Phenotypic Assessment of Six Cassava Families Grown from Seed in Burkina Faso | InformativeBD

Sawadogo O. Michel, Some Koussao, Ouedraogo M. Hamed, Tiama Djakaria, Tiendrebeogo Fidèle, Soro Monique, Tonde Wendmanegda Hermann, and Sawadogo Mahamadou, from the different institute of Burkina Faso. wrote a Reseach Article about, Phenotypic Assessment of Six Cassava Families Grown from Seed in Burkina Faso. Entitled, Phenotypic evaluation of six cassava families (Manihot esculenta Crantz) from seed in Burkina Faso. This research paper published by the International Journal of Biosciences (IJB). an open access scholarly research journal on Biosciences. under the affiliation of the International Network For Natural Sciences| INNSpub. an open access multidisciplinary research journal publisher.

Abstract

Phenotypic markers are important in plant genetic characterisation studies. They are used in the present study to assess the phenotypic structuring of cassava genotypes obtained by biparental crossing. The plant material studied consists of 56 cassava genotypes from the third generation of vegetative reproduction following germination of seeds from six families resulting from crosses. To evaluate these genotypes, an Alpha lattice experimental design was used with three replicates and three blocks per replicate. Blocks I and II each contained 19 genotypes and block III 18 genotypes. Data was collected on 10 qualitative traits on leaves, stems and roots. All the variables evaluated presented several modalities. The frequencies showed that: the green-purple color (41%) was dominant for the apical leaf color characteristic. Stems color were predominantly light brown (30%). Green color (57%) was most common in the petioles. Genotypes showed more dichotomous ports (44%). In addition, the relative Shannon-Weaver diversity index (H’) was very high for all characters within genotypes (H’=0.90) and families (H’=0.66). The most polymorphic traits between genotypes were flowering ability (H’=1), stem color (H’=0.99), tuberous root texture (H’=0.97), apical leaf color (H’=0.96) and branching type (H’=0.93). The same index showed high intra-family diversity, family VI (H’= 0.83), family II (H’= 0.76), family IV (H’=0.69), family I (H’= 0.61), family III (H’= 0.53) and family V (H’= 0.52) showing high internal variability. ACH was used to structure the genetics into three phenotypic groups. This observed diversity can be used for cassava breeding in Burkina Faso.

Read more : Natural Patch Power:Moringa Pods and Katakataka Leaves vs. Staphylococcus aureus | InformativeBD

Introduction

Manioc (Manihot esculenta Crantz 1766) is a perennial shrub 1 to 5 m high (Allem, 2002; Alves, 2002). It belongs to the class Dicotyledones, family Euphorbiaceae, genus Manihot and species Manihot esculenta Crantz (Isendahl, 2011; Soro, 2022). It has a diploid chromosome number of 2n=36 and a highly heterozygous genome (Alves, 2002). It is one of the most important tuberous root crops, highly valued for its starch content in tropical countries (N'Zué et al., 2014). Cassava is grown all over the world, particularly in West Africa (Agré et al., 2015). Cassava can be grown in areas with rainfall ranging from 500 mm to 8000 mm (François, 1989). Depending on the variety, production can be spread over a long period of the year, making the tuberous roots available when needed (François, 1989).

In recent years in Burkina Faso, climate variability has made farming very difficult. Crop diversification is very important to ensure food self-sufficiency. Tuber and root crops such as cassava can therefore be used to help achieve sustainable food security. In Burkina Faso, cassava production was estimated at around 17,081.25 tonnes in 2022 (FAOSTAT, 2024). As in all African countries, almost all cassava production in Burkina Faso is used for human and animal consumption (Amani et al., 2007). The tuberous roots are eaten raw or in the form of local dishes: boiled roots, grilled roots, placali, con'godê, attiéké and gari (Guira et al., 2017). In view of its food and nutritional potential, the quantities of cassava produced remain below national market demand, which in 2017 was estimated at around 124,917 tonnes of fresh tubers (Soro et al., 2022). In Burkina Faso, the major constraints to large-scale production are linked to several factors, namely: the long production cycle of six to 9 or even 12 months, the unsuitable quality of the soils used for its cultivation, which results in low root yields, the lack of suitable varieties, and the very narrow genetic base of cassava (Gmakouba et al., 2018). In order to meet consumer needs, production must be increased, and this requires efficient production technology based on the use of improved cassava varieties.

Exchanges of genetic material between producers mean that they end up with duplicates of the same cultivar (Soro et al., 2022). The reproduction of cassava, which is generally done by cuttings, leads to the spread of its bio-aggressors, which become more and more numerous and infest new fields. Studies carried out by Tiendrébéogo et al. (2009, 2012) reported the presence of Cassava Mosaic Diseases (CMD) in certain areas of Burkina Faso. Cassava is often grown under rainfed and irrigated systems in Burkina Faso. This is due to the earliness of the rains in relation to the length of the vegetative cycle and the poverty of the arable land, which means that average yields in farming areas are low, less than or equal to 15t/ha (FAOSTAT, 2024). In response to this situation, a great deal of research has been carried out by INERA through the introduction and evaluation of six (06) improved varieties, catalogued and popularised, TMS 4(2) 1425; TMS 91/02312; TMS 92/0067; TMS 92/0325; TMS 92/0427; TMS 94/0270) with potential yield (40/ha) (Gmakouba, 2018; Soro, 2022; MASA, 2014). But of these, only TMS 94/0270, commonly known as V5, is the most widely produced for its very good attiéké quality. To meet this challenge, new cassava varieties need to be developed, with a view to broadening the genetic base so as to obtain varieties that are tolerant to FGD, rich in beta-carotene, and with yields of up to 40 tonnes per hectare. It is therefore essential to assess the agro-morphological diversity of this cassava collection (Manihot esculenta Crantz) in order to better exploit the potential of these genotypes. This study was therefore carried out with the overall aim of determining the structure of the 56 genotypes obtained by biparental crossing. Specifically, the aim was (i) to determine the variability of genotypes through phenotypic traits and (ii) to identify the traits that best discriminate between genotypes and families.

Reference

Agré A, Dansi A, Rabbi I, Battachargee R, Dansi M, Melaku G. 2015. Agromorphological characterization of elite cassava (Manihot esculenta Crantz) cultivars collected in Benin. International Journal of Current Research in Biosciences and Plant Biology 2(2), 1–14. https://cgspace.cgiar.org/bitstream/handle/10568/58355/A.P.%20Agre,%20et%20al.pdf.

Alam MK. 2021. A comprehensive review of sweet potato (Ipomoea batatas [L.] Lam): Revisiting the associated health benefits. Trends in Food Science and Technology 115, 512–529. https://doi.org/10.1016/j.tifs.2021.07.001.

Allem AC. 2002. The origin and taxonomy of cassava. In: Hillocks RJ, Thresh JM, Bellotti AC, eds. Cassava: Biology, Production and Utilization. CABI Publishing, New York, 1–16. https://doi.org/10.1079/9780851995243.0001.

Alves AAC, Hillocks RJ, Thresh JM, Bellotti AC. 2002. Cassava botany and physiology. In: Cassava: Biology, Production and Utilization. CABI Publishing, London, 67–89. https://doi.org/10.1079/9780851995243.0067.

Bakayoko S, Soro D, N’dri B, Kouadio KK, Tschannen A, Nindjin C, Dao D, Girardin O. 2013. Étude de l’architecture végétale de 14 variétés améliorées de manioc (Manihot esculenta Crantz) dans le centre de la Côte d’Ivoire. Journal of Applied Biosciences 61, 4471–4477. https://doi.org/10.4314/jab.v61i0.85595.

