Varieties with diverse maturity class,
Striga and drought-tolerant maize varieties
Soil fertility management technologies
Good agronomic practices e.g. planting dates
5th International Disaster and Risk Conference IDRC 2014 Integrative Risk Management - The role of science, technology & practice 24-28 August 2014 in Davos, Switzerland
Yam has the advantage of utilizing the nutrient reserve accumulated when the soil is rested. Limited knowledge exists on the nutrient uptake patterns of the D. rotundata grown under field conditions. This study examined the effect of fertilizer application on growth pattern and yield of yam.
Improved upland rice technology effect on environment protection - Experience...ExternalEvents
http://www.fao.org/about/meetings/agroecology-symposium-china/en/
Preseentation of Feng Lu, from Yunnan Academy of Agricultural Sciences, on the effect of improved upland rice technology on environment protection. Experiences are shown from the mountainous area of Southern Yunnan, China. The presentation was prepared and delivered in occasion of the International Symposium on Agroecology in China, held in Kunming, China on 29-31 August 2016.
Varieties with diverse maturity class,
Striga and drought-tolerant maize varieties
Soil fertility management technologies
Good agronomic practices e.g. planting dates
5th International Disaster and Risk Conference IDRC 2014 Integrative Risk Management - The role of science, technology & practice 24-28 August 2014 in Davos, Switzerland
Yam has the advantage of utilizing the nutrient reserve accumulated when the soil is rested. Limited knowledge exists on the nutrient uptake patterns of the D. rotundata grown under field conditions. This study examined the effect of fertilizer application on growth pattern and yield of yam.
Improved upland rice technology effect on environment protection - Experience...ExternalEvents
http://www.fao.org/about/meetings/agroecology-symposium-china/en/
Preseentation of Feng Lu, from Yunnan Academy of Agricultural Sciences, on the effect of improved upland rice technology on environment protection. Experiences are shown from the mountainous area of Southern Yunnan, China. The presentation was prepared and delivered in occasion of the International Symposium on Agroecology in China, held in Kunming, China on 29-31 August 2016.
Field experiments were conducted at the University of Ilorin Teaching and Research Farm in 2005 and
2006 cropping seasons with the objective to evaluate the combining ability for maize grain yield and
other agronomic characters in 10 open pollinated maize varieties, which have been selected for high
yield and stress tolerance. General combining ability (gca) and year (y) effects were significant for all
the parameters except plant height, while specific combining ability (sca) and gca x year effects were
significant only for grain yield. However, Tze Comp4 Dmr Srbc2, Tze Comp4 C2 and Acr 94 Tze Comp5
which are good general combiners for maize grain yield, also showed positive significant gca x year
effects for flowering traits. Significant sca x year interaction effects were recorded for maize grain yield
and days to flowering, with Hei 97 Tze Comp3 C4 combining very well with 3 parents (Acr 90 Pool 16-Dt,
Tze Comp4-Dmr Srbc2 and Tze Comp4 C2). These parents and their hybrids probably have genes that
can be introgressed into other promising lines in developing early maturing and high yielding varieties
for cultivation in the Nigeria savannas.
Bangladesh though a LDC have generated national database on Land and Soil Resources which is mainly used for agricultural development planning and farmers service. SOLARIS and OFRS are two systems dealing with the NR database to generate output needed by academicians, researchers, extentionists and farmers
Research and transfer of Double-high technology based on agroecological princ...FAO
Presentation from Fusuo Zhang from Center for Resources, Environment and Food Security at China Agricultural University on technology transfer in agroecological farming approaches, in the framework of agricultural production in China. The presentation was prepared and delivered in occasion of the International Symposium on Agroecology for Food Security and Nutrition, held at FAO in Rome on 18-19 September 2014.
Maize grain yield is greatly constrained by the parasitic weed, Striga hermonthica. Levels of infestation are often so high that
maize can suffer total yield loss and farmers usually abandon severely infested fields.
Oral presentation made at the 19th European Association for Potato Research (EAPR) Conference held in Brussels on 7-11 July 2014. It is about a Potato Yield Gap Analysis study for Sub Saharan Africa through Participatory Modeling being conducted by the International Potato Center (CIP).
Technical Efficiency Differentials and Resource - Productivity Analysis amon...researchagriculture
The importance of soybean as a high protein, primary input in vegetable oil,
diary and feed industries is not in doubt. The technical efficiency and
resource
-
productivity of smallholder soybean farmers in Benue State, Nigeria were
estimated using cross sectional data obtained on 96 soybean farmers in the empirical
analysis. Results obtained with transcendental logarithmic (translog) stochastic
frontier model showed that the technical efficiencies varied widely from
0.254 to 0.999 with a mean of 0.718. This indicates that smallholder soybean
production was in the irrational stage of production (stage III) as depicted by the
returns
-
to
-
scale (RTS) of
-
2.848. Land and fertilizer were effectively allocated and
used, as confirmed by each variable having estimated coefficient value between zero
and unity, depicting stage II in the production curve. The productivity of the factors
can be enhanced by expanding the farm size at the existing level of labour so that the
variable of labour used could move from stage III to stage II in the production curve.
Labour saving resource and/or practices should be encouraged for productivity and
technical efficiency to be enhanced.
Conservation agriculture, livestock and livelihood strategies in the Indo-Ga...ILRI
Presentation by Olaf Erenstein, Nils Teufel & Arindam Samaddar (CIMMYT) to the CGIAR Systemwide Livestock Programme Livestock Policy Group Meeting, 1 December 2009
Transforming Maize-legume Value Chains –A Business Case for Climate-Smart Ag...CIMMYT
CIMMYT Senior Cropping Systems Agronomist Christian Thierfelder presented on climate-smart agriculture in southern Africa in a webinar titled Climate Resilient Agriculture Success Stories – Making a Case for Scale Up.
Genetic Variability for Grain Yield, Flowering and Ear Traits in Early and La...Premier Publishers
Field experiments were carried out to estimate genetic variation in full – sib families of a sweet corn population during the early and late sowing dates of 2012 at the Federal University of Agriculture, Makurdi, Nigeria. Experiments were laid out as lattice but analysed using Randomised Complete Block Design (RCBD) with three replications. Families differed significantly (P < 0.01) for all the traits (days to anthesis, days to silking, anthesis – silking interval (ASI), ear traits of ears/plant, cob length, kernel rows/cob and kernels/row on cob and grain yield), indicating genetic variability for these traits. The Genotypic Coeffient of Variation (GCV), Phenotypic Coefficient of Variation (PCV) and Genetic Advance (GA) were higher for ASI compared to days to anthesis and silking, but with similar range for the ear traits. Heritability was however moderate for ASI and high for days to anthesis and silking. The higher heritability and GA observed for most of the traits in the late sowing date is an indication that progress in selection will be faster during this date. Full-sib recurrent selection for grain yield via ear traits should therefore be used to improve the local sh2 sweet corn population in the Southern Guinea Savanna of Nigeria.
