This document discusses systems approaches for analyzing smallholder agriculture. It provides a 10-point "Farming Systems Decalogue" for conducting on-farm systems analysis, including dealing with farm diversity, spatio-temporal variability, and crop-livestock interactions. It also discusses the properties of smallholder farming systems, including anisotropy and heterogeneity. Examples are given of on-farm nutrient flows and the complexity of crop-livestock systems.
Trait data mining at European pre-breeding workshop at Alnarp (25 Nov 2009)Dag Endresen
Trait mining with eco-geographic data for improved utilization of plant genetic resources. Presentation for the cereal pre-breeding workshop at Alnarp. A brief overview of the new trait mining method: Focused Identification of Germplasm Strategy (FIGS). And many thanks to Michael Mackay and Ken Street for providing some of the slides!
Endresen, D.T.F. (2010). Predictive association between trait data and ecogeographic data for Nordic barley landraces. Crop Sci. 50(6):2418-2430. doi: 10.2135/cropsci2010.03.0174
NOVA PhD training course on pre-breeding, Nordic University Network (2012)Dag Endresen
Pre-breeding for sustainable plant production. Nova PhD course, January 2012 at Röstånga in Southern Sweden. Nova is a Nordic University Network.
Pre-breeding provides an important element in broadening the genetic diversity and introducing new and useful traits and properties to the food crops. New traits introduced in pre-breeding activities are not least important to meet the new challenges agriculture will face from the on-going climate change. The needed genetic diversity is often available outside of the genepool of cultivars and elite breeding lines. And sources of novel genetic diversity such as the primitive crops and even the wild relatives of the cultivated plants are expected to get increased focus when facing new challenges in agriculture.
The GBIF data portal provides information on in situ occurrences for many of the wild relatives to the cultivated plants that are not (yet) collected and accessioned by the ex situ seed genebank collections. The GBIF data portal will therefore provide a very valuable bridge between these data sources for genebank accessions and occurrence data sources outside of the genebank community. Occurrences from the GBIF data portal will assist in the identification of locations where potentially useful populations of crop wild relatives can be found. Ecological niche modeling provides a widely used approach for predicting species distributions and can be used for this purpose.
Recent work on predictive modeling to identify a link between useful crop traits and eco-geographic data associated with the source locations for germplasm may have particular value for pre-breeding efforts. The Focused Identification of Germplasm Strategy (FIGS) provides and approach for efficient identification of germplasm material with new and useful genetic diversity for a target trait property. Such predictive modeling approaches are of particular interest when performing pre-breeding because of the high costs related to working with this material. Cultivated plants are domesticated for properties and traits such as non-shattering seed behavior and more uniform harvest time that makes conducting agricultural experiments easier and less costly. Non-domesticated germplasm material and also the older cultivars and landraces have many agro-botanical traits that was moderated in modern cultivars to better suit agricultural practices and efficiency. Pre-breeding is largely about removing such undesired traits from the non-cultivated and less intensively domesticated material while maintaining potentially useful traits.
Nova PhD course home page:
http://www2.nova-university.org/chome/cpage.php?cnr=03-110404-412
https://sites.google.com/site/novaplantimprovementnetwork/home/phd-course-in-sweden-january-2012
FIGS workshop in Madrid, PGR Secure (9 to 13 January 2012)Dag Endresen
We organized last week (9 to 13 January 2012) a workshop in Madrid (Spain) on predictive characterization using the Focused Identification of Germplasm Strategy (FIGS) for wild relatives to the cultivated plants (crop wild relatives). This workshop was part of the EU funded PGR Secure project [1] (EU 7th framework programme). The objective of this workshop was to use predictive computer modeling with R [2] for data mining (trait mining) to identify genebank accessions and populations of crop wild relatives with a higher density of genetic variation for a target trait property (response, independent variable) using climate data and other environment data layers as the explanatory or independent multivariate variables. We have previously validated the FIGS approach for landraces of wheat and barley [3]. This study was one of the first attempts to validate the FIGS approach for other crops as well as for crop wild relatives (CWR). The crop landraces and crop wild relatives included in this study was: Oats (Avena sp.), Beet (Beta sp.), Cabbage and mustard (Brassica sp.), Medick including alfalfa, lucerne (Medicago sp.). We made good progress on the methodology, but also faced some major obstacles related to data availability.
Endresen, D.T.F. (2010). Predictive association between trait data and ecogeographic data for Nordic barley landraces. Crop Sci. 50(6):2418-2430. doi: 10.2135/cropsci2010.03.0174
Endresen, D.T.F., K. Street, M. Mackay, A. Bari, and E. De Pauw (2011). Predictive Association between Biotic Stress Traits and Eco-Geographic Data for Wheat and Barley Landraces. Crop Science 51 (5): 2036-2055. doi: 10.2135/cropsci2010.12.0717
Endresen, D.T.F. (2011). Utilization of Plant Genetic Resources: A Lifeboat to the Gene Pool [PhD Thesis]. Copenhagen University, Faculty for Life Sciences, Department of Agriculture and Ecology. Printed at Media-Tryck, Lund University Press, April 2011. Available at: http://goo.gl/pYa9x (PDF 37 MB). ISBN: 978-91-628-8268-6.