Belhadj H, Medini M, Bouhaouel I, Amara H. 2015. Analyse de la diversité phénotypique de quelques accessions autochtones de blé dur (Triticum turgidum ssp. durum Desf.) du sud tunisien. 11.

Djirabaye N, Papa SS, Naïtormbaïdé M, Mbaïguinam JM, Guisse A. 2016. Agro-morphological characterization of cassava (Manihot esculenta Crantz) cultivars from Chad. Agricultural Sciences 7, 77049. https://doi.org/10.4236/as.2016.77049.

Elias M, McKey D, Panaud O, Anstett MC, Robert T. 2001. Gestion traditionnelle de la diversité morphologique et génétique du veuf par les Makushi Amérindiens (Guyana, Amérique du Sud): Perspectives pour la conservation à la ferme des ressources génétiques des cultures. Euphytica 120, 143–157. https://doi.org/10.1023/A:1017501017031.

FAOSTAT. 2024. Food and Agriculture Organization of the United Nations Statistics Division. https://www.fao.org/faostat/en/#home.

Fukuda WMG, Guevara CL, Kawuki R, Ferguson ME. 2010. Selected morphological and agronomic descriptors for the characterization of cassava. International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria, 19.

Gashaw ET, Mekbib F, Ayana A. 2016. Genetic diversity among sugarcane genotypes based on qualitative traits. Advances in Agriculture 2016, Article ID 8909506, 8p. https://doi.org/10.1155/2016/8909506.

Gmakouba T, Some K, Traore ER, KpemouA KE, Zongo JD. 2018. Analyse de la diversité agromorphologique d’une collection de manioc (Manihot esculenta Crantz) du Burkina Faso. International Journal of Biological and Chemical Sciences 12(1), 402–421. http://www.ifgdg.org.

Guinko S. 1984. Végétation de la Haute Volta. Thèse de doctorat, Université Bordeaux III, 2 tomes, 556 p.

Guira F. 2016. Potentialités technologiques des racines de manioc à travers la production de l’attiéké: aspects nutritionnels, biochimiques, microbiologiques et moléculaires. Thèse de doctorat unique, Université Ouaga I Professeur Joseph KI-ZERBO, 173 p.

Isendahl C. 2011. The domestication and early spread of manioc (Manihot esculenta Crantz): A brief synthesis. Latin American Antiquity 22(4), 452–468.

Jain SK, Qualset CO, Bhatt GM, Wu KK. 1975. Geographical patterns of phenotypic diversity in a world collection of durum wheat. Crop Science 15, 700–704. https://doi.org/10.2135/cropsci1975.0011183X001500050026x.

Ka SL, Gueye M, Kanfany G, Diatta C, Mbaye MS, Noba K. 2020. Dynamique de levée des adventices du sorgho [Sorghum bicolor (L.) Moench] en zone soudanienne humide du Sénégal. International Review of Marine Science, Agronomy and Veterinary 8, 286–2930. http://www.ifgdg.org.

Mathura R, Dhander DG, Varma SP. 1989. Variability studies of cassava varieties on growth and yield under Tripura conditions. Journal of Root Crops 12, 25–28.

McKey D, Emperaireh L, Elias M, Pinton F, Robert T, Desmouliere S, Rival L. 2001. Gestions locales et dynamiques régionales de la diversité variétale du manioc en Amazonie, 26 p.

Médard R. 1973. Morphogénèse du manioc, Manihot esculenta Crantz, (Euphorbiacées-Crotonoidées): Étude descriptive. Adansonia 13, 483–494.

Ministère de l’Agriculture et de la Sécurité Alimentaire (MASA). 2014. Catalogue National des Espèces et Variétés Agricoles du Burkina Faso.

N’zué B, Okana M, Kouakou A, Dibi K, Zouhouri G, Essis B. 2014. Morphological characterization of cassava (Manihot esculenta Crantz) accessions collected in the centre-west, south-west, and west of Côte d’Ivoire. Greener Journal of Agricultural Sciences 4(6), 220–231. https://doi.org/10.15580/GJAS.2014.6.050614224.

Nartey F. 1978. Cassava cyanogenesis, ultrastructure, and seed germination. In: Denis R, Walter F, eds. Cassava. Copenhagen: Muksgaard, 234 p.

Raffaillac J-P, Second G. 2000. L’amélioration des plantes tropicales : le manioc.

Robooni T, Paul S, Rob M, Robert K. 2014. Combining ability analysis of storage root yield and related traits in cassava at the seedling evaluation stage of breeding. Journal of Crop Improvement 28(4), 530–546. http://www.tandfonline.com/loi/wcim20.

Sawadogo N, Naoura G, Ouoba A, Yaméogo N, Tiendrebeogo J, Ouedraogo MH. 2022. Phenotypical characteristics and genetic diversity of three types of sorghum [Sorghum bicolor (L.) Moench] cultivated in Burkina Faso based on qualitative traits. Moroccan Journal of Agricultural Sciences 3(2), 109–116. https://techagro.org/index.php/MJAS/article/view/941.

Sawadogo N. 2015. Diversité génétique des sorghos à grains sucrés [Sorghum bicolor (L.) Moench] du Burkina Faso. Thèse unique de doctorat, Université de Ouagadougou, 135 p.

Shannon CE, Weaver W. 1949. The mathematical theory of communication. University of Illinois Press, Urbana.

Soro M. 2022. Épidémiologie de la mosaïque du manioc (Manihot esculenta Crantz), diversité génétique et évaluation des accessions et variétés de manioc au Burkina Faso. Thèse unique de doctorat, Université Félix HOUPHOUËT BOIGNY, 184 p.

Tiendrébéogo F, Lefeuvre P, Hoareau M, Harimalala MA, De Bruyn A, Villemot J, Traoré VS, Konaté G, Traoré AS, Barro N, Reynaud B, Traoré O, Lett JM. 2012. Evolution of African cassava mosaic virus by recombination between bipartite and monopartite begomoviruses. Virology Journal 9(67).

Tiendrébéogo F, Lefeuvre P, Hoareau M, Traoré VSE, Barro N, Reynaud B, Traoré AS, Konaté G, Traoré O, Lett JM. 2009. Occurrence of East African cassava mosaic virus – Uganda (EACMV-UG) in Burkina Faso. Plant Pathology 58, 783.

Source : Phenotypic evaluation of six cassava families (Manihot esculenta Crantz) from seed in Burkina Faso   

Read More

Mapping Dindéresso Forest Landscapes with Sentinel-2 and Machine Learning | InformativeBD

Boalidioa Tankoano,  Dramane Ouedraogo,  Zézouma Sanon, Jérôme T. Yameogo, and Mipro Hien, from the different institute of Burkina Faso. wrote a Reseach Article about, Mapping Dindéresso Forest Landscapes with Sentinel-2 and Machine Learning. Entitled, Mapping heterogeneous landscapes using sentinel-2 imagery and machine learning algorithms: A case of the Dindéresso classified forest. This research paper published by the International Journal of Biosciences (IJB). an open access scholarly research journal on Biosciences. under the affiliation of the International Network For Natural Sciences| INNSpub. an open access multidisciplinary research journal publisher.