Trends and Determinants of Cereal Productivity: Econometric ANalysis of Natio...essp2
Ethiopian Development Research Institute (EDRI) and International Food Policy Research Institute (IFPRI), Seventh International Conference on Ethiopian Economy, June 24, 2010
Empirical EO based approach to wheat yield forecasting and its adaptation wit...CIMMYT
Remote sensing –Beyond images
Mexico 14-15 December 2013
The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)
Research in sustainable intensification in the sub-humid maize-based cropping...africa-rising
Presented by Mateete Bekunda (IITA), Ben Lukuyu (ILRI), Danny Coyne (IITA), Dan Makumbi (CIMMYT), Jean Claude Rubyogo (CIAT), Job Kihara (CIAT), Fen Beed (IITA), Adebayo Abass (IITA), Stephen Lyimo (SARI), Victor Afari-Sefa (AVRDC) and Festo Ngulu (IITA) at the Africa RISING East and Southern Africa annual review and planning meeting, Lilongwe, Malawi, 3-5 September 2013
Field experiments were conducted at the University of Ilorin Teaching and Research Farm in 2005 and
2006 cropping seasons with the objective to evaluate the combining ability for maize grain yield and
other agronomic characters in 10 open pollinated maize varieties, which have been selected for high
yield and stress tolerance. General combining ability (gca) and year (y) effects were significant for all
the parameters except plant height, while specific combining ability (sca) and gca x year effects were
significant only for grain yield. However, Tze Comp4 Dmr Srbc2, Tze Comp4 C2 and Acr 94 Tze Comp5
which are good general combiners for maize grain yield, also showed positive significant gca x year
effects for flowering traits. Significant sca x year interaction effects were recorded for maize grain yield
and days to flowering, with Hei 97 Tze Comp3 C4 combining very well with 3 parents (Acr 90 Pool 16-Dt,
Tze Comp4-Dmr Srbc2 and Tze Comp4 C2). These parents and their hybrids probably have genes that
can be introgressed into other promising lines in developing early maturing and high yielding varieties
for cultivation in the Nigeria savannas.
Bangladesh though a LDC have generated national database on Land and Soil Resources which is mainly used for agricultural development planning and farmers service. SOLARIS and OFRS are two systems dealing with the NR database to generate output needed by academicians, researchers, extentionists and farmers
Research and transfer of Double-high technology based on agroecological princ...FAO
Presentation from Fusuo Zhang from Center for Resources, Environment and Food Security at China Agricultural University on technology transfer in agroecological farming approaches, in the framework of agricultural production in China. The presentation was prepared and delivered in occasion of the International Symposium on Agroecology for Food Security and Nutrition, held at FAO in Rome on 18-19 September 2014.
Maize grain yield is greatly constrained by the parasitic weed, Striga hermonthica. Levels of infestation are often so high that
maize can suffer total yield loss and farmers usually abandon severely infested fields.
Oral presentation made at the 19th European Association for Potato Research (EAPR) Conference held in Brussels on 7-11 July 2014. It is about a Potato Yield Gap Analysis study for Sub Saharan Africa through Participatory Modeling being conducted by the International Potato Center (CIP).
Technical Efficiency Differentials and Resource - Productivity Analysis amon...researchagriculture
The importance of soybean as a high protein, primary input in vegetable oil,
diary and feed industries is not in doubt. The technical efficiency and
resource
-
productivity of smallholder soybean farmers in Benue State, Nigeria were
estimated using cross sectional data obtained on 96 soybean farmers in the empirical
analysis. Results obtained with transcendental logarithmic (translog) stochastic
frontier model showed that the technical efficiencies varied widely from
0.254 to 0.999 with a mean of 0.718. This indicates that smallholder soybean
production was in the irrational stage of production (stage III) as depicted by the
returns
-
to
-
scale (RTS) of
-
2.848. Land and fertilizer were effectively allocated and
used, as confirmed by each variable having estimated coefficient value between zero
and unity, depicting stage II in the production curve. The productivity of the factors
can be enhanced by expanding the farm size at the existing level of labour so that the
variable of labour used could move from stage III to stage II in the production curve.
Labour saving resource and/or practices should be encouraged for productivity and
technical efficiency to be enhanced.
Conservation agriculture, livestock and livelihood strategies in the Indo-Ga...ILRI
Presentation by Olaf Erenstein, Nils Teufel & Arindam Samaddar (CIMMYT) to the CGIAR Systemwide Livestock Programme Livestock Policy Group Meeting, 1 December 2009
Transforming Maize-legume Value Chains –A Business Case for Climate-Smart Ag...CIMMYT
CIMMYT Senior Cropping Systems Agronomist Christian Thierfelder presented on climate-smart agriculture in southern Africa in a webinar titled Climate Resilient Agriculture Success Stories – Making a Case for Scale Up.
Genetic Variability for Grain Yield, Flowering and Ear Traits in Early and La...Premier Publishers
Field experiments were carried out to estimate genetic variation in full – sib families of a sweet corn population during the early and late sowing dates of 2012 at the Federal University of Agriculture, Makurdi, Nigeria. Experiments were laid out as lattice but analysed using Randomised Complete Block Design (RCBD) with three replications. Families differed significantly (P < 0.01) for all the traits (days to anthesis, days to silking, anthesis – silking interval (ASI), ear traits of ears/plant, cob length, kernel rows/cob and kernels/row on cob and grain yield), indicating genetic variability for these traits. The Genotypic Coeffient of Variation (GCV), Phenotypic Coefficient of Variation (PCV) and Genetic Advance (GA) were higher for ASI compared to days to anthesis and silking, but with similar range for the ear traits. Heritability was however moderate for ASI and high for days to anthesis and silking. The higher heritability and GA observed for most of the traits in the late sowing date is an indication that progress in selection will be faster during this date. Full-sib recurrent selection for grain yield via ear traits should therefore be used to improve the local sh2 sweet corn population in the Southern Guinea Savanna of Nigeria.
Trends and Determinants of Cereal Productivity: Econometric ANalysis of Natio...essp2
Ethiopian Development Research Institute (EDRI) and International Food Policy Research Institute (IFPRI), Seventh International Conference on Ethiopian Economy, June 24, 2010
Empirical EO based approach to wheat yield forecasting and its adaptation wit...CIMMYT
Remote sensing –Beyond images
Mexico 14-15 December 2013
The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)
Research in sustainable intensification in the sub-humid maize-based cropping...africa-rising
Presented by Mateete Bekunda (IITA), Ben Lukuyu (ILRI), Danny Coyne (IITA), Dan Makumbi (CIMMYT), Jean Claude Rubyogo (CIAT), Job Kihara (CIAT), Fen Beed (IITA), Adebayo Abass (IITA), Stephen Lyimo (SARI), Victor Afari-Sefa (AVRDC) and Festo Ngulu (IITA) at the Africa RISING East and Southern Africa annual review and planning meeting, Lilongwe, Malawi, 3-5 September 2013
Climate Change and Future Food Security: The Impacts on root and Tuber CropsACDI/VOCA
Background: Climate Sensitivity of Agriculture
Importance or Root Crops to Jamaican Food Security
Estimating Yields (Manually)- Yield vs. Climate Dilemma
Methodology: Tools and Approaches
Results: Parameterization, Future Production under Climate Change
Conclusions: Climate Smart Implications & Main lessons learnt
Drought-tolerant maize genotypes belonging to two different maturity (10 early and 10 intermediate) groups were
evaluated for yield and other related characters in the Southern Guinea Savanna of Nigeria for two years (2009 and
2010). The differences among genotypes between and within maturity groups differed significantly (P<0.01)><0.01) only for grain yield. The rainfall patterns were favourable in
both cropping years with comparable values of growth parameters. Intermediate maturing genotypes (TZL COMP1-
W C6 F2, SUWAN-1-SR-SYN, TZB-SR, OBA SUPER I, EV 8435-SR) out-yielded early maturing ones with yield
advantage of 34.29% and taller by 17.04% compared to early ones. However, early genotypes were early to
anthesis with 6.57% advantage over intermediate genotypes. Four early genotypes (DMR-ESR Y CIF2, AC 90
POOL 16 DT, STR, TZE-W DT STR C4 and ACR 95TZE COMP4 C3) were superior for grain yield withn a range of
4.39 to 4.68 t ha-1. These genotypes could be selected either as parental breeding cultivars to overcome the
problem of moisture stress during the later part of the cropping season or introgressed with favourable cultivars for
high yield adaptable to drought-prone areas in the SGS agro-ecology.
