Household Health and Cocoa Production: A Baseline Survey of Smallholder Farming Households in Western Region, Ghana

Background Chronic illness and premature mortality from malaria, water-borne diseases, and respiratory illnesses have long been known to diminish the welfare of individuals and households in developing countries. Previous research has also shown that chronic diseases among farming populations suppress labor productivity and agricultural output. As the illness and death toll from HIV/AIDS continues to climb in most of sub-Saharan Africa, concern has arisen that the loss of household labor it causes will reduce crop yields, impoverish farming households, intensify malnutrition, and suppress growth in the agricultural sector. If chronic morbidity and premature mortality among individuals in farming households have substantial impacts on household production, and if a large number of households are affected, it is possible that an increase in morbidity and mortality from HIV/AIDS or other diseases could affect national aggregate output and exports. If, on the other hand, the impact at the household farm level is modest, or if relatively few households are affected, there is likely to be little effect on aggregate production across an entire country. Which of these outcomes is more likely in West Africa is unknown. Little rigorous, quantitative research has been published on the impacts of AIDS on smallholder farm production, particularly in West Africa. The handful of studies that have been conducted have looked mainly at small populations in areas of very high HIV prevalence in southern and eastern Africa. Conclusions about how HIV/AIDS, and other causes of chronic morbidity and mortality, are affecting agriculture across the continent cannot be drawn from these studies. In view of the importance of agriculture, and particularly smallholder agriculture, in the economies of most African countries and the scarcity of resources for health interventions, it is valuable to identify, describe, and quantify the impact of chronic morbidity and mortality on smallholder production of important crops in West Africa. One such crop is cocoa. In Ghana, cocoa is a crop of national importance that is produced almost exclusively by smallholder households. In 2003, Ghana was the world’s second-largest producer of cocoa. Cocoa accounted for a quarter of Ghana’s export revenues that year and generated 15 percent of employment. The success and growth of the cocoa industry is thus vital to the country’s overall social and economic development. Study Objectives and Methods In February and March 2005, the Center for International Health and Development of Boston University (CIHD) and the Department of Agricultural Economics and Agribusiness (DAEA) of the University of Ghana, with financial support from the Africa Bureau of the U.S. Agency for International Development and from Mars, Inc., which is a major purchaser of West African cocoa, conducted a survey of a random sample of cocoa farming households in the Western Region of Ghana. The survey documented the extent of chronic morbidity and mortality in cocoa growing households in the Western Region of Ghana, the country’s largest cocoa growing region, and analyzed the impact of morbidity and mortality on cocoa production. It aimed to answer three specific research questions. (1) What is the baseline status of the study population in terms of household size and composition, acute and chronic morbidity, recent mortality, and cocoa production? (2) What is the relationship between household size and cocoa production, and how can this relationship be used to understand the impact of adult mortality and chronic morbidity on the production of cocoa at the household level? The study population was the approximately 42,000 cocoa farming households in the southern part of Ghana’s Western Region. A random sample of households was selected from a roster of eligible households developed from existing administrative information. Under the supervision of the University of Ghana field team, enumerators were graduate students of the Department of Agricultural Economics and Agribusiness or employees of the Cocoa Services Division. A total of 632 eligible farmers participated in the survey. Of these, 610 provided complete responses to all questions needed to complete the multivariate statistical analysis reported here.

SOUND PROPAGATION AND SCATTERING IN BUBBLY LIQUIDS

In the ocean, natural and artificial processes generate clouds of bubbles which scatter and attenuate sound. Measurements have shown that at the individual bubble resonance frequency, sound propagation in this medium is highly attenuated and dispersive. Theory to explain this behavior exists in the literature, and is adequate away from resonance. However, due to excessive attenuation near resonance, little experimental data exists for comparison. An impedance tube was developed specifically for exploring this regime. Using the instrument, unique phase speed and attenuation measurements were made for void fractions ranging from 6.2 × 10^−5 to 2.7 × 10^−3 and bubble sizes centered around 0.62 mm in radius. Improved measurement speed, accuracy and precision is possible with the new instrument, and both instantaneous and time-averaged measurements were obtained. Behavior at resonance was observed to be sensitive to the bubble population statistics and agreed with existing theory, within the uncertainty of the bubble population parameters. Scattering from acoustically compact bubble clouds can be predicted from classical scattering theory by using an effective medium description of the bubbly fluid interior. Experimental verification was previously obtained up to the lowest resonance frequency. A novel bubble production technique has been employed to obtain unique scattering measurements with a bubbly-liquid-filled latex tube in a large indoor tank. The effective scattering model described these measurements up to three times the lowest resonance frequency of the structure.