Abstract

The anthropization of natural ecosystems has not excluded the domain classified by the State. As a result, the landscape of protected areas such as the Dinderesso Classified Forest is highly heterogeneous. The overall objective was to assess the performance of machine learning algorithms in better mapping the land use classes of the Dinderesso Classified Forest. To do this, a Sentinel-2 image and information collected in the field were used. The Sentinel-2 image was classified using Random Forest and Support Vector Machine algorithms. 850 regions of interest were selected for model training and validation. Random Forest performed best, with a Kappa coefficient of 91.49% compared with 90.17% for Support Vector Machine. The F-score for the Bare land and Agroforestry parks class was the highest (0.98) and the Gallery and Dense Vegetation class had the lowest F-score (0.82). Both algorithms showed high levels of performance, so they are suitable for classifying heterogeneous landscapes. The proportion of the Bare land and Agroforestry parks class was 29.29% compared with 70.71% for the natural formation classes (shrub savannahs, tree savannahs, Gallery, and Dense Vegetation). Given the level of anthropization of the Classified Forest, measures need to be taken to limit this process to conserve biodiversity.

Read more : White Spot Syndrome Virus: A Major Threat to Shrimp Farming in Asia | InformativeBD

Introduction

Burkina Faso, a Sahelian country, is home to major reservoirs of biodiversity in West Africa (Ouoba, 2006; Tankaono et al., 2017; Tiendrebeogo et al., 2019). The State's classified domain, which covers around 14% of the national territory, is the foundation of the national biodiversity conservation policy (Tankaono et al., 2016; Zida et al., 2015). However, human activities such as inappropriate agricultural practices, overpopulation, exploitation, and urban sprawl, combined with the poverty of rural populations, constitute serious threats to this classified State domain (Tankoano et al., 2015; Sanon et al., 2019). According to the latest report on Burkina Faso's forests, around 60% of the country's protected areas are under human occupation (DIFOR, 2007). Between 1990 and 2015, the surface area of plant cover was reduced by around 1% per year (FAO, 2015). One of the main causes of this deforestation of protected areas is agriculture and gold panning (Ouedraogo et al., 2010; Dimobe et al., 2015; Soulama et al., 2015; Zoungrana et al., 2015; Semeki Ngabinzeke et al., 2016). These two main activities lead to the fragmentation of the forest ecosystems in these protected areas (Kabulu et al., 2008; Kpedenou et al., 2016; Tankoano et al., 2016; Sanon et al., 2019). Faced with this situation, monitoring the country's last vestiges of biodiversity is becoming crucial, even imperative, at the risk of witnessing an erosion of national biodiversity. Unfortunately, financial and human resources are lacking.

Most studies concerning vegetation cover mapping in Burkina Faso are based on Landsat satellite images, but very few have used Sentinel-2 images. Nowadays, remote sensing has become a powerful tool for monitoring protected areas. Satellite imagery is commonly used to study the dynamics of land-use units, mutations between land-use units, and the impacts of agricultural activities and logging (JofackSokeng et al., 2016; Gansaonré et al., 2020; Tankoano et al., 2023). These various activities within protected areas lead to a certain het erogeneity in the landscape, which makes it difficult to classify land-use units with a high level of precision.

More and more satellites and classification algorithms are being developed for this purpose. Machine learning algorithms are also being used to classify satellite images. Sentinel-2 images, with their high resolution (10m), make it easier to detect the smallest units in the landscape. Machine learning algorithms enable accurate cartographic results, facilitating timely decision-making by protected area managers. 

However, the application of machine learning algorithms in classifying heterogeneous ecosystems has been explored little. Their contribution to improved accuracy, hence the reduction of interclass confusion, therefore needs to be explored in highly heterogeneous savannah ecosystems.

This study aims to evaluate the ability of machine learning algorithms to classify a heterogeneous landscape using a sentinel-2 image with high accuracy. Specifically, the aim was to (i) map the Dinderesso Classified Forest using a Sentinel-2 image and machine learning ; (ii) assess the ability of each two machine learning algorithms (RF and SVM) to better classify the land use/land cover within Dinderesso classified forest.

Reference

Breiman L. 2001. Random forests. Machine Learning 45, 5-32. https://doi.org/10.1023/A:1010933404324

Chowdhury MS. 2024. Comparison of accuracy and reliability of random forest, support vector machine, artificial neural network and maximum likelihood met hod in land use/cover classification of urban set ting. Environmental Challenges 14. https://doi.org/10.1016/j.envc.2023.100800

Congalton R. 1991. A review of assessing the accuracy of classification of remotely sensed data. Remote Sens. Environ. 37, 35–46. https://doi.org/10.1016/0034-4257(91)90048-B

Cracknell MJ, Reading AM. 2014. Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information. Comput. Geosci. 63, 22–33. https://doi.org/10.1016/j.cageo.2013.10.008

Dagne SS, Hirpha HH, Tekoye AT, Dessie YB, Endeshaw AA. 2023. Fusion of sentinel-1 SAR and sentinel-2 MSI data for accurate urban land use-land cover classification in Gondar City, Ethiopia. Environmental Systems Research 12(1), 40. https://doi.org/10.1186/s40068-023-00324-5

Diallo H, Bamba I, Barima YSS, Visser M, Ballo A, Mama A, Vranken I, Maïga M, Bogaert J. 2011. Effet s combinés du climat et  des pressions anthropiques sur la dynamique évolutive de la végétation d’une zone protégée du Mali (Réserve de Fina, Boucle du Baoulé). Sécheresse 22(3), 97-107. DOI: 10.1684/sec.2011.0306

Dimobe K, Ouédraogo A, Soma S, Goet ze D, Porembski S, Thiombiano A. 2015. Identification of driving factors of land degradation and deforestation in the Wildlife Reserve of Bontioli (Burkina Faso, West Africa). Global Ecology and Conservation 4, 559-571. https://doi.org/10.1016/j.gecco.2015.10.006

Foody G. 2002. Status of land cover classification accuracy assessment. Remote Sens. Environ. 80, 185–201. https://doi.org/10.1016/S0034-4257(01)00295-4

Geymen A, Baz I. 2008. The potential of remote sensing for monitoring land cover changes and effects on physical geography in the area of Kayisdagi mountain and its surroundings (Istanbul). Environmental Monitoring and Assessment 140(3), 33-42. https://link.springer.com/article/10.1007/s10661-007-9844-6

Gholamy A, Kreinovich V, Kosheleva O. 2018. Why 70/30 or 80/20 relation between training and testing sets: A pedagogical explanation. Dep. Tech. Rep. 1209, 1–6.

Inoussa MM, Mahamane A, Mbow C, Saâdou M, Yvonne B. 2011. Dynamique spatio-temporelle des forêts claires dans le Parc national du W du Niger (Afrique de l’Ouest). Sécheresse 22(3), 97-107. DOI: 10.1684/sec.2011.0305

Islami FA, Tarigan SD, Wahjunie ED, Dasanto BD. 2022. Accuracy assessment of land use change analysis using Google Earth in Sadar Watershed Mojokerto Regency. IOP Conf. Series: Earth and Environmental Science 950, 012091. https://iopscience.iop.org/article/10.1088/1755-1315/950/1/012091

Kabba STV, Li J. 2011. Analysis of land use and land cover changes, and their ecological implication in Wuhan, China. Journal of Geography and Geology 3, 104-118.

Liu C, Frazier P, Kumar L. 2007. Comparative assessment of the measures of thematic classification accuracy. Remote Sens. Environ. 107, 606–616.

Mbow C. 2009. Potentiel et  dynamique des stocks de carbone des savanes soudaniennes et  soudano-guinéennes du Sénégal. Thèse de Doctorat d’Et at, Université Cheikh Anta Diop, Dakar, Sénégal, 319p.

N’Da DH, N’Guessan EK, Wadja ME, Affian K. 2008. Apport de la télédétection au suivi de la déforestation dans le parc national de la Marahoué (Côte d’Ivoire). Télédétection 8(1), 17-34.