Agronomy and crop-livestock interaction activities in Ghana 2019/20 africa-rising
Presented by Abdul Rahman Nurudeen(IITA), Bekele Kotu(IITA), Gundula Fischer(IITA), Kipo Jimah(IITA), Francis Muthoni(IITA), Williams Attakora(CSIR-SARI), Addah Wesseh(UDS) at Africa RISING Ghana Country Planning Meeting, Tamale, Ghana, and Virtual, 24 - 25 June 2020.
Effect of different initial soil moisture on desi chickpea ICCV 95107 (Cicer ...Agriculture Journal IJOEAR
Abstract— This study aimed to assess some priming methods and durations under ranging field capacities of water in Kirinyaga County in Kenya in 2012/2013 growing seasons. A two season field experiment was conducted at Mwea Irrigation Agricultural Development Centre (MIAD) farm to evaluate chickpea advanced lines of ICCV 97105 for growth and growth rates under no priming, hydro priming and halo prime at three levels of i.e. 0.1, 0.2 and 0.3 % NaCl2 concentration with three priming durations (8, 10 and 12 hours) and varying initial soil moisture levels 100% field capacity (FC), 75 % FC, 50 % FC and 25 % FC). The experiment was laid out in a split plot design with three replications. Pre sowing irrigation, combined priming method and priming duration allocated in the main, and sub-plots, respectively. The control treatment was the pre-sowing irrigation at field capacity (FC). The results revealed the maximum/optimum crop growth rate (CGR) of desi chickpea was achieved with 100% FC during wet season I (October, 2012-January, 2013) which was 181.0kg DM/ha/day, while it was 114 kg DM/ha/day with 90-96% FC during the drier season II (July -Oct 2013). Desi chickpea grows slowly under low seasonal rainfall (311.2 mm) than higher seasonal rainfall (565.1 mm). Therefore, it is necessary to apply higher pre sowing irrigation of up to 100% FC in dry areas. Relating crop growth rate CGR during 75-90 days after sowing (DAS) phase period with DM and CGR to grain yield at harvest 120 DAS revealed that it is possible to predict DM and grain yield with 80.5 and 77.5% confidence by use of linear production functions:
Land restoration, climate change and why cheap stuff doesn't get done. Patrick Worms
The world is warming rapidly, soils are disappearing massively, and cheap solutions exist (and no, they're not Teslas - sorry, Elon). So, why aren't being deployed at scale?
Integrated Management of Soil Fertility - Prerequisite for Increased Agricult...SIANI
This study was presented during the conference “Production and Carbon Dynamics in Sustainable Agricultural and Forest Systems in Africa” held in September, 2010.
Characterization and the Kinetics of drying at the drying oven and with micro...Open Access Research Paper
The objective of this work is to contribute to valorization de Nephelium lappaceum by the characterization of kinetics of drying of seeds of Nephelium lappaceum. The seeds were dehydrated until a constant mass respectively in a drying oven and a microwawe oven. The temperatures and the powers of drying are respectively: 50, 60 and 70°C and 140, 280 and 420 W. The results show that the curves of drying of seeds of Nephelium lappaceum do not present a phase of constant kinetics. The coefficients of diffusion vary between 2.09.10-8 to 2.98. 10-8m-2/s in the interval of 50°C at 70°C and between 4.83×10-07 at 9.04×10-07 m-8/s for the powers going of 140 W with 420 W the relation between Arrhenius and a value of energy of activation of 16.49 kJ. mol-1 expressed the effect of the temperature on effective diffusivity.
"Understanding the Carbon Cycle: Processes, Human Impacts, and Strategies for...MMariSelvam4
The carbon cycle is a critical component of Earth's environmental system, governing the movement and transformation of carbon through various reservoirs, including the atmosphere, oceans, soil, and living organisms. This complex cycle involves several key processes such as photosynthesis, respiration, decomposition, and carbon sequestration, each contributing to the regulation of carbon levels on the planet.
Human activities, particularly fossil fuel combustion and deforestation, have significantly altered the natural carbon cycle, leading to increased atmospheric carbon dioxide concentrations and driving climate change. Understanding the intricacies of the carbon cycle is essential for assessing the impacts of these changes and developing effective mitigation strategies.
By studying the carbon cycle, scientists can identify carbon sources and sinks, measure carbon fluxes, and predict future trends. This knowledge is crucial for crafting policies aimed at reducing carbon emissions, enhancing carbon storage, and promoting sustainable practices. The carbon cycle's interplay with climate systems, ecosystems, and human activities underscores its importance in maintaining a stable and healthy planet.
In-depth exploration of the carbon cycle reveals the delicate balance required to sustain life and the urgent need to address anthropogenic influences. Through research, education, and policy, we can work towards restoring equilibrium in the carbon cycle and ensuring a sustainable future for generations to come.
Willie Nelson Net Worth: A Journey Through Music, Movies, and Business Venturesgreendigital
Willie Nelson is a name that resonates within the world of music and entertainment. Known for his unique voice, and masterful guitar skills. and an extraordinary career spanning several decades. Nelson has become a legend in the country music scene. But, his influence extends far beyond the realm of music. with ventures in acting, writing, activism, and business. This comprehensive article delves into Willie Nelson net worth. exploring the various facets of his career that have contributed to his large fortune.
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Introduction
Willie Nelson net worth is a testament to his enduring influence and success in many fields. Born on April 29, 1933, in Abbott, Texas. Nelson's journey from a humble beginning to becoming one of the most iconic figures in American music is nothing short of inspirational. His net worth, which estimated to be around $25 million as of 2024. reflects a career that is as diverse as it is prolific.
Early Life and Musical Beginnings
Humble Origins
Willie Hugh Nelson was born during the Great Depression. a time of significant economic hardship in the United States. Raised by his grandparents. Nelson found solace and inspiration in music from an early age. His grandmother taught him to play the guitar. setting the stage for what would become an illustrious career.
First Steps in Music
Nelson's initial foray into the music industry was fraught with challenges. He moved to Nashville, Tennessee, to pursue his dreams, but success did not come . Working as a songwriter, Nelson penned hits for other artists. which helped him gain a foothold in the competitive music scene. His songwriting skills contributed to his early earnings. laying the foundation for his net worth.