Bari, A., K. Street, M. Mackay, D.T.F. Endresen, E. De Pauw, and A. Amri (2012). Focused identification of germplasm strategy (FIGS) detects wheat stem rust resistance linked to environmental variables. Genetic Resources and Crop Evolution (in press). doi:10.1007/s10722-011-9775-5
Endresen, D.T.F., K. Street, M. Mackay, A. Bari, A. Amri, E. De Pauw, K. Nazari, and A. Yahyaoui (2012). Sources of Resistance to Stem Rust (Ug99) in Bread Wheat and Durum Wheat Identified Using Focused Identification of Germplasm Strategy (FIGS). Crop Science 52, in press. doi: 10.2135/cropsci2011.08.0427
Trait data mining at European pre-breeding workshop at Alnarp (25 Nov 2009)Dag Endresen
Trait mining with eco-geographic data for improved utilization of plant genetic resources. Presentation for the cereal pre-breeding workshop at Alnarp. A brief overview of the new trait mining method: Focused Identification of Germplasm Strategy (FIGS). And many thanks to Michael Mackay and Ken Street for providing some of the slides!
Endresen, D.T.F. (2010). Predictive association between trait data and ecogeographic data for Nordic barley landraces. Crop Sci. 50(6):2418-2430. doi: 10.2135/cropsci2010.03.0174
NOVA PhD training course on pre-breeding, Nordic University Network (2012)Dag Endresen
Pre-breeding for sustainable plant production. Nova PhD course, January 2012 at Röstånga in Southern Sweden. Nova is a Nordic University Network.
Pre-breeding provides an important element in broadening the genetic diversity and introducing new and useful traits and properties to the food crops. New traits introduced in pre-breeding activities are not least important to meet the new challenges agriculture will face from the on-going climate change. The needed genetic diversity is often available outside of the genepool of cultivars and elite breeding lines. And sources of novel genetic diversity such as the primitive crops and even the wild relatives of the cultivated plants are expected to get increased focus when facing new challenges in agriculture.
The GBIF data portal provides information on in situ occurrences for many of the wild relatives to the cultivated plants that are not (yet) collected and accessioned by the ex situ seed genebank collections. The GBIF data portal will therefore provide a very valuable bridge between these data sources for genebank accessions and occurrence data sources outside of the genebank community. Occurrences from the GBIF data portal will assist in the identification of locations where potentially useful populations of crop wild relatives can be found. Ecological niche modeling provides a widely used approach for predicting species distributions and can be used for this purpose.
Recent work on predictive modeling to identify a link between useful crop traits and eco-geographic data associated with the source locations for germplasm may have particular value for pre-breeding efforts. The Focused Identification of Germplasm Strategy (FIGS) provides and approach for efficient identification of germplasm material with new and useful genetic diversity for a target trait property. Such predictive modeling approaches are of particular interest when performing pre-breeding because of the high costs related to working with this material. Cultivated plants are domesticated for properties and traits such as non-shattering seed behavior and more uniform harvest time that makes conducting agricultural experiments easier and less costly. Non-domesticated germplasm material and also the older cultivars and landraces have many agro-botanical traits that was moderated in modern cultivars to better suit agricultural practices and efficiency. Pre-breeding is largely about removing such undesired traits from the non-cultivated and less intensively domesticated material while maintaining potentially useful traits.
Nova PhD course home page:
http://www2.nova-university.org/chome/cpage.php?cnr=03-110404-412
https://sites.google.com/site/novaplantimprovementnetwork/home/phd-course-in-sweden-january-2012
FIGS workshop in Madrid, PGR Secure (9 to 13 January 2012)Dag Endresen
We organized last week (9 to 13 January 2012) a workshop in Madrid (Spain) on predictive characterization using the Focused Identification of Germplasm Strategy (FIGS) for wild relatives to the cultivated plants (crop wild relatives). This workshop was part of the EU funded PGR Secure project [1] (EU 7th framework programme). The objective of this workshop was to use predictive computer modeling with R [2] for data mining (trait mining) to identify genebank accessions and populations of crop wild relatives with a higher density of genetic variation for a target trait property (response, independent variable) using climate data and other environment data layers as the explanatory or independent multivariate variables. We have previously validated the FIGS approach for landraces of wheat and barley [3]. This study was one of the first attempts to validate the FIGS approach for other crops as well as for crop wild relatives (CWR). The crop landraces and crop wild relatives included in this study was: Oats (Avena sp.), Beet (Beta sp.), Cabbage and mustard (Brassica sp.), Medick including alfalfa, lucerne (Medicago sp.). We made good progress on the methodology, but also faced some major obstacles related to data availability.