Nery T, Sadler R, Solis-Aulestia M, White B, Polyakov M, Chalak M. 2016. Comparing supervised algorithms in land use and land cover classification of a Landsat time-series. Int. Geosci. Remote Sens. Symp, 5165–5168.

Ouédraogo I, Tigabu M, Savadogo P, Compaoré H, Oden PC, Ouadba JM. 2010. Land cover change and its relation with population dynamics in Burkina Faso, West Africa. Land Degradation and Development 21, 453-462.

Pointius RG Jr. 2000. Quantification error versus location in comparison of categorical maps. Photogrammetric Engineering and Remote Sensing 66(8), 1011-1016.

Rahman A, Abdullah HM, Tanzir MT, Hossain MJ, Khan BM, Miah MG, Islam I. 2020. Performance of different machine learning algorithms on satellite image classification in rural and urban set up. Remote Sensing Applications: Society and Environment 20.  https://doi.org/10.1016/j.rsase.2020.100410

Smits P, Dellepaine S, Schowengerdt R. 1999. Quality assessment of image classification algorithms for land cover mapping: a review and a proposal for a cost-based approach. Int. J. Remote Sen. 20, 1461–1486.

Soulama S, Kadeba A, Nacoulma BMI, Traoré S, Bachmann Y, Thiombiano A. 2015. Impact des activités anthropiques sur la dynamique de la végétation de la réserve partielle de faune de Pama et  de ses périphéries (sud-est du Burkina Faso) dans un contexte de variabilité climatique. Journal of Applied Biosciences 87, 8047-8064.

Tabopda WG, Huynh F. 2009. Caractérisation et  suivi du recul des ligneux dans les aires protégées au Nord du Cameroun: analyse par télédétection spatiale dans la réserve forestière de Kalfou. Journées d’animation scientifique (JAS09) de l’AUF, Alger, 11p.

Tankoano B, Hien M, N’Da DH, Sanon Z, Akpa YL, Jofack Sokeng V-C, Somda I. 2016. Cartographie de la dynamique du couvert végétal du Parc National des Deux Balé à l’Ouest du Burkina Faso. International Journal of Innovation and Applied Studies 16, 837-846.

Tankoano B, Hien M, Sanon Z, Dibi NH, Yameogo TJ, Somda I. 2015. Dynamique spatio-temporelle des savanes boisées de la Forêt Classée de Tiogo au Burkina Faso. Int. J. Biol. Chem. Sci. 9(4), 1983-2000.

Tiendrebeogo M, Bamna D, Pedabga A, Goungounga J. 2019. Fiche descriptive Ramsar, Burkina Faso, Complexe d’Aires Protégées Pô-Nazinga-Sissili. Ramsar. Available at: https://rsis.ramsar.org/fr/ris/2366?language=fr

Source : Mapping heterogeneous landscapes using sentinel-2 imagery and machine learning algorithms: A case ofthe Dindéresso classified forest 

Read More

Climate-Smart Agriculture Boosts Food Security Among Urban Gardeners in Réo, Burkina Faso | InformativeBD

Nadège Compaoré, and Joseph Yaméogo, from the institute of Burkina Faso. wrote a Research Article about, Climate-Smart Agriculture Boosts Food Security Among Urban Gardeners in Réo, Burkina Faso. Entitled, Impact of climate smart agriculture adoption on food security: The case of urban market gardeners in the city of Réo, Burkina Faso. This research paper published by the Journal of Biodiversity and Environmental Sciences (JBES). an open access scholarly research journal on Biodiversity. under the affiliation of the International Network For Natural Sciences| INNSpub. an open access multidisciplinary research journal publisher.

Abstract

Climate change is affecting Burkina Faso’s cities. This situation is forcing urban dwellers to take innovative measures to adapt. Several smart strategies have been implemented in urban market gardening to cope with recent rainfall variability over the period 2001-2021. The main objective of the study is to analyze the changes in rainfall in the area, the smart strategies used and the consequences in terms of food security of the strategies promoted in urban market gardening in Réo. To achieve this, a methodology combining secondary and primary data was required. Descriptive statistics, linear and logistic regression and the rainfall concentration index (PCI) were used to process the data collected. The study showed that the area has a high variability, with a PCI >20, reflecting a high variability and concentration of rainfall over a few months. In addition, the cumulative annual rainfall is increasing over the decade 2001-2021. This situation forces farmers to adopt a number of intelligent strategies to deal with the situation. This has led to leafy vegetable production, multi-species integration in vegetable plots and the introduction of short-cycle vegetables. These strategies have led to an increase in dietary diversity and a high level of food consumption, which has had an impact on the food security of market gardeners. The level of food insecurity is also low. This shows that the smart strategies promoted in the garden plots lead to high levels of food security for the market gardeners.

Read more : Modeling Mung Bean Growth with Polynomial Interpolation: Insights for Better Cultivation | InformativeBD

Introduction

Climate variability refers to variations in the mean state and other statistics (such as standard deviations, occurrence of extremes) of the climate on all time scales (IPCC, 2022). It affects every continent in the world and Africa is no exception (IPCC, 2021). In West Africa, changes in precipitation are long-run trends (Lüning et al., 2018; Zhang et al., 2021). In the Sahelian zone of West Africa, the Sahelian rainfall regime is characterised by a persistent deficit in the number of rainy days. At the same time, the frequency of extreme rainfall events has increased between 1970 and 2010 (Panthou et al., 2014). The proportion of annual precipitation associated with extreme precipitation increased from 17 % in 1970 to 1990, to 18.9 % in 1991 to 2000, and to 21 % in 2001 to 2010 (Panthou et al., 2014).

Sylla et al. (2016) suggest that West Africa will experience shorter rainy seasons, widespread arid and semi-arid conditions, longer dry spells and more intense extreme precipitation. In the face of this situation, smart agriculture has been identified by international organizations as a solution (Finizola et al., 2024). This is because it is a key strategy to ensure the sustainability of agricultural systems and to guarantee food security and nutrition in the context of a changing climate (Antwi and AntwiAgyei, 2023). Consequently, the issue is the subject of research in many countries around the world. Studies have been conducted in India (Kaur et al., 2023; Agarwal et al., 2022), Indonesia (Luckyardi et al., 2022) and Bangladesh (Hasn et al., 2018). In Africa, the majority of studies on climate-smart strategies have focused on East Africa. Studies focus on the drivers of smart agriculture adoption in Malawi (Shani et al., 2024), Ethiopia (Zeleke et al., 2024) and Kenya (Ndung'u et al., 2023). Other studies explore the impact of smart strategies on livelihoods (Tilahun et al., 20/23) and food security in South Africa (Abegunde et al., 2022). 

However, there are few studies in the Sahel region of West Africa, such as in Burkina Faso. Several studies in the north and south-west (Yanogo and Yaméogo, 2023), in the Mouhoun loop (Rouamba et al., 2023) and in the west (Sougoué et al., 2023) show an increase in extreme rainfall between 1980 and 2020. In urban areas, however, the situation will be critical, as extreme precipitation trends will increase over the period 2020-2040 (Yaméogo, 2024). The integration of smart strategies has become an important necessity for urban dwellers. In Burkina Faso's cities, people are opting to change their socio-economic activities, as in the city of Réo, in the province of Sanguié, in the centre-west of Burkina Faso. The town is crisscrossed by many low-lying areas. The inhabitants take advantage of these natural conditions to grow vegetables in the town. However, the variability of rainfall forces them to reorganize the cultivation systems on their plots (Yanogo, 2023). In order to cope with the current rainfall conditions, this situation forces the gardeners to adopt a variety of smart strategies in the garden plots. The main objective of the study is therefore to analyze the changes in rainfall in the area, the smart strategies used and the consequences in terms of food security of the strategies promoted in urban market gardening in Réo.

