, indicating that primiparous cows with a genetically high BCS over the lactation would have a greater chance of producing a calf that survived (maternal calf survival) and would transmit the genes that allowed the calf to be born more easily (maternal calving ease) and to survive (direct calving ease).
•Management guidelines in mandibular osteomyelitis in Osteopetrosis.•A female patient with osteopetrosis presented osteomyelitis after teeth extraction.•Our case suggests the importance to maintain ...at maximum existing mandibular bone.•Dental prevention could reduce occurrence of osteomyelitis in Osteopetrosis.
Osteopetrosis is a poorly known and probably underdiagnosed pathology. It is caused by various genetic abnormalities resulting in osteoclast dysfunction. Functional and aesthetic consequences have a major impact on the patient’s quality of life. Ten percent of osteopetrosis cases develop osteomyelitis that usually involves the mandible. Management of this complication remains complex and often unsatisfactory.
We report a case of a 62-year-old woman with osteopetrosis, complicated by mandibular osteomyelitis with intra-oral bone exposure and submental fistulas. Management was performed with antibiotic therapy and surgical necrotic resection. This cured the fistulas but the bone exposure persisted.
This case report highlights the difficulty of achieving complete healing of osteomyelitis in osteopetrosis. Antibiotic therapy, surgical management, or even hyperbaric oxygen therapy are required, but must be adapted to the case. A free flap procedure is undesirable but, when it is necessary, a bone marrow transplant could be considered to restore osteoclast function.
The management of mandibular osteomyelitis in patients with osteopetrosis must adapt to the situation and severity. To avoid most cases of osteomyelitic complications in patients suffering from osteopetrosis, we propose that a preventive strategy of better dental care should be considered.
The objective of this study was to investigate the genetic relationship between body condition score (BCS) and calving traits (including calving ease and calf survival) for Ayrshire second-parity ...cows in Canada. The use of random regression models allowed assessment of the change of genetic correlation from 100 d before calving to 335 d after calving. Therefore, the influence of BCS in the dry period on subsequent calving could be studied. Body condition scores were collected by field staff several times over the lactation in 101 herds from Québec and calving records were extracted from the official database used for Canadian genetic evaluation of calving ease. Daily heritability of BCS increased from 0.07 on d 100 before calving to 0.25 at 335 d in milk. Genetic correlations between BCS at different stages ranged between 0.59 and 0.99 and indicated that genetic components for BCS did not change much over lactation. With the exception of the genetic correlation between BCS and direct calving ease, which was low and negative, genetic correlations between BCS and calving traits were positive and moderate to high. Correlations were the highest before calving and decreased toward the end of the ensuing lactation. The correlation between BCS 10 d before calving and maternal calving ease was 0.32 and emphasized the relationship between fat cows before calving with dystocia. Standards errors of the genetic correlations estimates were low. Genetic correlations between BCS and calf survival were moderate to high and favorable. This indicates that cows with a genetically high BCS across lactation would have a greater chance of producing a calf that survived (maternal calf survival) and that they would transmit genes that allow the calf to survive (direct calf survival).
arab winter Kurzman, Charles; Fahmy, Dalia F.; Gengler, Justin ...
Contexts (Berkeley, Calif.),
05/2013, Letnik:
12, Številka:
2
Journal Article
Five experts, Charles Kurzman, Dalia F. Fahmy, Justin Gengler, Ryan Calder, and Sarah Leah Whitson comment on whether or not the promise of an Arab Spring of democracy and freedom has withered.
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