Endresen, D.T.F. (2010). Predictive association between trait data and ecogeographic data for Nordic barley landraces. Crop Sci. 50(6):2418-2430. doi: 10.2135/cropsci2010.03.0174
Endresen, D.T.F., K. Street, M. Mackay, A. Bari, and E. De Pauw (2011). Predictive Association between Biotic Stress Traits and Eco-Geographic Data for Wheat and Barley Landraces. Crop Science 51 (5): 2036-2055. doi: 10.2135/cropsci2010.12.0717
Endresen, D.T.F. (2011). Utilization of Plant Genetic Resources: A Lifeboat to the Gene Pool [PhD Thesis]. Copenhagen University, Faculty for Life Sciences, Department of Agriculture and Ecology. Printed at Media-Tryck, Lund University Press, April 2011. Available at: http://goo.gl/pYa9x (PDF 37 MB). ISBN: 978-91-628-8268-6.
Bari, A., K. Street, M. Mackay, D.T.F. Endresen, E. De Pauw, and A. Amri (2012). Focused identification of germplasm strategy (FIGS) detects wheat stem rust resistance linked to environmental variables. Genetic Resources and Crop Evolution (in press). doi:10.1007/s10722-011-9775-5
Endresen, D.T.F., K. Street, M. Mackay, A. Bari, A. Amri, E. De Pauw, K. Nazari, and A. Yahyaoui (2012). Sources of Resistance to Stem Rust (Ug99) in Bread Wheat and Durum Wheat Identified Using Focused Identification of Germplasm Strategy (FIGS). Crop Science 52, in press. doi: 10.2135/cropsci2011.08.0427
Professor Andrew Lowe poses the question 'How can we help biodiversity adapt to the ravages of climate change?'. Andrew is the director of the Australian Centre of Evolutionary Biology and Biodiversity at the University of Adelaide, to find out more about the Centre and its many research activities visit http://www.adelaide.edu.au/environment/acebb/.
Density and distribution of chimpanzee (Pan troglodytes verus, Schwarz 1934) ...Open Access Research Paper
The loss of biodiversity mainly due to human activities is a global concern. The survival of wild mammals, including the West African chimpanzee (Pan troglodytes verus), which is considered a critically endangered species, is threatened. However, information on the status of the remaining populations of such a primate and its distribution is rarely available or out of date for some sites. This study aims at improving the knowledge of the west chimpanzee population density and distribution in Mont Sangbé National Park (MSNP), West Côte d’Ivoire, for conservation purposes. We counted chimpanzee sleeping nests along 64 line transects of one kilometer each in the forest area of the MSNP by following distance sampling methods. Then, we recorded the GPS coordinates of all signs of the presence of the species during transects and recce surveys. We observed 148 signs of the presence of chimpanzees including 94 nests counted along transects. The average density of chimpanzees in the forest area of MSNP was estimated at 0.25 individuals/km² and 0.48 individuals/km² when using a value of a lifetime of nests of 164.38 days and 84.38 days, respectively. In addition, the distribution map showed that the signs of the presence of chimpanzees are mainly observed in two areas: the southern and the north-eastern forest areas of the MSNP. We recommend the application of other survey methods (genetics, camera trapping, nest counts combined with the modeling of nest lifetime estimates) for a better understanding of the chimpanzee population ecology and for conservation management in the PNMS.
Spatial-temporal variation of biomass production by shrubs in the succulent k...Innspub Net
Forage production in arid and semi-arid rangelands is not uniform but varies with seasons and in various landscapes. The aim of this study was to investigate the spatial and temporal variation in forage production in RNP. Plants sampling was carried out in 225 plots distributed in each of the five vegetation types. In each vegetation strata, sampling points was based on proximity to an occupied stock post, a rain gauge, a foothill and flat plains. A total of were measured in the 5 study sites. Line Intercept Method in combination with harvest method were used in ground measurement of biomass production. To assess biomass production using remote sensing technique, par values were obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) imageries which consisted of 8 days composite images at spatial resolution of 1km² pixel size. There was positive correlation between line intercepts and biomass production Biomass production was higher in succulent Karoo biome than in desert biome. There was a strong relationship between biomass production with rainfall and with fpar values. Since leaf and stem succulents’ plants were found to contribute the highest amount of forage production in RNP, they should be given conservation priority.
Integrating local crowdsourced and remotely sensed data to characterize range...ILRI
Presented by Francesco Fava (ILRI), Nathan Jensen (ILRI), Lucas de Oto (Uni-Twente) and Andrew Mude (ILRI) at the CGIAR Platform for Big Data in Agriculture Convention, Nairobi, 3-5 October 2018
Agricultural Drought Severity assessment using land Surface temperature and N...John Kapoi Kapoi
This study was focused on Nakuru, a tropical region in the Rift Valley of Kenya, bounded between latitude 0.28°N and 1.16°S, and longitude 36.27° E and 36.55°E. The main The main aim of this
research is to assess the agricultural drought in high potential region of Kenya with an objective of mapping the agricultural drought severity levels, assessing the precipitation and normalized difference
vegetation index deviation over its long term mean average in the region and to generate land surface temperature and emissivity maps to compare the surface temperature proportion during the drought
and normal period.