Reference

Abegunde VO, Sibanda M, Obi A. 2022. Effect of climate-smart agriculture on household food security in small-scale production systems: A micro-level analysis from South Africa. Cogent Social Sciences 8(1), 2086343. https://doi.org/10.1080/23311886.2022.2086343

Agalati B, Yabi JA. 2017. Déterminants de la sécurité alimentaire des ménages des terroirs riverains des zones cynégétiques des aires protégées du Nord-Bénin. Annales de l’Université de Parakou, Série « Sciences Naturelles et Agronomie » Hors-serie 1, 82-91.

Agarwal T, Goel PA, Gartaula H, Rai M, Bijarniya D, Rahut DB, Jat ML. 2022. Gendered impacts of climate-smart agriculture on household food security and labor migration: Insights from Bihar, India. International Journal of Climate Change Strategies and Management 14(1), 1-19.

Ajani SR, Adebukola BC, Oyindamola YB. 2006. Measuring household food insecurity in selected local government areas of Lagos and Ibadan, Nigeria. Pakistan Journal of Nutrition 5(1), 62-67.

Ali H, Menza M, Hagos F, Haileslassie A. 2022. Impact of climate-smart agriculture adoption on food security and multidimensional poverty of rural farm households in the Central Rift Valley of Ethiopia. Agriculture & Food Security 11(1), 1-16.

Anuga SW, Fosu-Mensah BY, Nukpezah D, Ahenkan A, Gordon C, Baye RS. 2022. Climate-smart agriculture: Greenhouse gas mitigation in climate-smart villages of Ghana. Environmental Sustainability 5(4), 457-469.

Atılgan A, Tanrıverdi C, Yücel A, Oz H, Degirmenci H. 2017. Analysis of long-term temperature data using Mann-Kendall trend test and linear regression methods: The case of the Southeastern Anatolia Region. Scientific Papers. Series A. Agronomy LX, 455-462.

Balram P, Fanal L. 2020. Meteorological drought assessment using standardized precipitation index for different agro-climatic zones of Odisha. Mausam 71(3), 467-480.

Belay A, Mirzabaev A, Recha JW, Oludhe C, Osano PM, Berhane Z, Solomon D. 2023. Does climate-smart agriculture improve household income and food security? Evidence from Southern Ethiopia. Environment, Development and Sustainability 1-28. https://doi.org/10.1007/s10668-023-03307-9

Coates J, Swindale A, Bilinsky P. 2007. Household Food Insecurity Access Scale (HFIAS) for measurement of food access: Indicator guide. Washington, DC: FANTA (Food and Nutrition Technical Assistance), FHI 360, p. 36.

El-Geziry TM. 2022. Analysis of air temperature trends as a climate change indicator for Alexandria (Egypt). Athens Journal of Sciences 9, 239-256. https://doi.org/10.30958/ajs.9-4-2

Erekalo KT, Yadda TA. 2023. Climate-smart agriculture in Ethiopia: Adoption of multiple crop production practices as sustainable adaptation and mitigation strategies. World Development Sustainability 3, 100099. https://doi.org/10.1016/j.wds.2023.100099

FAO (Food and Agricultural Organization). 2011. Guidelines for measuring household and individual dietary diversity. Prepared by Kennedy G, Ballard T, Dop M. FAO, p. 56.

Finizola e Silva M, Van Schoubroeck S, Cools J, Van Passel S. 2024. A systematic review identifying the drivers and barriers to the adoption of climate-smart agriculture by smallholder farmers in Africa. Frontiers in Environmental Economics 3, 1356335.

Hasan MK, Desiere S, D’Haese M, Kumar L. 2018. Impact of climate-smart agriculture adoption on the food security of coastal farmers in Bangladesh. Food Security 10, 1073-1088.

Hongbete F, Kindossi JM, Bio Bone B, Akissoe N, Hounhouigan JD, Nago MC. 2017. Evolution des habitudes alimentaires des Baatonu au Nord Bénin. Annales de l’Université de Parakou, Série « Sciences Naturelles et Agronomie » Hors-serie 1, 92-99.

Huluka AT, Wondimagegnhu BA. 2019. Determinants of household dietary diversity in the Yayo biosphere reserve of Ethiopia: An empirical analysis using the sustainable livelihood framework. Cogent Food & Agriculture 5(1), 1690829. https://doi.org/10.1080/23311932.2019.1690829

IPCC. 2021. Summary for policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte V, Zhai P, Pirani A, Connors SL, Péan C, Berger S, Caud N, Chen Y, Goldfarb L, Gomis MI, Huang M, Leitzell K, Lonnoy E, Matthews JBR, Maycock TK, Waterfield T, Yelekçi O, Yu R, Zhou B (eds.)]. In Press, p. 40.

IPCC. 2022. Annex II: Glossary [Möller V, van Diemen R, Matthews JBR, Méndez C, Semenov S, Fuglestvedt JS, Reisinger A (eds.)]. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Pörtner HO, Roberts DC, Tignor M, Poloczanska ES, Mintenbeck K, Alegría A, Craig M, Langsdorf S, Löschke S, Möller V, Okem A, Rama B (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, 2897-2930. https://doi.org/10.1017/9781009325844.029

Kharisma V, Abe N. 2020. Food insecurity and associated socioeconomic factors: Application of Rasch and binary logistic models with household survey data in three megacities in Indonesia. Social Indicators Research 148(2), 655-679.

Koala S, Nakoulma G, Dipama JM. 2023. Évolution des précipitations et de la température à l’horizon 2050 avec les modèles climatiques CMIP5 dans le bassin versant du Nakambé (Burkina Faso). International Journal of Progressive Sciences and Technologies (IJPSAT) 37(2), 110-124.

Lüning S, Gałka M, Danladi IB, Adagunodo TA, Vahrenholt F. 2018. Hydroclimate in Africa during the medieval climate anomaly. Palaeogeography, Palaeoclimatology, Palaeoecology 495, 309-322.

McKee TB, Doesken NJ, Kleist J. 1993. The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th Conference on Applied Climatology 17(22), 179-183.

Michiels P, Gabriels D, Hartmann R. 1992. Using the seasonal and temporal precipitation concentration index for characterizing the monthly rainfall distribution in Spain. Catena 19(1), 43-58. https://doi.org/10.1016/0341-8162(92)90016-5

Ndiaye M. 2014. Food security indicators, integrating nutrition and food security programs in emergency situations and for building resilience. Regional Training Workshop, 10-12 Juin 2014 Afrique de l’Ouest/Sahel – Saly, Sénégal, p. 24.

Ndung’u S, Ogema V, Thiga M, Wandahwa P. 2023. Factors influencing the adoption of climate-smart agriculture practices among smallholder farmers in Kakamega County, Kenya. African Journal of Food, Agriculture, Nutrition and Development 23(10), 24759-24782.

Nkoko N, Cronje N, Swanepoel JW. 2024. Factors associated with food security among small-holder farming households in Lesotho. Agriculture & Food Security 13(1), 1-10.

Nolan M, Rikard‐Bell G, Mohsin M, Williams M. 2006. Food insecurity in three socially disadvantaged localities in Sydney, Australia. Health Promotion Journal of Australia 17(3), 247-253.