APPLICATION OF THE KHOKHLOV-ZABOLOTSKAYA-KUZNETSOV EQUATION TO MODELING HIGH-INTENSITY FOCUSED ULTRASOUND BEAMS

High-intensity focused ultrasound is a form of therapeutic ultrasound which uses high amplitude acoustic waves to heat and ablate tissue. HIFU employs acoustic amplitudes that are high enough that nonlinear propagation effects are important in the evolution of the sound field. A common model for HIFU beams is the Khokhlov-Zabolotskaya-Kuznetsov (KZK) equation which accounts for nonlinearity, diffraction, and absorption. The KZK equation models diffraction using the parabolic or paraxial approximation. Many HIFU sources have an aperture diameter similar to the focal length and the paraxial approximation may not be appropriate. Here, results obtained using the “Texas code,” a time-domain numerical solution to the KZK equation, were used to assess when the KZK equation can be employed. In a linear water case comparison with the O’Neil solution, the KZK equation accurately predicts the pressure field in the focal region. The KZK equation was also compared to simulations of the exact fluid dynamics equations (no paraxial approximation). The exact equations were solved using the Fourier-Continuation (FC) method to approximate derivatives in the equations. Results have been obtained for a focused HIFU source in tissue. For a low focusing gain transducer (focal length 50λ and radius 10λ), the KZK and FC models showed excellent agreement, however, as the source radius was increased to 30λ, discrepancies started to appear. Modeling was extended to the case of tissue with the appropriate power law using a relaxation model. The relaxation model resulted in a higher peak pressure and a shift in the location of the peak pressure, highlighting the importance of employing the correct attenuation model. Simulations from the code that were compared to experimental data in water showed good agreement through the focal plane.

ACOUSTO-OPTIC IMAGING IN DIFFUSE MEDIA USING PULSED ULTRASOUND AND THE PHOTOREFRACTIVE EFFECT

Acousto-optic imaging (AOI) in optically diffuse media is a hybrid imaging modality in which a focused ultrasound beam is used to locally phase modulate light inside of turbid media. The modulated optical field carries with it information about the optical properties in the region where the light and sound interact. The motivation for the development of AOI systems is to measure optical properties at large depths within biological tissue with high spatial resolution. A photorefractive crystal (PRC) based interferometry system is developed for the detection of phase modulated light in AOI applications. Two-wave mixing in the PRC creates a reference beam that is wavefront matched to the modulated optical field collected from the specimen. The phase modulation is converted to an intensity modulation at the optical detector when these two fields interfere. The interferometer has a high optical etendue, making it well suited for AOI where the scattered light levels are typically low. A theoretical model for the detection of acoustically induced phase modulation in turbid media using PRC based interferometry is detailed. An AOI system, using a single element focused ultrasound transducer to pump the AO interaction and the PRC based detection system, is fabricated and tested on tissue mimicking phantoms. It is found that the system has sufficient sensitivity to detect broadband AO signals generated using pulsed ultrasound, allowing for AOI at low time averaged ultrasound output levels. The spatial resolution of the AO imaging system is studied as a function of the ultrasound pulse parameters. A theoretical model of light propagation in turbid media is used to explore the dependence of the AO response on the experimental geometry, light collection aperture, and target optical properties. Finally, a multimodal imaging system combining pulsed AOI and conventional B- mode ultrasound imaging is developed. B-mode ultrasound and AO images of targets embedded in both highly diffuse phantoms and biological tissue ex vivo are obtained, and millimeter resolution is demonstrated in three dimensions. The AO images are intrinsically co-registered with the B-mode ultrasound images. The results suggest that AOI can be used to supplement conventional B-mode ultrasound imaging with optical information.