The data was obtained from NOAA-AVHRR, LANDSAT TM and ETM+ and was processed with ERDAS Imagine and GIS software of the Environmental Systems Research Institute (ESRI).The land
surface temperature was derived using Planck’s radiative principles. The thermal band of Landsat TM was utilized to extract the radiance and brightness temperature. The brightness temperature was
combined with surface emissivity to derive the land surface temperature (LST) while NDVI was derived from bands 3 and 4 and its result was divided by the LST to determine the moisture levels.
The products were classified into five main classes to reflect the moisture levels. Rainfall and NDVI performance was also processed from NOAA AVHRR and long term mean established and compared
with the specific year of study performance.
The result of the study revealed that NOAA-AVHRR data offers very useful information in drought monitoring and early warning, LST and NDVI is useful in moisture level mapping that can be used
to detect drought and the drought in Nakuru is characterized by both low and high temperatures that exacerbates the crop failure.
Landscape-Scale Assessments for Strategic Targeting of
Climate Smart Agriculture (CSA) Practices in East Africa
Poster presented at the 3rd Global Science Conference on Climate-Smart Agriculture in Montpellier.
Read more: http://ccafs.cgiar.org/3rd-global-science-conference-%E2%80%9Cclimate-smart-agriculture-2015%E2%80%9D#.VRurLUesXX4
performance evaluation and characterization of wetted soil parameters of impr...IJEAB
Field study was conducted to evaluate the emission uniformity (EU), global coefficient of variation (CGv), emitter flow variation (Qvar) and distribution uniformity (DU), and determine the wetted radius (rw) on soil surface of improvised medi-emitters installed in a tomato field. Soil water content (SWC) at four layers was determined after different periods of irrigation. Radius of wetted soil surface was determined and predicted. Irrigation frequency had no significant effect on the average discharge rate of the medi-emitters throughout the growing cycle. Average Qvar and CGv were significantly (P=0.05) influenced by the frequency of application while the EU and DU did not significantly (P=0.05) differ among the treatments. There were significant differences in the average values of SWC in different soil layers under the different periods of irrigation. Both the observed and calculated rw on the soil surface were fitted with fourth order polynomial. The model performance parameters of MAE and RMSE between the calculated and observed radii were low, indicating good prediction. Medical infusion set can successfully replace the more expensive conventional emitters for drip irrigation system.
The significance of indigenous weather forecast knowledge and practices under...Premier Publishers
This paper discusses the implication of indigenous knowledge-based weather forecasts (IK-BFs) as a tool for reducing risks associated with weather variability and climate change among smallholder farmers on the south eastern slopes of Mount Kilimanjaro in Moshi Rural District of Tanzania. Participatory research approaches and household surveys were used to identify and document past and existing IK-BF practices. Local communities in the study transect use traditional experiences and knowledge to predict impending weather conditions by observing a combination of locally available indicators: plant phenology (40.80%), bird behaviour (21.33%), atmospheric changes (10.40%), insects’ behaviour (7.20%), environmental changes on Kilimanjaro, Pare and Ugweno mountains (4.80%), astronomical indicators (4.8%), animal behaviour (4.00%), water related indicators (3.73%) and traditional calendars (2.93%). The study established that 60% of farmers use and trust IK-BFs over modern science-based forecasts (SCFs). Although about 86.3% of respondents observed some correlation between IK-BFs and SCFs, and 93.6% supported integration of the two sets of information, the nature and extent of their correlation is not yet established. We none the less recommend that IK-BFs be taken into relevant national policies and development frameworks to facilitate agro-ecological conservation for use and delivery of effective weather and climate services to farming communities.
Diversity of plant parasitic nematodes associated with common beans (Phaseolu...Innspub Net
Common beans (Phaseolus vulgaris L.) are the most important legume staple food in Kenya coming second to maize. In Central Highlands of Kenya, the 0.4-0.5ton ha-1 output is below the genetic yield potential of 1.5-2ton ha-1 partly due pests and diseases. Plant parasitic nematodes (PPN) have been reported to cause yield losses of up to 60% on beans. Though bean production is important in the Central highlands of Kenya, information on PPN associated with the beans in the region is lacking. This study was therefore undertaken to establish the diversity of PPN associated with common beans and to assess the root knot nematode damage on beans in the region. The study covered 50 farms (32 in Kirinyaga and 18 in Embu Counties) distributed in eight localities namely Kibirigwi (L1), Makutano (L2), Kagio (L3), Mwea (L4) and Kutus (L5) in Kirinyaga County and Nembure (L6), Manyatta (L7) and Runyenjes (L8) in Embu County and covering three Agro Ecological Zones (AEZs); UM2 (L1, L2, L3 & L4), UM3 (L5, L7 & L8) and UM4 (L6) AEZs. Manyatta (L7) and Nembure (L6), had the highest and second highest gall indices, respectively, while Kibirigwi (L1), Makutano (L2) and Mwea (L4) had some of the lowest gall indices. The most common PPN in bean roots were Meloidogyne spp. Pratylenchus spp. and Scutellonema spp. with a frequency of 94.38%, 78.25% and 59.13%, respectively. This further confirm the importance of these nematodes in bean production systems. Upper Midland 3 (UM3) AEZs and UM4 had higher nematode population densities and diversity than UM2. Disease severity and nematode composition and distribution were notably low in the irrigated areas Kibirigwi, Kagio and Mwea compared to rain-fed areas such as Makutano, Nembure and Manyatta.