Ogisi OD, Begho T. 2023. Adoption of climate-smart agricultural practices in sub-Saharan Africa: A review of the progress, barriers, gender differences, and recommendations. Farming System 1(2), 100019. https://doi.org/10.1016/j.farsys.2023.100019

Panthou G, Vischel T, Lebel T. 2014. Recent trends in the regime of extreme rainfall in the Central Sahel. International Journal of Climatology 34(15), 3998-4006. https://doi.org/10.1002/joc.3984

Rawat KS, Pa RK, Singh SK. 2021. Rainfall variability analysis using Precipitation Concentration Index: A case study of the western agro-climatic zone of Punjab, India. The Indonesian Journal of Geography 53(3), 388-399.

Roba KT, O’Connor TP, O’Brien NM, Aweke CS, Kahsay ZA, Chisholm N, Lahiff E. 2019. Seasonal variations in household food insecurity and dietary diversity and their association with maternal and child nutritional status in rural Ethiopia. Food Security 11, 651-664.

Rouamba S, Yaméogo J, Sanou K, Zongo R, Yanogo IP. 2023. Trends and variability of extreme climate indices in the Boucle du Mouhoun (Burkina Faso). GEOREVIEW: Scientific Annals of Stefan cel Mare University of Suceava. Geography Series 33(1), 70-84. https://doi.org/10.4316/GEOREVIEW.2023.01.07

Rusliyadi M, Ardi YWY, Winarno K. 2023. Binary logistics regression model to analyze factors influencing technology adoption process vegetable farmers case in Central Java Indonesia. In: Proceedings of the International Symposium Southeast Asia Vegetable, 460-470.

Salman M, Haque S, Hossain ME, Zaman N, Hira FTZ. 2023. Pathways toward the sustainable improvement of food security: adopting the household food insecurity access scale in rural farming households in Bangladesh. Research in Globalization 7, 100172.

Shani FK, Joshua M, Ngongondo C. 2014. Determinants of smallholder farmers’ adoption of climate-smart agricultural practices in Zomba, Eastern Malawi. Sustainability 16(9), 3782.

Sougué M, Merz B, Sogbedji JM, Zougmoré F. 2023. Extreme rainfall in southern Burkina Faso, West Africa: trends and links to Atlantic Sea surface temperature. Atmosphere 14(2), 284.

Sylla MB, Nikiema PM, Gibba P, Kebe I, Klutse NAB. 2016. Climate change over West Africa: recent trends and future projection. Adaptation to climate change and variability in rural West Africa. In: Yaro J., Hesselberg J. (eds) Adaptation to Climate Change and Variability in Rural West Africa. Springer, Cham, 25-40. https://doi.org/10.1007/978-3-319-31499-0_3

Tabe-Ojong PJ, Martin, Aihounton GB, Lokossou JC. 2023. Climate-smart agriculture and food security: cross-country evidence from West Africa. Global Environmental Change 81, 102697. https://doi.org/10.1016/j.gloenvcha.2023.102697

Tilahun G, Bantider A, Yayeh D. 2023. Analyzing the impact of climate-smart agriculture on household welfare in subsistence mixed farming system: evidence from Geshy Watershed, Southwest Ethiopia. Global Social Welfare 10(3), 235-247.

Yaméogo J, Ndoutorlengar M, Rouamba S. 2022. Perceptions of climate risks, socio-environmental impacts and adaptation strategies: the case of market gardeners in the lowlands of Nédialpoun, Zoula Village (Burkina Faso). IIARD International Journal of Geography and Environmental Management 8(2), 20-35. https://doi.org/10.56201/ijgem.v8.no2.2022.pg20.35

Yaméogo J, Rouamba S, Sanou K, Zongo R, Yanogo PI. 2023. Perception of extreme climatic events on bananas around the Petit Balè Dam in the Boromo Commune (Burkina Faso). European Journal of Science, Innovation and Technology 3(4), 209-223.

Yaméogo J, Sawadogo A. 2024. Consequences of precipitation variability and socio-economic activity on surface water in the Vranso Water Basin (Burkina Faso). Glasnik Srpskog Geografskog Drustva 104(1), 255-266. https://doi.org/10.2298/gsgd2401255y

Yaméogo J, Yanogo PI. 2023. Assessment of climate change adaptation strategies in developing countries: the case of Burkina Faso. Journal of Innovations and Sustainability 7(2), 1-32.

Yaméogo J. 2024. Trends and forecasts of extreme precipitation indices in three cities of Burkina Faso: between a non-parametric statistical analysis and Holt-Winters Smoothing Method. Discover Atmosphere, 2024 (In press).

Yanogo IP, Yaméogo J. 2023. Recent rainfall trends between 1990 and 2020: contrasting characteristics between two climate zones in Burkina Faso (West Africa). Glasnik Srpskog Geografskog Drustva 103(1), 87-106. https://doi.org/10.2298/GSGD2301087Y

Yanogo PI. 2023. Rainfall variability and changes in market gardening systems: a case study in Réo (mid-west region of Burkina Faso). Present Environment & Sustainable Development 17(2), 213-228. https://doi.org/10.47743/pesd2023172016

Zeleke G, Teshome M, Ayele L. 2024. Determinants of smallholder farmers’ decisions to use multiple climate-smart agricultural technologies in North Wello Zone, Northern Ethiopia. Sustainability 16(11), 4560.

Zhang Q, Berntell E, Li Q, Ljungqvist FC. 2021. Understanding the variability of the rainfall dipole in West Africa using the EC-Earth last millennium simulation. Climate Dynamics 57(1), 93-107.

Source : Impact of climate smart agriculture adoption on food security: The case of urban market gardeners inthe city of Réo, Burkina Faso

 

Read More

Mango Varieties of Burkina Faso: Properties & Potential Uses | InformativeBD

 

Hyacinthe Kanté-Traoré,  Marie Dufrechou , Dominique Le Meurlay , Vanessa Lançon-Verdier , Hagrétou Sawadogo-Lingani , and Mamoudou H. Dicko from the different institute of the Burkina Faso and France wrote a research article about, Mango Varieties of Burkina Faso: Properties & Potential Uses entitled,"Physicochemical properties and potential use of six mango varieties from Burkina Faso" this research paper published by the International Journal of Biosciences| IJB an open access scholarly research journal on Biology, under the affiliation of the International Network For Natural Sciences | INNSpub, an open access multidisciplinary research journal publisher. 

Abstract:

The quality profile of the six most important mango varieties from Burkina Faso, as well as their potential use, was investigated. It appeared that Amélie variety showed the highest levels of total sugars (74.49±0.04 % dry weight), β-carotene (1752.72±41.64 µg/100 g of fresh weight), vitamin C (58.94±1.77 mg/100 g of fresh weight), titratable acidity (1.56±0.01 %), and energy value (80.55±0.01 Kcal/100 g dry weight). However, this variety has the lowest soluble solids/titratable acidity ratio (TSS/TA) of 11.24±0.17 and the lowest total fiber content (1.87±0.03 % fresh weight). Kent variety contained the highest levels of pulp (81.31±1.67 % fresh weight), total soluble solids (23.1±0.00 % fresh weight), total fiber content (2.77±0.08 % fresh weight) and the lowest β-carotene content (220.21±14.97 µg/100 g fresh weight). All varieties have significant levels of total phenolic compounds (mini-maxi content). This study not only showed significant differences in biochemical contents and physical characteristics among mango varieties but will also guide mango processors and nutritionists in choosing the most suitable varieties according to target food products.