Rise to Stardom
Breakthrough Albums
The 1970s marked a turning point in Willie Nelson's career. His albums "Shotgun Willie" (1973), "Red Headed Stranger" (1975). and "Stardust" (1978) received critical acclaim and commercial success. These albums not only solidified his position in the country music genre. but also introduced his music to a broader audience. The success of these albums played a crucial role in boosting Willie Nelson net worth.
Iconic Songs
Willie Nelson net worth is also attributed to his extensive catalog of hit songs. Tracks like "Blue Eyes Crying in the Rain," "On the Road Again," and "Always on My Mind" have become timeless classics. These songs have not only earned Nelson large royalties but have also ensured his continued relevance in the music industry.
Acting and Film Career
Hollywood Ventures
In addition to his music career, Willie Nelson has also made a mark in Hollywood. His distinctive personality and on-screen presence have landed him roles in several films and television shows. Notable appearances include roles in "The Electric Horseman" (1979), "Honeysuckle Rose" (1980), and "Barbarosa" (1982). These acting gigs have added a significant amount to Willie Nelson net worth.
Television Appearances
Nelson's char
WRI’s brand new “Food Service Playbook for Promoting Sustainable Food Choices” gives food service operators the very latest strategies for creating dining environments that empower consumers to choose sustainable, plant-rich dishes. This research builds off our first guide for food service, now with industry experience and insights from nearly 350 academic trials.
UNDERSTANDING WHAT GREEN WASHING IS!.pdfJulietMogola
Many companies today use green washing to lure the public into thinking they are conserving the environment but in real sense they are doing more harm. There have been such several cases from very big companies here in Kenya and also globally. This ranges from various sectors from manufacturing and goes to consumer products. Educating people on greenwashing will enable people to make better choices based on their analysis and not on what they see on marketing sites.
Natural farming @ Dr. Siddhartha S. Jena.pptxsidjena70
A brief about organic farming/ Natural farming/ Zero budget natural farming/ Subash Palekar Natural farming which keeps us and environment safe and healthy. Next gen Agricultural practices of chemical free farming.
Artificial Reefs by Kuddle Life Foundation - May 2024punit537210
Situated in Pondicherry, India, Kuddle Life Foundation is a charitable, non-profit and non-governmental organization (NGO) dedicated to improving the living standards of coastal communities and simultaneously placing a strong emphasis on the protection of marine ecosystems.
One of the key areas we work in is Artificial Reefs. This presentation captures our journey so far and our learnings. We hope you get as excited about marine conservation and artificial reefs as we are.
Please visit our website: https://kuddlelife.org
Our Instagram channel:
@kuddlelifefoundation
Our Linkedin Page:
https://www.linkedin.com/company/kuddlelifefoundation/
and write to us if you have any questions:
info@kuddlelife.org
Artificial Reefs by Kuddle Life Foundation - May 2024
Akinseye_Open Defence
1. FACTORING CLIMATE VARIABILITY AND CHANGE
INTO CROP MODELS FOR ENHANCING SORGHUM
PERFORMANCE IN THE WEST AFRICAN
SEMI-ARID TROPICS
AKINSEYE, Folorunso Mathew
Major Supervisor: Prof. S. O Agele (FUTA-Nigeria)
Co-Supervisor: Dr. P. C. S. Traore (ICRISAT- Mali)
German Adviser: Prof. Dr. A. M Whitbread(UG& ICRISAT-India)
Department of Meteorology and Climate Science
Federal University of Technology, Akure, Ondo State.
PhD Final Presentation
2. Why is climate variability so important
to agriculture ?
Agriculture is the largest employer of labour, a guarantee for food
security in the world and is probably the most weather-dependent of all
human activities.
Climate variability has been, and continues to be, the principal source of
fluctuations in global food production, particularly in the semi- arid
tropics.
Throughout history, climatic extremes has wreaked havoc on
agriculture, water resources etc.
In addition, with other physical, social, political and economic factors,
climate variability contribute to vulnerability of economic losses,
hunger, famine and dislocation
3. Introduction
West African semi-arid is home to some 300 million people with at least 70%
engaged in agricultural activity (FAO,2007), it accounts for 35% of the GDP,
(World bank, 2000) and ~ 90% of cropland managed under rainfed conditions
(FAOSTAT,2005).
Rainfall is one of the most important natural resources and rainfall variability
manifests intra-annual, inter-annual and decadal scales.
Crucial problem for rainfed agriculture: Decision about the optimal planting date
for current season
- Planting as early as possible to avoid wastage of valuable growth time
- Planting too early /late may lead to crop failures and high economic losses
Low crop yield (productivity) of major cereal crops attributed to constraining
environmental conditions ,depleted soil fertility (Nitrogen and phosphorus),
diseases ( e.g. Midges),high costs of fertilizers (Winterbottom et al., 2013)
4. Introduction Cont’d
• In the semi-arid tropics, sorghum and millet contribute to more
than 80% of the food needs and has mean yield of 800kg/ha
(Maredia et al., 1998, 2000)
• In 2008, sorghum was cultivated in Mali on an area of 990 995
ha with a production of 1, 027,202 tons and yield average is
1036kg/ha(http://faostat.fao.org/site)
• Crop growth models are used around the world as a research
tool for yield forecast because models
– provide dynamical estimates of climate driven potential yield,
and yield components as well as water balance
– useful for assessing the agricultural risks of climate change in
the 21st Century
5. Introduction Cont’d
Decision Support for Agro-technology Transfer (DSSAT) (Jones et al., 2003).
Agricultural Productions Systems sIMulator (APSIM) (McCown et al., 1996;
Keating et al., 2003).
Samara Version 2 implemented on the Ecotrop platform of the Centre
International de Recherche Agronomique pour le De´veloppement (CIRAD)
Dingkhun et al., (2003)
• DSSAT model was previously used in simulation studies by Adiku et al., 2007;
MacCarthy et al., (2013) over Ghana) and Traore et al., (2007) in the Sahel zone
• APSIM model was also used in previous studies in West Africa by MacCarthy
et al., 2009 and Apkonikpe et al., (2010).
• comparative evaluation of these models has not been undertaken for
sorghum growth and development in West Africa
Crop simulation models integrate the interaction of genotypic traits,
environmental factors (e.g. soils, weather) and management (G x E x M)
6. Literature Review
• Lobell et al., (2011), the potential yield loss due to the climate change impact is
about 5% for each degree Celsius of global warming.
• IPCC (2014) predicts an approximate 50% decrease in yields from rain-fed
agriculture by 2020 in some countries.
Reference
Climate
model Crop model Scenario Area Horizon Crop Baseline
Adejuwon (2006) HadCM2 EPIC 1%/year in CO2 Nigeria
2035/2055/
2085
Cassava, maize,
millet, rice, sorghum 1960/1990
Jones and
Thornton (2003) HadCM2
CERES maize
(DSSAT) Not found WA (details) 2055 Maize
1990
climate
normals
Liu et al., (2008) HadCM3 GEPIC A1FI, B1, A2, B2
SSA, WA
(details) 2030
Global, cassava,
maize, millet, rice,
sorghum, wheat 1990/1999
Lobell et al.,
(2008) 20 GCMs Empirical A1B, A2, B1 WA 2030
Cassava, groundnut,
maize, millet,
rice, sorghum, wheat,
yams 1998/2002
Parry et al.,
(2004) HadCM3
Empirical +
BLS
A1FI, A2A, A2B,
A2C,
B1A, B2A, B2B WA
2020/2050/
2080 Global 1990
Salack (2006) Scenario DSSAT 4
(+1 8C, +1.5 8C,
+3 8C)/ (+5%,
+10%, +20%)
Niger/
Burkina
2020/2050/
2080
Millet( mtdo/ zatib
genotypes), sorghum 1961/1990
Table 1:
7. Future projections suggest a drier
western Sahel (e.g., Senegal, part of Mali)
A wetter eastern Sahel (e.g., Mali, Niger)
No change or slight increases in annual
rainfall towards more southern locations
(e.g., Ghana, Nigeria) (Hulme et al.,
2001,Adiku et al.,2014).