Climate and potential habitat suitability for cultivation and in situ conserv...Innspub Net
Sustainable management actions are needed for several indigenous agro forestry plant species like the black plum (Vitex doniana Sweet) because they are facing increasing pressures due to the rapid human growth and threats such as climate change. By combining species distribution modelling using the Maximum Entropy Algorithm (Max Ent) and representation gap analysis, this study accessed the impacts of current and future (2050) climates on the potential distribution of Vitex doniana in Benin with insight on the protected areas network (PAN). The model showed a high goodness-of-fit (AUC = 0.92 ± 0.02) and a very good predictive power (TSS = 0.72 ± 0.01). Our findings indicated annual mean rainfall, annual mean diurnal range of temperature and mean temperature of the driest quarter as the most important predictors driving the distribution of V. doniana. Under current climate, about 85 % of Benin area is potentially suitable for its cultivation. This potential suitable area is projected to increase by 3 to 12 % under future climatic conditions. A large proportion (76.28 %) of the national PAN was reported as potentially suitable for the conservation of the species under current climate with increase projections of 14 to 23 % under future climate. The study showed that V. doniana can be cultivated in several areas of Benin and that the PAN is potentially suitable for its conservation. These findings highlighted some of the opportunities of integrating V. doniana in the formal production systems of Benin and also its potentialities in ecosystems restoration under the changing climate. Get the full articles at: http://www.innspub.net/ijaar/climate-and-potential-habitat-suitability-for-cultivation-and-in-situ-conservation-of-the-black-plum-vitex-doniana-sweet-in-benin-west-africa/
Professor Andrew Lowe poses the question 'How can we help biodiversity adapt to the ravages of climate change?'. Andrew is the director of the Australian Centre of Evolutionary Biology and Biodiversity at the University of Adelaide, to find out more about the Centre and its many research activities visit http://www.adelaide.edu.au/environment/acebb/.
Density and distribution of chimpanzee (Pan troglodytes verus, Schwarz 1934) ...Open Access Research Paper
The loss of biodiversity mainly due to human activities is a global concern. The survival of wild mammals, including the West African chimpanzee (Pan troglodytes verus), which is considered a critically endangered species, is threatened. However, information on the status of the remaining populations of such a primate and its distribution is rarely available or out of date for some sites. This study aims at improving the knowledge of the west chimpanzee population density and distribution in Mont Sangbé National Park (MSNP), West Côte d’Ivoire, for conservation purposes. We counted chimpanzee sleeping nests along 64 line transects of one kilometer each in the forest area of the MSNP by following distance sampling methods. Then, we recorded the GPS coordinates of all signs of the presence of the species during transects and recce surveys. We observed 148 signs of the presence of chimpanzees including 94 nests counted along transects. The average density of chimpanzees in the forest area of MSNP was estimated at 0.25 individuals/km² and 0.48 individuals/km² when using a value of a lifetime of nests of 164.38 days and 84.38 days, respectively. In addition, the distribution map showed that the signs of the presence of chimpanzees are mainly observed in two areas: the southern and the north-eastern forest areas of the MSNP. We recommend the application of other survey methods (genetics, camera trapping, nest counts combined with the modeling of nest lifetime estimates) for a better understanding of the chimpanzee population ecology and for conservation management in the PNMS.
Spatial-temporal variation of biomass production by shrubs in the succulent k...Innspub Net
Forage production in arid and semi-arid rangelands is not uniform but varies with seasons and in various landscapes. The aim of this study was to investigate the spatial and temporal variation in forage production in RNP. Plants sampling was carried out in 225 plots distributed in each of the five vegetation types. In each vegetation strata, sampling points was based on proximity to an occupied stock post, a rain gauge, a foothill and flat plains. A total of were measured in the 5 study sites. Line Intercept Method in combination with harvest method were used in ground measurement of biomass production. To assess biomass production using remote sensing technique, par values were obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) imageries which consisted of 8 days composite images at spatial resolution of 1km² pixel size. There was positive correlation between line intercepts and biomass production Biomass production was higher in succulent Karoo biome than in desert biome. There was a strong relationship between biomass production with rainfall and with fpar values. Since leaf and stem succulents’ plants were found to contribute the highest amount of forage production in RNP, they should be given conservation priority.
Integrating local crowdsourced and remotely sensed data to characterize range...ILRI
Presented by Francesco Fava (ILRI), Nathan Jensen (ILRI), Lucas de Oto (Uni-Twente) and Andrew Mude (ILRI) at the CGIAR Platform for Big Data in Agriculture Convention, Nairobi, 3-5 October 2018
Agricultural Drought Severity assessment using land Surface temperature and N...John Kapoi Kapoi
This study was focused on Nakuru, a tropical region in the Rift Valley of Kenya, bounded between latitude 0.28°N and 1.16°S, and longitude 36.27° E and 36.55°E. The main The main aim of this
research is to assess the agricultural drought in high potential region of Kenya with an objective of mapping the agricultural drought severity levels, assessing the precipitation and normalized difference
vegetation index deviation over its long term mean average in the region and to generate land surface temperature and emissivity maps to compare the surface temperature proportion during the drought
and normal period.
