Search Results - Sliven~

Integrated management of multiple water sources for multiple uses: rural communities in Limpopo Province, South Africa by van Koppen, Barbara, Hofstetter, Moritz, Nesamvuni, A. E., Chiluwe, Q.

“…A last potential policy implication regards community-driven planning, design and construction of water infrastructure according to people’s priorities. …”
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The future of zoonotic risk prediction by Carlson, C.J., Farrell, M.J., Grange, Z., Han, B.A., Mollentze, N., Phelan, A.L., Rasmussen, A.L., Albery, G.F., Bett, Bernard K., Brett-Major, D.M., Cohen, L.E., Dallas, T., Eskew, E.A., Fagre, A.C., Forbes, K.M., Gibb, R., Halabi, S., Hammer, C.C., Katz, R., Kindrachuk, J., Muylaert, R.L., Nutter, F.B., Ogola, J., Olival, K.J., Rourke, M., Ryan, S.J., Ross, N., Seifert, S.N., Sironen, T., Standley, C.J., Taylor, K., Venter, M., Webala, P.W.

“…To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. …”
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A cross-sectional survey of the knowledge, attitudes, and practices of antimicrobial users and providers in an area of high-density livestock-human population in western Kenya by Kemp, S.A., Pinchbeck, G.L., Fèvre, Eric M., Williams, N.J.

“…Background: Antimicrobial resistance (AMR) is one of the most important global health crises in recent times and is driven primarily by antimicrobial consumption. In East Africa, there is a paucity of data regarding the knowledge, attitudes, and practices (KAP) related to antimicrobial use (AMU). …”
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Monitoring biophysical and socioeconomic impacts of CSA practices at Doyogena and Basona Climate-Smart Landscapes, Ethiopia by Nigussie, Abebe, Ambaw, Gebermedihin, Tesfaye, Abonesh

“…At Basona, on the other hand, the impact of seven CSA options was evaluated, namely, (i) terrace (soil bunds); (ii) terraces coupled with phalaris and tree lucerne); (iii) trenches; (iv) enclosure; (v) percolation pits; (vi) check-dams; and (vii) gully rehabilitation.…”
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Evaluation of GRACE derived groundwater storage changes in different agro-ecological zones of the Indus Basin by Akhtar, F., Nawaz, R. A., Hafeez, Mohsin, Awan, Usman Khalid, Borgemeister, C., Tischbein, B.

“…The GWSA’s poor correlation with the in-situ measurements particularly in the mountainous region of the KRB is driven by the 4 months lag time unlike in the LBDC (i.e. 3 months); besides, the observations wells are sparse and limited. …”
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Oil palm and gendered time use: A mixed-methods case study from West Kalimantan, Indonesia by Rowland, D., Zanello, G., Waliyo, E., Ickowitz, A.

“…We find that relative to non-oil-palm adopting swidden farmers, participation in oil palm plasma schemes is associated with more time spent in productive labour for both men and women, driven by off-farm labour on oil palm plantations. …”
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Effectively targeting climate investments: A methodology for mapping climate–agriculture–gender inequality hotspots by Koo, Jawoo, Azzarri, Carlo, Mishra, Avni, Lecoutere, Els, Puskur, Ranjitha, Chanana, Nitya, Singaraju, Niyati, Nico, Gianluigi, Khatri-Chhetri, Arun

“…Women are at a particular disadvantage, given their lower adaptive capacity due to unequal access to productive resources and services, driven by deeply entrenched social and gender norms and other structural barriers. …”
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NIR instruments and prediction methods for rapid access to grain protein content in multiple cereals by Chadalavada, K., Anbazhagan, K., Ndour, A., Choudhary, S., Palmer, W., Flynn, J.R., Mallayee, S., Sharada, Pothu, Prasad, Kodukula V.S.V., Varijakshapanicker, Padmakumar, Jones, Christopher S., Kholová, Jana

“…We explored classical deterministic methods (via winISI, FOSS), novel machine learning (ML)-driven methods (via Hone Create, Hone), and a convolutional neural network (CNN)-based method for building the calibrations to predict grain protein out of the NIR spectra. …”
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Machine learning model accurately predict maize grain yields in conservation agriculture systems in southern Africa by Muthoni, Francis K., Thierfelder, Christian L., Mudereri, B.T., Manda, J., Bekunda, Mateete A., Hoeschle-Zeledon, Irmgard

“…Integration of machine learning (ML) and free remotely sensed big data have opened huge opportunities for data-driven insights into complex problems in agriculture. …”
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Country-specific challenges to improving effectiveness, scalability and sustainability of agricultural climate services in Africa by Hansen, James, Born, Lorna, Dossou-Yovo, Elliott Ronald, Mwongera, Caroline, Dalaa, Mustapha Alasan, Tahidu, Osman, Whitbread, Anthony M., Solomon, Dawit, Zougmoré, Robert B., Zebiak, Stephen E., Dinku, Tufa, Grossi, Amanda

“…These differences have been driven largely by differing national policies, delivery capacity and external actors, but not by responsiveness to agricultural sector demands. …”
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ADT: The automatic weather station data tool by Faniriantsoa, Rija, Dinku, Tufa