Read more: Fighting ChestnutBlight: Antagonistic Microscopic Fungi | InformativeBD

Introduction

Mango (Mangifera indica L.) fruit is among the main speculation of the fruit sector in most tropical countries worldwide. In Burkina Faso, its cultivation covers 58 % of the orchards and its production is estimated to be 56 % of the annual all fruits production (APROMAB, 2016). The annual production of mango has increased from 337 101 tons in 2008 to 404 400 tons in 2014 (Ouédraogo et al.,2017) with an operating area of 33,700 hectares. About forty varieties of mango are available in Burkina Faso. Among these, varieties of mango trees identified in the orchards in the largest production area (Comoé, Kénédougou and Houet provinces located in western Burkina Faso) are Brooks (29.61%), Lippens (25.15 %), Amélie (18.96 %), Kent (18.65%), Keitt (5.52 %) and Springfield (2.07 %) (Guira,2008). The distribution of mango varieties in orchards has been studied by Rey et al. (2004). This distribution varied according to the area. For instance, in the province of Houet, the Amélie variety represents a high proportion of the mangoes produced compared to the colored varieties (Kent, Keitt). Amélie variety occupied 40 to 50 % of the cultivated land (PAFASP, 2011). The production yield is around 150 to 200 kg of fruit/tree for Brooks and Lippens varieties, 100 to 150 kg of fruit/tree for Amélie, Keitt and Kent varieties and 50 to 100 kg of fruit/tree for Springfield variety (Guira, 2008). In addition to the high agricultural potential of mango, the pulp of this fruit has a high nutritional value. Indeed, it is an excellent source of β-carotene (provitamin A), vitamin C, carbohydrates, fibers, phenolic compounds and minerals (Robles-Sanchez et al.,2009; Ma et al., 2011; Liu et al., 2013; Somé et al.,2014). Levels of these compounds are significantly different among varieties and are dependent on agronomic, climatic and environmental conditions. Furthermore, mangos being a climacteric fruit, the degree of maturity, growing conditions and storage conditions highly influence the levels of these metabolites. Secondary metabolites are powerful antioxidants that reduce oxidative stress and have anti-cancer properties ((Kim et al., 2003; Chiou et al.,2007). For instance, carotenoids play an important role in human health by acting as a source of provitamin A or as protective anti-oxidants necessary for good reproduction and growth, in the normal operation of the eye system, and in the integrity of epithelial cells and the functionality of the immune system (Murkovic et al., 2002).

Studies on mango varieties from Burkina Faso with respect to diseases and pest attacks have been previously performed (Vayssieres et al., 2008;Ouédraogo, 2011). Moreover, Amélie fruit variety has been studied on aspects related to technological valorization, chemical composition and nutritional value and on the storage effects on vitamin C, carotenoids and browning (Sawadogo-Lingani and Traoré, 2001, 2002a; Sawadogo-Lingani et al., 2002;Sawadogo-Lingani et al, 2005). Other studies dealt with methods of producing unconventional food for pigs based on mango wastes in Burkina Faso(Kiendrebeogo et al., 2013). The drying technology ofthe Brooks variety has been studied and the physicochemical, biochemical, and technological characterization of the different existing mango varieties were determined (Rivier et al., 2009). Yet, without the knowledge and mastery of these characteristics, qualitative and standardized processing cannot be achieved. Production of puree ,concentrates and beverages from mango require specific characteristics, where the ratio of total sugar content/acid (TSS/TA) associated with an intense yellow-orange color and soft texture play major roles. On the other hand, the production of dried mangoes, frozen or canned fruit pieces requires firmer fruits for which color is important. The present work aims to determine the biochemical characteristics of the six most exported and most processed mango varieties in Burkina Faso. The characterization of these six varieties will allow proposing of appropriate use technologies for each variety.

Reference

Arias R, Lee TC, Logendra L, Janes H. 2000. Correlation of lycopene measured by HPLC with the L*, a*, b* color readings of a hydroponic tomato and the relationship of maturity with color and lycopene content. Journal of Agricultural and Food Chemistry 48, 1697–1702.

Association Interprofessionnelle Mangue Du Burkina (APROMAB). 2016. Rapport de l’atelier bilan de la campagne mangue 2016 de la commercialisation de la mangue. Bobo-Dioulasso, novembre 2016. 28 p.

Bafodé BS. 1988. Projet de transformation et de conditionnement des mangues à Boundiali en Côte d’Ivoire. SIARC (Section des ingénieurs alimentaires/région chaude Montpellier 87 p.

Champ M, Langkilde AM, Brouns F, Kettlitz B, Le Bail Collet Y. 2003. Advances in dietary fibre characterisation. 1. Definition of dietary fibre, physiological relevance, health benefits and analytical aspects. Nutrition Research Reviews 16, 71–82. http://dx.doi.org/10.1079/NRR200254

Chiou A, Karathanos VT, Mylona A, Salta FN, Preventi F, Andrikopoulos NK. 2007. Currants (Vitis vinifera L.) content of simple phenolics and antioxidant activity. Food Chemistry 102, 516-522.

Cocozza FM, Jorge JT, Alves RE, Filgueiras HAC, Garruti DS, Pereira MEC. 2004. Sensory and physical evaluations of cold stored ‘Tommy Atkins’ mangoes influenced by 1-MCP and modified atmosphere packaging. Acta Horticulture 645, 655–661.

D’Souza MC, Singha S, Ingle M. 1992. Lycopene concentration of tomato fruit can be estimated from chromaticity values. Hort Science 27, 465–466.

Dubois M, Gilles KA, Hamilton JK, Rebers PA, Smith F. 1956. Colorimetric method for determination of sugars and related substances. Analytical Chemistry 28, 350–356.

Elsheshetawy HE, Mossad A, Elhelew WK, Farina V. 2016. Comparative study on the quality characteristics of some Egyptian mango varieties used for food processing. Annals of Agricultural Science 61(1), 49–56.

Frenich AG; Torres MH, Vega AB, Vidal JM, Bolanos, PP. 2005. Determination of ascorbic acid and carotenoids in food commodities by liquid chromatography with mass spectrometry detection. Journal of Agricultural and Food Chemistry 53, 7371-7376.

Gonzalez-Aguilar GA, Buta JG, Wang CY. 2001. Methyl jasmonate reduces chilling injury symptoms and enhances color development of ‘Kent’ mangoes. Journal of Agricultural 81, 1244–1249.

Grundy MML, Edwards CH, Mackie AR, Gidley MJ, Butterworth PJ, and Ellis PR. 2016. Re-evaluation of the mechanisms of dietary fiber and implications for macronutrient bioaccessibility, digestion and postprandial metabolism. British Journal of Nutrition 116(5), 816-33.

Guira M. 2008. Description des principales variétés de manguiers cultivées au Burkina Faso. Fiche technique N°2.

Hernández Y, Lobo MG, González M. 2006. Determination of vitamin C in tropical fruits: A comparative evaluation of methods. Food Chemistry 96, 654-664.

Hirschler R. 2012. Whiteness, Yellowness and Browning in Food Colorimetric In: Color in Food. Technological and Psychophysical Aspects. Edited by José Luis Caivano and María Del Pilar Buera. CRC Press, Taylor & Francis Group, Boca Raton, FL: 2012.

Hu W, Jiang Y. 2007. Quality attributes and control of fresh-cut produce. Stewart Postharvest Rev, 3, 1-9.

Ibarra-Garza IP, Ramos-Parra PA, Hernández-Brenes C, Jacobo-Velázquez DA. 2015. Effects of postharvest ripening on the nutraceutical and physicochemical properties of mango (Mangifera indica L. cv Keitt) Postharvest Biology and Technology 103, 45–54.

Jha SN, Chopra S, Kingsly ARP. 2007. Modeling of color values for non-destructive evaluation of maturity of mango. Journal of Food Engineering 78, 22–26.

Kim D, Jeong SW, Lee CY. 2003. Antioxidant capacity of phenolic phyto-chemicals from various cultivars of plums. Food Chemistry 81, 321-326.