Literature Review Cont’d
Fig.1b: Median Temperature change (%) for Mid-
century RCP8.5 over West Africa
Fig.1a: Median Precipitation Change (%) for Mid-
century RCP8.5 over West Africa
8. RESEARCH QUESTIONS
How do process-based crop models perform on diverse photoperiod
sensitive sorghum varieties under current climate system and near
future climate change scenarios in the terms of yield potentials across
semi-arid region?
Which definition of onset of rain is most appropriate to define the start
of growing season (OGS) and fitted into farmer’s planting time, for
major cereal crops(maize, millet and sorghum) across agroecological
zones of Mali?
9. AIMS AND OBJECTIVES
Aims:
“To address the need for substantial improvement in the characterization of food
security risks and enhance the development of adaptation measures for Sub-
Sahara Africa (SSA) in the circumstances of the changing growing environmental
(biophysical) conditions”.
Specific objectives are to;
evaluate the onset and length of growing season in order to establish the
most suitable dates for planting major cereal crops in the agro-ecological zones
of Mali;
determine the effect of sowing date on photoperiod sensitive sorghum
genotypes and yield potentials under non-limiting water and nutrient supply;
assess the process-based crop growth models (DSSAT, APSIM and Samara)
improvements through model calibration and validation for phenology and
yield prediction in sorghum;
provide comparison of the sensitivity of the current system to climate change,
and then recommend the most suitable adaptation strategies.
10. Fig.3: Map of the Mali showing the selected rainfall station and
ecological zones in accordance with the annual mean rainfall
OGS was evaluated
from four (4)
definitions of onset
of rain by;
Def_1 -Sivakumar,
(1988)
Def_2 -Kasei and
Afuakwa, (1991).
Def_3 - Omotosho et
al., (2000)
Def_4 –FAO, (1978)
CGS - Cessation of
growing season
defined after Traore
et al., (2000)
LGS = CGS - OGS
Research Methodology – PART 1
11. Hypothesis
• OGS - onset dates was validated with farmers sowing window
for maize, millet and sorghum
– Accept null: if the mean onset date provided at least 7days
to farmers planting date
• LGS was evaluated with duration to maturity of some major
crops varieties (FAO, 2008)
Crop type Local name Selected name Breeder Variety
maturity
Duration from
planting to
Maturity(days)
Maize Zangueréni Zangueréni IER Early 80 - 90
Dembagnuman Obatanpa CIMMYT/CRI Medium 105-110
Sotubaka Suwan 1-SR CIMMYT/IITA Late 110–120
Millet Sossat Sossat c-88 ICRISAT/IER Early 90
Toroniou Toroniou IER Medium 100 -110
M9D3 M9D3 IER Late 125 -130
Sorghum Jakumbe CSM63E IER Early 100
Jigui Seme CSM388 IER Medium 125
Soumalemba IS15-401 CIRAD/ICRISAT Late 145
Table 2: Characteristics of the most cultivated crop varieties within the West Africa semiarid tropics.
12. Research Methodology –PART 2
Fig. 4: Study Area
The field experiment was
conducted under non-limiting
water and nutrients supply
13. CLIMATIC CONDITION AT FIELD SITE
Fig.4c: Climatic pattern of the experimental site
Fig.4b: An Automatic weather station less than 500m away from sorghum field trial
14. Code Genotypes
Name
Race/type Geographical
Origin
Target use quality of
Stover
grain quality Plant
type
G1 CSM63E Guinea Mali Biomass Poor Good int
G2 621 B Caudatum Senegal Dual purpose High Good short
G3 Soumba Caudatum Senegal Dual purpose High; stay
green
intermediate
/mold
int
G4 Nieleni Hybrid Senegal Dual purpose High good int
G5 Fadda Guinea
(Hybrid)
Burkina Faso Dual purpose High guinea
grain(good)
int
G6 Pablo Guinea Senegal Biomass Poor good tall
G7 Grinkan Caudatum Mali Dual purpose High int/mold short
G8 CSM335 Guinea Mali Biomass Poor poor tall
G9 IS15401 Guinea Cameroon Biomass High good tall
G10 SK5912 Caudatum Nigeria Dual purpose High int/mold int
MATERIALS AND METHODS
Table3b: Characterization of the Genetics materials
15. Experimental Design: Randomized complete block design,
2 factors, 4 replications, Plot size: 8x4.8m(Fig.5a)
Sowing: Jun 14, Jul 09 and Aug 05. Spacing: 75 x 20cm (Fig.
5b)
EXPERIMENTAL DESIGN FIELD LAY-OUT
Fig.5a
Fig.5b
L1 L2 L3 L4 L5 L6 L7
16. Table 5: Comparison of modeling approaches applied regarding the major
processes that determine crop growth and development
DSSAT APSIM SAMARA
Leaf area
development
Simple function estimation of
rate of leaf appearance, PHINT
(in degrees day/leaf)
Phyllochron (leaf apperance
rate) specific leaf area
(respectively leaf size)
Phyllochron ,detailed Light
extiction and coversion based on
some morphological detail of the
canopy(Dingkuhn et al., 2008)
Light
utilization/
DM estimates
RUE based on Beer-Lambert’s
law that estimates light
interception
RUE based on Beer-Lambert’s
law
Beer-Lambert’s law on the basis of
leaf blade aggregate LAI
Crop
Phenology
Estimation of thermal time (T)
through developmental phases,
Photoperiod (day length),
water/nutrient effects
simulated through a number of
development phases, using a
thermal time approach
(Muchow and Carberry, 1990;
Hammer and Muchow, 1994),
with the temperature response
characterized , Photoperiod (day
length) and water
Estimation of thermal time (T), air
temperatures at 2m, Photoperiod
(day length), water without nutrient
effects (Dingkuhn et al.,
2003,2008) .
Yield
formation
Yield is a function of harvest
index(HI) based on number of
grain and biomass production
yield formation depends on
grain number and grain size
yield formation depends on
(Coefficient of Panicle Sink
Population* Panicle Structured
Mass Maximum / Grain weight).