“…To address these data gaps, efforts over the last decade, largely driven by external donor funding, have focused on expanding meteorological observation networks in many parts of Africa, mainly through the provision of Automatic Weather Stations (AWS) to National Meteorological Services (NMS). …”
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Mapping the spatial distribution of underutilised crop species under climate change using the MaxEnt model: a case of KwaZulu-Natal, South Africa by Mugiyo, H., Chimonyo, Vimbayi Grace Petrova, Kunz, R., Sibanda, M., Nhamo, L., Masemola, C. R., Modi, Albert Thembinkosi, Mabhaudhi, Tafadzwanashe

“…The future distribution of NUS was simulated using a maximum entropy (MaxEnt) model using regional circulation models (RCMs) from the CORDEX archive, each driven by a different global circulation model (GCM), for the years 2030 to 2070. …”
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Identification of genomic regions associated with agronomic and disease resistance traits in a large set of multiple DH populations by Sadessa, Kassahun, Beyene, Yoseph, Ifie, Beatrice E., Mahabaleswara, Suresh L., Olsen, Michael, Ogugo, Veronica, Wegary, Dagne, Tongoona, Pangirayi, Danquah, Eric, Offei, Samuel Kwame, Boddupalli, P.M., Gowda, Manje

“…Genome-wide association study (GWAS) using a mixed linear FarmCPU model identified SNPs associated with the studied traits i.e., about seven and eight SNPs for the grain yield; 16 and 12 for anthesis date; seven and eight for anthesis silking interval; 14 and 5 for both ear and plant height; and 15 and 5 for moisture under both WW and WS environments, respectively. …”
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Mapping spatial distribution and geographic shifts of east African highland banana (Musa spp.) in Uganda by Ochola, D., Boekelo, B., Ven, G.W. van de, Taulya, G., Kubiriba, Jerome, Asten, Piet J.A. van, Giller, Kenneth E.

“…The maps of spatial distribution and geographic shift of banana can support targeting of context-specific intensification options and policy advocacy to avert agriculture driven environmental degradation.…”
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Rwanda’s agrifood system: Structure and drivers of transformation by Diao, Xinshen, Ellis, Mia, Mugabo, Serge, Pauw, Karl, Rosenbach, Grace, Spielman, David J., Thurlow, James

“…The growth diagnostic in this paper reveals that it is domestic markets that have driven the recent growth in Rwanda’s AFS other than exports. …”
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Socio-economic determinants of land use and land cover change in South-Kivu wetlands, eastern D.R. Congo: Case study of Hogola and Chisheke wetlands by Chuma, G.B., Mondo, J.M., Sonwa, D.J., Karume, K., Mushagalusa, G.N., Schmitz, S.

“…The conversion of wetlands into farmlands was driven by annual household income, wetland utilization patterns, households' main activity and the seniority in exploiting wetlands. …”
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TaGSNE, a WRKY transcription factor, overcomes the trade-off between grain size and grain number in common wheat and is associated with root development by Khan, Nadia, Yanfei Zhang, Jingyi Wang, Yuying Li, Xin Chen, Lili Yang, Jie Zhang, Chaonan Li, Long Li, Shoaib-ur-Rehman, Reynolds, Matthew P., Lichao Zhang, Zhang, Xueyong, Xinguo Mao, Ruilian Jing

“…In this study, we identify the WRKY gene TaGSNE (Grain Size and Number Enhancer) in common wheat, and find that it has relatively high expression in leaves and roots, and is induced by multiple abiotic stresses. Eleven single-nucleotide polymorphisms were identified in TaGSNE, forming two haplotypes in multiple germplasm collections, named as TaGSNE-Hap-1 and TaGSNE-Hap-2. …”
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Kenya's agrifood system: Structure and drivers of transformation by Xinshen Diao, Pauw, Karl, Smart, Jenny, Thurlow, James

“…The analysis further reveals that it is the domestic market, not exports, that has driven the recent growth in Kenya’s AFS. Rapid urbanization and increased incomegenerating opportunities in the rural nonfarm sector are causing dietary patterns to shift, which will continue to shape the transformation of the AFS in Kenya. …”
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Soil greenhouse gas emissions from a sisal chronosequence in Kenya by Wachiye, Sheila, Merbold, Lutz, Vesala, Timo, Rinne, Janne, Leitner, Sonja, Räsänen, Matti, Vuorinne, Ilja, Heiskanen, Janne, Pellikka, Petri

“…The effects of stand age on Fs were examined using static GHG chambers and gas chromatography for a period of one year in seven stands: young stands aged 1–3 years, mature stands aged 7–8 years, and old stands aged 13–14 years. …”
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GWAS identifies genetic loci underlying nitrogen responsiveness in the climate resilient C4 model Setaria italica (L.) by Bandyopadhyay, Tirthankar, Swarbreck, Stéphanie M., Jaiswal, Vandana, Maurya, Jyoti, Gupta, Rajeev, Bentley, Alison R., Griffiths, Howard, Prasad, Manoj

“…Results: Our study show that N dependent yield rise in S. italica is driven by grain number whose responsiveness to N availability is genetically underlined. …”
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