The data was obtained from NOAA-AVHRR, LANDSAT TM and ETM+ and was processed with ERDAS Imagine and GIS software of the Environmental Systems Research Institute (ESRI).The land
surface temperature was derived using Planck’s radiative principles. The thermal band of Landsat TM was utilized to extract the radiance and brightness temperature. The brightness temperature was
combined with surface emissivity to derive the land surface temperature (LST) while NDVI was derived from bands 3 and 4 and its result was divided by the LST to determine the moisture levels.
The products were classified into five main classes to reflect the moisture levels. Rainfall and NDVI performance was also processed from NOAA AVHRR and long term mean established and compared
with the specific year of study performance.
The result of the study revealed that NOAA-AVHRR data offers very useful information in drought monitoring and early warning, LST and NDVI is useful in moisture level mapping that can be used
to detect drought and the drought in Nakuru is characterized by both low and high temperatures that exacerbates the crop failure.
Landscape-Scale Assessments for Strategic Targeting of
Climate Smart Agriculture (CSA) Practices in East Africa
Poster presented at the 3rd Global Science Conference on Climate-Smart Agriculture in Montpellier.
Read more: http://ccafs.cgiar.org/3rd-global-science-conference-%E2%80%9Cclimate-smart-agriculture-2015%E2%80%9D#.VRurLUesXX4
performance evaluation and characterization of wetted soil parameters of impr...IJEAB
Field study was conducted to evaluate the emission uniformity (EU), global coefficient of variation (CGv), emitter flow variation (Qvar) and distribution uniformity (DU), and determine the wetted radius (rw) on soil surface of improvised medi-emitters installed in a tomato field. Soil water content (SWC) at four layers was determined after different periods of irrigation. Radius of wetted soil surface was determined and predicted. Irrigation frequency had no significant effect on the average discharge rate of the medi-emitters throughout the growing cycle. Average Qvar and CGv were significantly (P=0.05) influenced by the frequency of application while the EU and DU did not significantly (P=0.05) differ among the treatments. There were significant differences in the average values of SWC in different soil layers under the different periods of irrigation. Both the observed and calculated rw on the soil surface were fitted with fourth order polynomial. The model performance parameters of MAE and RMSE between the calculated and observed radii were low, indicating good prediction. Medical infusion set can successfully replace the more expensive conventional emitters for drip irrigation system.
The significance of indigenous weather forecast knowledge and practices under...Premier Publishers
This paper discusses the implication of indigenous knowledge-based weather forecasts (IK-BFs) as a tool for reducing risks associated with weather variability and climate change among smallholder farmers on the south eastern slopes of Mount Kilimanjaro in Moshi Rural District of Tanzania. Participatory research approaches and household surveys were used to identify and document past and existing IK-BF practices. Local communities in the study transect use traditional experiences and knowledge to predict impending weather conditions by observing a combination of locally available indicators: plant phenology (40.80%), bird behaviour (21.33%), atmospheric changes (10.40%), insects’ behaviour (7.20%), environmental changes on Kilimanjaro, Pare and Ugweno mountains (4.80%), astronomical indicators (4.8%), animal behaviour (4.00%), water related indicators (3.73%) and traditional calendars (2.93%). The study established that 60% of farmers use and trust IK-BFs over modern science-based forecasts (SCFs). Although about 86.3% of respondents observed some correlation between IK-BFs and SCFs, and 93.6% supported integration of the two sets of information, the nature and extent of their correlation is not yet established. We none the less recommend that IK-BFs be taken into relevant national policies and development frameworks to facilitate agro-ecological conservation for use and delivery of effective weather and climate services to farming communities.
Diversity of plant parasitic nematodes associated with common beans (Phaseolu...Innspub Net
Common beans (Phaseolus vulgaris L.) are the most important legume staple food in Kenya coming second to maize. In Central Highlands of Kenya, the 0.4-0.5ton ha-1 output is below the genetic yield potential of 1.5-2ton ha-1 partly due pests and diseases. Plant parasitic nematodes (PPN) have been reported to cause yield losses of up to 60% on beans. Though bean production is important in the Central highlands of Kenya, information on PPN associated with the beans in the region is lacking. This study was therefore undertaken to establish the diversity of PPN associated with common beans and to assess the root knot nematode damage on beans in the region. The study covered 50 farms (32 in Kirinyaga and 18 in Embu Counties) distributed in eight localities namely Kibirigwi (L1), Makutano (L2), Kagio (L3), Mwea (L4) and Kutus (L5) in Kirinyaga County and Nembure (L6), Manyatta (L7) and Runyenjes (L8) in Embu County and covering three Agro Ecological Zones (AEZs); UM2 (L1, L2, L3 & L4), UM3 (L5, L7 & L8) and UM4 (L6) AEZs. Manyatta (L7) and Nembure (L6), had the highest and second highest gall indices, respectively, while Kibirigwi (L1), Makutano (L2) and Mwea (L4) had some of the lowest gall indices. The most common PPN in bean roots were Meloidogyne spp. Pratylenchus spp. and Scutellonema spp. with a frequency of 94.38%, 78.25% and 59.13%, respectively. This further confirm the importance of these nematodes in bean production systems. Upper Midland 3 (UM3) AEZs and UM4 had higher nematode population densities and diversity than UM2. Disease severity and nematode composition and distribution were notably low in the irrigated areas Kibirigwi, Kagio and Mwea compared to rain-fed areas such as Makutano, Nembure and Manyatta.