Stress involved Water and Nitrogen stress
shorten the growth stages
Water and Nitrogen stress
shorten the growth stages
Water stress shorten the growth
stages
Evapo-
transpiration
Priestley- Taylor /Ritchie
approach
Priestley- Taylor approach FAO-method based on Penman-
Monteith
17. OBJECTIVE 1 - RESULTS
zone Hypothesis Def_1 Def_2 Def_3 Def_4 Maize Sorghum Millet
Sahelian
Mean Onset 193 191 193 173 194 (Jul 13 ) 188 (Jul 07) 188 (Jul 07)
St.dev 15 14 14 15 6 6 6
Time_lag -5 -3 -5 15
Mean LGS 70 72 70 90
Sudano-
sahelian
Mean Onset 179 177 178 158 174(Jun 23) 178(Jun 27) 178(Jun 27
St.dev 15 16 17 16 6 6 6
Time_lag -5 -3 -4 19
Mean LGS 101 103 102 123
Sudanian
Mean Onset 160 159 162 144 172 (Jun 21) 177(Jun 26 ) 177(Jun 26)
St.dev 14 13 14 13 8 8 8
Time_lag 12 13 10 28
Mean LGS 132 133 131 149
Guinea
savanna
Mean Onset 147 146 145 133 156(Jun 05) 156(Jun 05) 156(Jun 05)
St.dev 15 15 13 9 12 12 12
Time_lag 9 10 11 23
Mean LGS 151 152 154 165
Table 6: Comparison of mean onset dates according to each method estimates with the farmers planting time for
maize, sorghum and millet. The bold part indicates the most suitable method found closed to the hypothesis set.
18. Fig. 6: (a) –Sahelian; (b) –Sudano-sahelian; zone: Probability distribution Length of growing season (in
days) based on the most appropriate OGS
OBJECTIVE 1 – RESULTS Cont’d
19. Fig. 6: (c) Sudanian; (d)- Guinea savannah zone: Probability distribution Length of growing season (in
days) based on the most appropriate OGS
OBJECTIVE 1 – RESULTS Cont’d
20. Fig.7a: Effect of sowing date on flowering time for 10 sorghum genotypes
0
500
1000
1500
2000
2500
G1 G2 G3 G4 G5 G6 G7 G8 G9 G10
Thermaltime(0Cdays)
Genotypes
I II III
Low PPsen Moderate PPsen
High PPsen
G1 – G4 represent early maturity genotypes(85-110days), observed the lowest cumulative thermal time
to flowering and less sensitive to variation of sowing date
G5-G8 represent medium maturity genotypes(110-135days), observed medium cumulative thermal time
to flowering and moderate sensitive to variation of sowing
G9-G10 represent medium maturity genotypes(115-155days), flowering time remains more or less
constant independent of sowing dates observed highest cumulative thermal time to flowering and
highly decreased to variation of sowing
OBJECTIVE 2 – EXPERIMENTATION-RESULTS
21. Fig.7b: Effect of sowing date on Total leaf Number(TLN) per plant for 10 sorghum genotypes
• As observed, TLN reduced up to 7 leaves for cultivars that
are very sensitive to day length because of the shortened
vegetative phase.
0
5
10
15
20
25
30
35
G1 G2 G3 G4 G5 G6 G7 G8 G9 G10
TotalLeavesNumber(TLN)
Genotypes
I
II
III
OBJECTIVE 2 – EXPERIMENTATION-RESULTS
22. Total biomass produced varied among the cultivars especially for the medium
and high photoperiod sensitive genotypes
And also observed significant decreased with late planting date, this is due
shortened of the growth phases
All the genotypes were efficient for biomass production with the highest value
in early planting dates (I &II)
As observed, the estimated RUE among the genotypes showed a significant
reduction up to 1/3 value of the early planting date
0
5000
10000
15000
20000
25000
30000
G1 G2 G3 G4 G5 G6 G7 G8 G9 G10
Totalbiomass(kg/ha)
Genotypes
I II III
Fig. 7c: Effect of sowing date on total biomass and Grain yield
OBJECTIVE 2 – EXPERIMENTATION-RESULTS Cont’d
23. 0
500
1000
1500
2000
2500
3000
3500
4000
4500
G1 G2 G3 G4 G5 G6 G7 G8 G9 G10
Grainyield(kg/ha)
Genotypes
I II III
Fig. 7d: Effect of sowing date on Grain yield
Highest grain yield values was obtained in early planting dates (I & II) except for
G1 , G8 and G10 respectively.
OBJECTIVE 2 – EXPERIMENTATION-RESULTS Cont’d
24. OBJECTIVE 3: CROP MODELING - RESULTS
Fig: 9 a: Model-fitted cultivars responses to day length between emergence to
Flag leaf initiation over the three sowing date as observed from the field
The duration to flag leaf initiation is driven by thermal time and
genotypic response to photoperiod changes - that varied from
low to highly sensitivity.
0
100
200
300
400
500
600
12 13 14
ThermaltimetoFlagleafinitiation
(0Cdays)
Photoperiod length (h)
CSM63E
CSM335
Fadda
IS15401
25. 0
500
1000
1500
2000
2500
3000
CSM63E CSM335 Fadda IS15401
GDD(°Cdays)
SAMARA
APSIM
DSSAT
Observed
Fig. 9b: Comparison of model-estimated growing degree days (GDD) with
the field-observed estimated between emergence and maturity (exclusive
of PSP) across cultivars
OBJECTIVE 3: CROP MODELING - RESULTS
APSIM and DSSAT estimates were close to observed compared
to Samara, the difference is due to model parameterization
27. 0
10
20
30
40
0 10 20 30 40
SimulatedTLN
Observed TLN
APSIM
DSSAT
SAMARA
Fig. 10a: Model-simulated total leaf numbers (TLN) against the observed TLN
values for all cultivars used over the three sowing dates (Jun14, July 09, Aug.05).
0
2
4
6
8
10
0 2 4 6 8 10
SimulatedMaxLAI(m2/m2)
Observed Max LAI(m2/m2)
APSIM
DSSAT
SAMARA
Fig. 10b: Model-simulated maximum leaf area Index (MaxLAI) against the
observed MaxLAI values for all cultivars used over the three sowing dates (Jun14,
July 09, Aug.05).
APSIM: RMSE =2.2, NRMSE =
10.6 %, R2= 0.88;
DSSAT: RMSE =2.0, NRMSE =
9.6%, R2= 0.86;
Samara: RMSE =1.3,NRMSE =
6.4 %, R2= 0.96
APSIM:
RMSE=2.4,NRMSE = 85
%, R2= 0.1;
DSSAT:
RMSE=2.6,NRMSE = 92
%, R2= 0.5;
Samara: RMSE=
0.9,NRMSE = 33 %, R2=
0.4
Model-calibrated and observed for TLN and Max LAI
28. Fig.11: Comparison of model-validation for duration to flowering and maturity with field
observed
Model performance against independent trials for phenology under different growing
season, locations and planting densities
29. 0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
CSM63E CSM335 Fadda IS15401
Grainyield(kg/ha)
Cultivars
Observed
DSSAT
APSIM
SAMARA
Fig12a
0
5000
10000
15000
20000
25000
CSM63E CSM335 Fadda IS15401
Totalbiomass(kg/ha)
Cultivars
Observed
DSSAT
APSIM
SAMARA
Fig12b
Table 9: Grain yield(kg/ha)
APSIM DSSAT SAMARA
RMSE(kg/ha) 833 753.0 810.0
NRMSE(%) 40.0 36 38
R2 0.6 0.6 0.4
Total biomass(kg/ha)
APSIM DSSAT SAMARA
RMSE(kg/ha) 3798 3144 3653
NRMSE(%) 40 33 39
R2 0.8 0.8 0.5
Models performance against an independent dataset for grain yield and total biomass
under different growing season, locations and planting densities
30. OBJECTIVE 4- CLIMATE CHANGE SCENARIOS AND IMPACTS ON
SORGHUM PRODUCTION
• Climate scenarios from CMIP5 GCMs using a 30-year baseline daily
weather of MODERN-ERA RETROSPECTIVE ANALYSIS FOR RESEARCH AND
APPLICATIONS (MERRA) dataset(1980-2009)
• For future projections (2040-2069), five GCMs namely CCSM4, GFDL-
ESM2M, Had GEM2-ES, MIROC5, and MPI-ESM-MR (Rosenzweig et al.