Kiendrebeogo T, Mopate Logtene Y, IDO G, kabore-Zoungrana CY. 2013. Procédés de production d’aliments non conventionnels pour porcs à base de déchets de mangues et détermination de leurs valeurs alimentaires au Burkina Faso. Journal of Applied Biosciences 67, 5261–5270 ISSN 1997–5902.

Kothalawala SG, Jayasinghe JMJK. 2017. Nutritional Evaluation of Different Mango Varieties available in Sri Lanka. International Journal of Advanced Engineering Research and Science (IJAERS), 4(7), ISSN: 2349-6495(P)/2456-1908(O). https://dx.doi.org/10.22161/ijaers.4.7.20

Liu FX, Fu SF, Bi XF, Chen F, Liao XJ, Hu XS, Wu JH. 2013. Physicochemical and anti-oxidant properties of four mango (Mangifera indica L.) varieties in China. Food Chemistry 138, 396–405.

Ma X, Wu H, Liu L, Yao Q, Wang S, Zhan R, Xing S, Zhou Y. 2011. Polyphenolic compounds and anti-oxidant properties in mango fruits. Scientia Horticulturae, 129, 102–107.

Mahayothee B, Muhlbaue W, Neidhart S, Carle R. 2004. Influence of postharvest ripening process on appropriate maturity drying mangoes ‘Nam Dokmai’ and ‘Kaew’. Acta Horticulture 645, 241–248.

Malundo TMM, Shewfelt RL, Ware GO, Baldwin EA. 2001. Sugars and Acids Influence Flavor Properties of Mango (Mangifera indica). Journal of the American Society for Horticultural Science 126(1), 115–121.

Medlicott AP, Thompson AK. 1985. Analysis of Sugars and Organic Acids in Ripening Mango Fruits (Mangifera indica L. var Keitt) By High Performance Liquid Chromatography Journal of the Science of Food and Agriculture 36, 561-566.

Murkovic M, Mulleder U, Neunteufl H. 2002. Carotenoid Content in Different Varieties of Pumpkins. Journal of Food Composition and Analysis 15, 633–638. https://dx.doi.org/10.1006/jfca.2002.1052.   http://www.idealibrary.com

Ornelas-Paza JDJ, Yahiab EM. 2014. Effect of the moisture content of forced hot air on the postharvest quality and bioactive compounds of mango fruit (Mangifera indica L. cv. Manila). Journal of the Science of Food and Agriculture 94, 1078–1083.

Ouédraogo N, Boundaogo M, Drabo A, Savadogo R, Ouattara M, Dioma EC, Ouédraogo M. 2017. Guide de la transformation de la mangue par le séchage au Burkina Faso. UNMO/CIR, SNV, PAFASP, décembre 2017. 56 p.

Ouédraogo SN. 2011. Dynamique spatio temporelle des mouches des fruits (diptera, tephritidae) en fonction des facteurs biotiques et abiotiques dans les vergers de manguiers de l’Ouest du Burkina Faso. Thèse de doctorat spécialité écophysiologie. Université Paris Est, Ecole doctorale science de la vie et de la santé, 184 p.

Padda MS, Amarante CVT, Garciac RM, Slaughter DC, Mitchama EJ. 2011. Methods to analyze physicochemical changes during mango ripening: A multivariate approach. Postharvest Biology and Technology 62, 267-274.

Passannet AS, Aghofack-Nguemezi J, Gatsing D. 2018. Variabilité des caractéristiques physiques des mangues cultivées au Tchad: caractérisation de la diversité fonctionnelle. Journal of Applied Biosciences 128, 12932 -12942. ISSN 1997-5902.  https://dx.doi.org/10.4314/jab.v128i1.6

Programme d’Appui aux Filières Agro-Sylvo-Pastorales (PAFASP). 2011. Analyse des chaines de valeur ajoutée des filières Agro-Sylvo-Pastorales: bétail/viande, volaille, oignon et mangue. CAPES, rapport définitif, 212 p.

Renard CMGC. 2005b. Variability in cell wall preparations: Quantification and comparison of common methods. Carbohydrate Polymers 60, 515–522.

Rivier M, Méot JM, Ferré T, Briard M. 2009. Le séchage des mangues Éditions Quæ, CTA, e-ISBN (Quæ) : 978-2-7592-0342-0 ISBN (CTA): 978-92-9081-421-4.

Robles-Sánchez RM, Rojas-Graüb MA, Odriozola-Serrano I, González-Aguilara GA, Martín-Belloso O. 2009. Effect of minimal processing on bioactive compounds and anti-oxidant activity of fresh-cut ‘Kent’ mango (Mangifera indica L.). Postharvest Biology and Technology 51, 384–390.

Sajib MAM, Jahan S, Islam MZ, Khan TA, Saha BK. 2014. Nutritional evaluation and heavy metals content of selected tropical fruits in Bangladesh. International Food Research Journal 21(2), 609-615.

Sawadogo-Lingani H, Traoré SA. 2001. Critères d´appréciation de la maturité physiologique de la variété de mangue Amélie du Burkina Faso. Sciences et Techniques, série Sciences Naturelles et Agronomie 25(1),  60-71.

Sawadogo-Lingani H, Traoré SA. 2002a. Composition chimique et valeur nutritive de la mangue Amélie (Mangifera indica L.) du Burkina Faso.2002. Journal des Sciences 2(1), 35-39.

Sawadogo-Lingani H, Thiombiano G, Traoré SA. 2002. Effets des prétraitements et du séchage solaire sur la vitamine C, les caroténoïdes et le brunissement de la mangue. Sciences et Techniques, série Sciences de la santé 25(2), 75-88.

Sawadogo-Lingani H, Thiombiano G, Traoré SA. 2005. Effets du stockage sur la vitamine C, les caroténoïdes et le brunissement de la mangue Amélie séchée. Revue CAMES, Série A, Sciences et Médecine, 3, 62-67.

Singleton VL, Orthofer R, Lamuela-Raventos RM. 1999. Analysis of total phenols and other oxidation substrates and anti-oxidants by means of Folin-Ciocalteu reagent. Methods Enzymol. 299, 152–178.

Somé TI, Sakira AK, Tamimi ED. 2014. Determination of β-carotene by High Performance Liquid Chromatography in Six Varieties of Mango (Mangifera indica L) from Western Region of Burkina Faso American Journal of Food and Nutrition 2(6), 95-99.

Vayssieres JF, Korie S, Coulibaly T, Temple L, Boueyi S. 2008.The mango tree in northern Benin (1), variety inventory, yield assessment, early infested stages of mangos and economic loss due to the fruit fly (Diptera Tephritidae). Fruits, 63, 1-22.

Vasquez-Caicedo AL, Neidhart S, Carle R. 2004. Postharvest ripening behavior of nine Thai mango varieties and their suitability for industrial applications. Acta Hortic. 645, 617–625.

Veda S, Platel K, Srinivasan K. 2007. Varietal Differences in the Bioaccessibility of β-Carotene from Mango (Mangifera indica) and Papaya (Carica papaya) Fruits. Journal of Agricultural and Food Chemistry 55, 7931–7935.

West C, Castenmiller JJM. 1998. Quantification of the ‘SLAMENGHI’ factors for carotenoid bioavailability and bioconversion. International Journal for Vitamin and Nutrition Research 68, 371–377.

Wongmetha O, Ke LS, Liang YS. 2015. The changes in physical, bio-chemical, physiological characteristics and enzyme activities of mango cv. Jinhwang during fruit growth and development. NJAS – Wageningen Journal of Life Sciences 72–73, 7–12.

Source: Physicochemicalproperties and potential use of six mango varieties from Burkina Faso

Read More