Climate and potential habitat suitability for cultivation and in situ conserv...Innspub Net
Sustainable management actions are needed for several indigenous agro forestry plant species like the black plum (Vitex doniana Sweet) because they are facing increasing pressures due to the rapid human growth and threats such as climate change. By combining species distribution modelling using the Maximum Entropy Algorithm (Max Ent) and representation gap analysis, this study accessed the impacts of current and future (2050) climates on the potential distribution of Vitex doniana in Benin with insight on the protected areas network (PAN). The model showed a high goodness-of-fit (AUC = 0.92 ± 0.02) and a very good predictive power (TSS = 0.72 ± 0.01). Our findings indicated annual mean rainfall, annual mean diurnal range of temperature and mean temperature of the driest quarter as the most important predictors driving the distribution of V. doniana. Under current climate, about 85 % of Benin area is potentially suitable for its cultivation. This potential suitable area is projected to increase by 3 to 12 % under future climatic conditions. A large proportion (76.28 %) of the national PAN was reported as potentially suitable for the conservation of the species under current climate with increase projections of 14 to 23 % under future climate. The study showed that V. doniana can be cultivated in several areas of Benin and that the PAN is potentially suitable for its conservation. These findings highlighted some of the opportunities of integrating V. doniana in the formal production systems of Benin and also its potentialities in ecosystems restoration under the changing climate. Get the full articles at: http://www.innspub.net/ijaar/climate-and-potential-habitat-suitability-for-cultivation-and-in-situ-conservation-of-the-black-plum-vitex-doniana-sweet-in-benin-west-africa/
Forest and agroforesty options for building resilience in refugee situations:...World Agroforestry (ICRAF)
Humanitarian Networks and Partnerships Week (HNPW) 2020
Climate Crisis Inter-Network
"Fit for Purpose? Current Tools and Approaches to Mitigate Climate Risks in Humanitarian Settings"
HLPE 2019. Agroecological and other innovative approaches for sustainable agriculture and food systems that enhance food security and nutrition. A report by the High Level Panel of Experts on Food Security and Nutrition of the Committee on World Food Security, Rome
Vulnerabilities of forests and forest dependent people
Peter Minang, FTA, ICRAF
Social and environmental justice as a trigger of robust ambitious climate action and prosperous future for all
Chilean pavilion, COP 25, Madrid, 7th December 2019
An increasing multitude of insect pests and pathogens is targeting indigenous trees of natural forests, agroforestry systems, and exotic trees in planted forests in Africa. This is raising major concerns for a continent already challenged by adaptations to climate change, as it threatens a vital resource for food security of rural communities, economic growth, and ecosystem conservation. The accidental introduction through trade of non‐native species in particular is accelerating, and it adds to the damage to tree‐based landscapes by native pests and diseases. Old‐time and new invaders heavily impact planted forests of exotic eucalypts, pines, and acacias, and are spreading quickly across African regions. But many non‐native pathogens are recently found affecting important indigenous trees.
Decent work and economic growth: Potential impacts of SDG 8 on forests and fo...World Agroforestry (ICRAF)
This paper assesses the potential impact of Sustainable Development Goal (SDG) 8 on forests and forest-dependent people. The concepts of decent work and economic growth are put in the context of predominant development theories and paradigms (modernization, economic growth, basic needs, sustainable development) which shape the agendas of governments, private sector, civil society, and investors. These stakeholders pursue different goals and interests, with uneven prioritization of SDG 8 targets and mixed impacts on forests and livelihoods.
Forest conservation and socio-economic benefits through community forest conc...World Agroforestry (ICRAF)
With an extension of 2.1 million ha, the Maya Biosphere Reserve (MBR) in Petén, Guatemala is the largest protected area in Central America. To reconcile forest conservation and socio-economic development, community forest concessions were created in its Multiple Use Zone (MUZ) in the late 1990s and early 2000s. Operated by a community forest enterprise (CFE), and with a cycle of 25 years, the concessions grant usufruct rights to local communities on an area of about 400,000 ha. Currently, nine concessions are active, while the contracts of two concessions were cancelled and the management plan of another suspended.