2013) were used for the RCP 8.5 scenario that assumes an elevated
CO2 concentration of 571 ppm compared with the current 390 ppm.
31. Projected decline change towards
western Sahel significant increase
change towards eastern and southern
Sahel
All the GCMs seasonal rainfall
projected changes differs across the
station, CCSM4 and MIROC5 projected
above baseline except Nioro du Rip
Fig. 13: Projected change (%) in the growing season (May to October) rainfall between Baseline (1980-2009)
and GCM’s future projection (2040- 2069) .
RCP8.5 analyses – Climate change impact on moisture regime between Baseline and
GCM’s future projectionSeasonal Rainfall
-40
-30
-20
-10
0
10
20
30
40
Changeinseasonalrainfall(%)
Nioro du Rip, Senegal Current average = 720 mm(a)
-40
-30
-20
-10
0
10
20
30
40
Changeinseasonalrainfall(%)
Sadore, Niger Current Average = 517 mm(b)
-20.0
-15.0
-10.0
-5.0
0.0
5.0
10.0
15.0
20.0
Changeinseasonalrainfall(%)
Navrongo, Ghana, Current Average = 903mm(c)
32. Onset of growing season (OGS)
Fig. 14: Comparison between Baseline(1980-2009) and GCMs mean
projection (2040-2069) for estimated Onset of growing season
Low significant change ( -5 to +7days) -
uncertainty would lies in the distribution of
rainfall during the growing period
Sadore and Navrongo projected early OGS
– corroborate the projection of more wetter
future climate.
08-Jun
13-Jun
18-Jun
23-Jun
28-Jun
03-Jul
08-Jul
Onsetofgrowingseason
Nioro du Rip, Senegal
Baseline (1980-2009)
Median_RCP 8.5 (2040-2069)
(a)
02-Jun
04-Jun
06-Jun
08-Jun
Onsetofgrowingseason
Sadore, Niger
Baseline (1980-2009)
Median_RCP 8.5 (2040-2069)
(b)
04-Jun
05-Jun
06-Jun
07-Jun
08-Jun
Onsetofgrowingseason
Navrongo, Ghana
Baseline (1980-2009)
Median_RCP 8.5 (2040-2069)
(f)
33. Length of growing season (LGS)
Fig. 15: Comparison between Baseline(1980-2009) and GCMs mean projection
(2040-2069) for estimated length of growing season (LGS)
Sadore: LGS shows significant increase in
(4) and decrease in (1); inter-annual
variability is high
Nioro: LGS decrease (3), no change (2),
variability remains high
Navrongo: No change variability remains
moderate
CCSM4 projected increase across the
stations except Nioro du Rip
80
100
120
140
160
180
Lengthofgrowingseason(days)
Nioro du Rip
Baseline (1980-2009)
Median_RCP 8.5 (2040-2069)
(a)
80
90
100
110
120
130
140
Lengthofgrowingseason(days)
Sadore, Niger
Baseline (1980-2009)
Median_RCP 8.5 (2040-2069)
(b)
80
100
120
140
160
180
Lengthofgrowingseason(days)
Navrongo, Ghana
Baseline (1980-2009)
Median_RCP 8.5 (2040-2069)
(c)
34. Fig. 16: Comparison of average monthly variability of minimum temperature between the
Baseline (1980-2009) and GCMs Scenario (2040-2069) for the selected stations
Both Tmax and Tmin uniformly increase
throughout growing season between
baseline and the GCMs projection
Tmin projected faster in magnitude than
Tmax
Suggests increase in GDD for the
crops,
Exacerbated moisture stress in rainfed
agriculture leads to grain weight loss
Climate change impact on temperatures regime between Baseline and
GCM’s future projection
10
15
20
25
30
35
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
AverageTmin(0C)
Nioro du Rip, Senegal
BASELINE
CCSM4
GFDL-ESM2M
HadGEM2-ES
MIROC5
MPI-ESM-MR
(a)
Growing season
10
15
20
25
30
35
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
AverageTmin(0C)
Sadore,Niger
BASELINE
CCSM4
GFDL-ESM2M
HadGEM2-ES
MIROC5
MPI-ESM-MR
(b)
Growing season
20
25
30
35
40
45
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
AverageTmax(0C)
Navrongo, Ghana
BASELINE
CCSM4
GFDL-ESM2M
HadGEM2-ES
MIROC5
MPI-ESM-MR
Growing season
(c)
35. Fig. 17: Projected change for minimum temperature between baseline (1980-2009
and GCMs scenario (2040-2069)
All GCMs project increased
temperature at varying
magnitudes across six stations
Highest value was projected
by HadGEM2-ES followed by
MPI-ESM-MR while the least
warming is projected by CCSM4
except at Nioro du Rip
Minimum temperatures projection change
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
ChangeinAverageTmin(0C)
Nioro du Rip, Senegal, Current average = 23.7 0C, ∆=0.12 0C(a)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
ChangeinAverageTmin(0C)
Sadore, Niger Current average = 25.6 0C ∆= 0.14 0C(b)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
ChangeinaverageTmin(0C)
Navrongo, Ghana, Current average = 22.9 0C ∆=0.11 0C(c)
36. Fig. 18: Projected change for maximum temperature between baseline
(1980-2009 and GCMs scenario (2040-2069)
Maximum temperatures projection change
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
ChangeinAverageTmax(0C)
Nioro du Rip, Senegal , Current Average = 34.40C , ∆=0.140C(a)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
ChangeinAverageTmax(0C)
Sadore, Niger Current average = 36.9 0C ∆= 0.19 0C(b)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
ChangeinaverageTmax(0C)
Navrongo,Ghana, Current average = 33 0C ∆ =0.13 0C
(c)
38. Results
• CSM63E- DSSAT simulated lower grain yield compared to
APSIM and Samara, low inter-annual variability except at Mopti
and Kano by DSSAT
• CSM335 -DSSAT and Samara shows higher inter-year
variability across the sites compared to APSIM model, highest
grain yield simulated at Koutiala and lowest grain yield at
Sadore.