Color Region Grouping and Shape Recognition with Deformable Models

A new deformable shape-based method for color region segmentation is described. The method includes two stages: over-segmentation using a traditional color region segmentation algorithm, followed by deformable model-based region merging via grouping and hypothesis selection. During the second stage, region merging and object identification are executed simultaneously. A statistical shape model is used to estimate the likelihood of region groupings and model hypotheses. The prior distribution on deformation parameters is precomputed using principal component analysis over a training set of region groupings. Once trained, the system autonomously segments deformed shapes from the background, while not merging them with similarly colored adjacent objects. Furthermore, the recovered parametric shape model can be used directly in object recognition and comparison. Experiments in segmentation and image retrieval are reported.

Deformable Shape Detection and Description via Model-Based Region Grouping

A method for deformable shape detection and recognition is described. Deformable shape templates are used to partition the image into a globally consistent interpretation, determined in part by the minimum description length principle. Statistical shape models enforce the prior probabilities on global, parametric deformations for each object class. Once trained, the system autonomously segments deformed shapes from the background, while not merging them with adjacent objects or shadows. The formulation can be used to group image regions based on any image homogeneity predicate; e.g., texture, color, or motion. The recovered shape models can be used directly in object recognition. Experiments with color imagery are reported.

Region Segmentation via Deformable Model-Guided Split and Merge

An improved method for deformable shape-based image segmentation is described. Image regions are merged together and/or split apart, based on their agreement with an a priori distribution on the global deformation parameters for a shape template. The quality of a candidate region merging is evaluated by a cost measure that includes: homogeneity of image properties within the combined region, degree of overlap with a deformed shape model, and a deformation likelihood term. Perceptually-motivated criteria are used to determine where/how to split regions, based on the local shape properties of the region group's bounding contour. A globally consistent interpretation is determined in part by the minimum description length principle. Experiments show that the model-based splitting strategy yields a significant improvement in segmention over a method that uses merging alone.

MosaicShape: Stochastic Region Grouping with Shape Prior

A novel method that combines shape-based object recognition and image segmentation is proposed for shape retrieval from images. Given a shape prior represented in a multi-scale curvature form, the proposed method identifies the target objects in images by grouping oversegmented image regions. The problem is formulated in a unified probabilistic framework and solved by a stochastic Markov Chain Monte Carlo (MCMC) mechanism. By this means, object segmentation and recognition are accomplished simultaneously. Within each sampling move during the simulation process,probabilistic region grouping operations are influenced by both the image information and the shape similarity constraint. The latter constraint is measured by a partial shape matching process. A generalized parallel algorithm by Barbu and Zhu,combined with a large sampling jump and other implementation improvements, greatly speeds up the overall stochastic process. The proposed method supports the segmentation and recognition of multiple occluded objects in images. Experimental results are provided for both synthetic and real images.

A Neural Network Model of Speech Acquisition and Motor Equivalent Speech Production

This article describes a neural network model that addresses the acquisition of speaking skills by infants and subsequent motor equivalent production of speech sounds. The model learns two mappings during a babbling phase. A phonetic-to-orosensory mapping specifies a vocal tract target for each speech sound; these targets take the form of convex regions in orosensory coordinates defining the shape of the vocal tract. The babbling process wherein these convex region targets are formed explains how an infant can learn phoneme-specific and language-specific limits on acceptable variability of articulator movements. The model also learns an orosensory-to-articulatory mapping wherein cells coding desired movement directions in orosensory space learn articulator movements that achieve these orosensory movement directions. The resulting mapping provides a natural explanation for the formation of coordinative structures. This mapping also makes efficient use of redundancy in the articulator system, thereby providing the model with motor equivalent capabilities. Simulations verify the model's ability to compensate for constraints or perturbations applied to the articulators automatically and without new learning and to explain contextual variability seen in human speech production.