• Fadda –exhibited high grain yield potential, inter-annual
variability remains high across the models and sites
• IS15401 – model simulated low grain yield across the sites
39. -30
-20
-10
0
10
20
30
RelativeChangeingrainyield(%)
CSM63E - without Adaptation
Mopti
Sadore
Nioro du Rip
Kano
Koutiala
Navrongo
(a)
Impact of projected GCMs scenario on sorghum cultivars without adaptation
-30
-20
-10
0
10
20
30
RelativeChangeingrainyield(%)
CSM335 - without Adaptation
Mopti
Sadore
Nioro du Rip
Kano
Koutiala
Navrongo
(b)
-30
-20
-10
0
10
20
30
RelativeChangeingrainyield(%)
Fadda - without Adaptation
Mopti
Sadore
Nioro du Rip
Kano
Koutiala
Navrongo
(c)
-30
-20
-10
0
10
20
30
RelativeChangeingrainyield(%)
IS15401 - without Adaptation
Mopti
Sadore
Nioro du Rip
Kano
Koutiala
Navrongo
(d)
Fig. 20: Comparison of the relative change (%) in yield projection for the cultivars between the baseline and
future projected climate scenario (2040-2069) without Adaptation across selected sites
40. -30
-20
-10
0
10
20
30
Relativechangeingrainyield(%)
CSM63E With Adaptation
Mopti
Sadore
Nioro du Rip
Kano
Koutiala
Navrongo
(a)
-30
-20
-10
0
10
20
30
Relativechangeingrainyield(%)
FADDA – With Adaptation
Mopti
Sadore
Nioro du Rip
Kano
Koutiala
Navrongo
(c)
-30
-20
-10
0
10
20
30
RelativeChangeingrainyield(%)
IS15401 – With Adaptation
Mopti
Sadore
Nioro du Rip
Kano
Koutiala
Navrongo
(d)
-40
-30
-20
-10
0
10
20
30
40
RelativeChangeingrainyield(%)
CSM335- With Adaptation
Mopti
Sadore
Nioro du Rip
Kano
Koutiala
Navrongo
(b)
Impacts of adaptation measure on genotypic difference under climate change
Fig. 21 : Comparison of the relative change (%) in yield projection for the cultivars between the baseline
and future projected climate scenario (2040-2069) with Adaptation across selected sites
41. Discussions
• Medium and late maturity cultivars found to be photoperiodically sensitive
and strong response to variation in sowing dates
• Calibration shows the models capability to predict crop duration for the
agronomically relevant range of sowing dates.
– A near perfect fit was observed for the phenological growth stages
between the crop model-simulated and field-observed values
– the uncertainty lied in the prediction of total grain yield and biomass
• Total biomass and grain yield varied strongly among the models, the
variation from models output could be linked to model internal mechanism
or quality of the field data.
• On the sensitivity of current systems to climate change:
– Decline changes in yield output between baseline and 5GCMs for all the
models across sites
– Models showed effect of the latitude and photoperiod on the cultivars
(e.g. Fadda)
– High demand for water (CSM335 and IS15401) which resulted in low yield
– the increase in rainfall amounts projected by some GCMs (e.g. CCSM4)
does not match with the projected increase in mean simulated grain
yields
– Tmin projected faster than Tmax that suggests increase in GDD
42. Conclusions
The determination of onset date of growing season from single
method across AEZ of Mali may lead to false onset or too late date
estimation.
Based on the estimated LGS across AEZ and evaluation with
duration to maturity of major crops varieties, the results suggest
early-maturing varieties for Sahelian zone,
early and medium maturing varieties for Sudano-sahelian zone,
All level of maturity for Sudanian and Guinean zones provided the flowering
time would occur 15-20days prior to CGS (e.g. sorghum and millet) or varieties
that can withstand the terminal drought(CGS) during grain filling
The novel and apparent merit of this study is that
Crop modelling is found as a valuable tool to understand
genotype × environment × management (G × E × M) interactions
on crop growth and yield potential
Nearly all the widely used crop models tested showed their
capability in assessing climate impacts/risk for range of
photoperiod sensitive sorghum cultivars
43. Conclusions cont’d
The study confirmed warming across the dryland West
Africa (high confidence) – seemingly faster in cooler areas
(e.g. Nioro du Rip, Senegal).
Rainfall may likely increase eastwards, decrease westwards
and slight increase/no change southward: this suggests
climate adaptation will be local
Impacts of projected changes by GCM’s vary significantly
across different study sites compared and cultivars.
Projected yields changes from three crop models at different
contrasted sites, it suggests an insight on the need for climate-
smart varieties as long-time plan adaptation strategy to
ensure increase productivity under warming projected climate.
44. CONTRIBUTION OF THE RESEARCH TO
KNOWLEDGE
Strengthened the prediction skill to define the onset of growing
season, as well as the length of growing season in semi-arid region in
order to minimize climatic risk especially for staple crops(maize, millet
and sorghum)
Crop models improvement through calibration of photoperiod sensitive
sorghum for the growth parameters and yield development was
established
Application of multi-model climate change scenarios projection (GCMs)
into dynamic crop models for enhancing sorghum productivity in West
Africa semi-arid tropics and the development of the adaptation
strategies.
45. Recommendations
Further evaluations of onset date via participatory approach
with farmers, agrometeorologists and agriculture extension
officers, for ‘on-line’ dissemination to farmers;
As modelling can help reduce number of field experiments and
can save resources, it is therefore recommended that a reliable
yield projection should be cultivar specific through model
calibration and validation with data sets from carefully-
conducted experiments;
Crop breeders should work closely with both climate and crop
modellers in the region to improve on climate-smart traits in
sorghum varieties that would be more resilient to elevated
mean temperature during the growing period;
Many, many more models exist and much, much more
uncertainty subsists. Regional capacity to operate models
and interpret projections is lacking and must be aggressively
developed – e.g. through science-policy platforms
46. ACKNOWLEDGEMENT
• This research study was funded by Federal Ministry
of Education and Research (BMBF) through the
West African Service Centre on Climate Change and
Adapted Land Use (WASCAL), Graduate Research
Program (GRP). Financial support is gratefully
acknowledged.
• Grateful to the University management and
Department for my study leave.
Experimental site is ICRISAT Samanko, Mali located at 12.520N/-8.070W
The station is mono- modal rainfall pattern with peak in August which takes about 40% of the growing period total rainfall. Cropping year (2013) was categorized as wet year with rainfall amount above the average(1968-2010), Delayed OGS was observed but the LGP was longer than average and NRD was more compared to the average past 43year as estimated
All the genotypes seed were released by Sorghum breeding department, ICRISAT Bamako
The results show that days to flowering in sorghum depends on the cultivar sensitive to daylength period, This result agreed with (Dingkuhn et al., 2008)
Which found the panicle initiation in West Africa sorghum genotype is driven the genetic material and sensitivity to day length period.
The results show that days to flowering in sorghum depends on the cultivar sensitive to daylength period, This result agreed with (Dingkuhn et al., 2008)
Which found the panicle initiation in West Africa sorghum genotype is driven the genetic material and sensitivity to day length period.
The duration to flag leaf initiation is influenced by genetic differences among the cultivar and also response changes to photoperiod which varied from low to highly sensitive
DSSAT and APSIM simulated biomass production in addition to Nitrogen effect but SAMARA does not, therefore, its RUE is higher in SAMARA model for the cultivar compare to DSSAT and APSIM
Phenology: All the models captured the days to Flowering very well expect for the late cultivar (IS15401), RMSE was lowest except for DSSAT and SAMARA. However, SAMARA closest to the observed for day to physiological maturity for all the cultivar.
All the model over-estimated the yield, but mean bias error is lower in SAMARA compare to the DSSAT and APSIM. This may be there was no effect of Nitrogen on the SAMARA model. for biomass DSSAT and SAMARA are closer to the observed compared to APSIM which was over-estimated