Sustainable land management for improved livelihoods and environmental sustai...World Agroforestry (ICRAF)
A healthy viable multifunctional landscape has the capability of supporting sustainable agricultural productivity, providing agroforestry and forest products (timber, fuel wood, fruits, medicine, fertilizer, gum etc.) for the sustenance of mankind while providing other environmental services. However these products are increasingly becoming unavailable due to declining soil fertility, climatic extremes, and high costs of inputs. Identifying low-cost, sustainable ways to attain food security and sustainable environment for millions of smallholder farmers in Sub Saharan Africa (SSA) remains a major developmental challenge.
Rangelands are more than just grass but rather complex and biodiverse ecosystems. Covering nearly half the world’s land area, they are in need of restoration and sustainable management.
Combining land restoration and livelihoods - examples from Niger
Systems approaches to support ecological intensification
1. Jeroen Groot, 26 March 2012
Systems approaches and tradeoffs
analysis: smallholder agriculture
Linking concepts to practice
Pablo Tittonell
Farming Systems Ecology – Wageningen University, The Netherlands
World Agroforestry Centre
13 February 2013
2. Systems approaches to ecological intensification
A Farming Systems Decalogue:
(i) Deal with farm diversity;
(ii) Deal with spatio-temporal variability;
(iii) Deal with crop-livestock interactions;
(iv) Capture decision-making on factor allocation at farm scale;
(v) Scale from cropping systems to multifunctional landscapes;
(vi) Deal with collective decisions in communities/territories;
(vii) Prospect farming futures and scenarios;
(viii) Analyse (quantify and map out) tradeoffs;
(ix) Involve actors and embrace lay knowledge systems;
(x) Inform design and targeting of innovations.
4. Anisotropy and heterogeneity
Agroecosystems: complex socio-
ecological systems
Anisotropy
Heterogeneity Ecological niches
Landscape organisation
• Connectivity
• Contingency
Soil C gradients in
Mr. Oluka’s farm Resource allocation
(Ouganda)
• Local knowledge and
perceptions of heterogeneity
• Differential responses to
interventions
Ebanyat, 2010 • Need to target technologies
5. veau d’infestation.
Anisotropy and heterogeneity
our visualiser les différences spatialisées dans la dynamique d’infestation, les
sont comparées selon les sous-zones écologiques dans la Figure 20.
ISTOM
Variation spatio-temporelle Ecole d’Ingénieur en Agro-Développement International
Index d'infestation moyen 32, Boulevard du Port F.-95094 - Cergy-Pontoise Cedex
4,5 tél : 01.30.75.62.60 télécopie : 01.30.75.62.61 istom@istom.net
4
3,5 MÉMOIRE DE FIN D’ÉTUDES
3
ZE 1
2,5 Les déterminants de la variabilité spatiale et temporelle
ZE 2
2 de la pression des pucerons et de leurs ennemis naturels
1,5
ZE 3 dans une région agricole du Kenya
1 ZE 4
0,5
0
S1 S2 S3 S4 S5
Index d’infestation moyen des champs en fonction des semaines de relevés, pour les quatre zones
s. Kajulu, Kenya, 2011
ur la base de ces données, des dynamiques d’infestation différentes se dessinent selon
s-zones écologiques. La sous-zone écologique 3 présente en effet un index
tion supérieur à celui des autres sous-zones, en début de période : jusqu’à la
ne. Or cette sous-zone écologique est caractérisée par un intense réseau de haies, et
aïque de champs très fine. Si la concentration en plantes hôtes des pucerons Aphis
(Photographie de la zone d’étude : Kajulu, Kenya (Source : André, 2011))
ra et Aphis fabae joue le rôle de refuge pour les pucerons, ceci pourrait expliquer une
on plus importante dans les champs, dès le début du cycle de culture du haricot. SOUTENU EN SEPTEMBRE 2011
Concernant l’infestation en sous-zone écologique 4, elle commence à un niveau plus André Laure Vaitiare
Promotion 97
ais sa pente est plus forte. Or cette zone-ci se caractérise par l’absence de haies, et un Stage réalisé à Kajulu, Kisumu, Kenya.
plus ouvert que les autres zones. La sous-zone écologique 3 pourrait donc jouer le Ainsi qu’à Montpellier, France
Du 15/02/11 au 31/07/11
éservoir à pucerons pour les autres sous-zones alentours. Au sein du CIRAD, URSCA.
Maîtres de stage : Pierre SILVIE et Pascal CLOUVEL
es index d’infestation dessous-zones écologiques 1 et 2 sont représentés dans ce Tuteur de mémoire : Claire LAVIGNE, INRA Avignon
e à partir d’un seul jeu de données : un seul champ était suivi pour chacune de ces
6. Heterogeneity and farmer diversity
• Esta foto muestra dos granjas contiguas, separadas por una cerca, e ilustra la diferencia entre campesinos.
Soil fertility gradients = ‘Soilscape’ + History of use + Current management
• Mientras que en el campo de la izquierda se ve un gradiente de productividad muy marcado, en el campo
del vecino la productividad es más homogénea
Tittonell et al., 2005a,b - AGEE
8. A functional typology for East African highland systems
T yp e 1
T yp e 3
MKT LV S TK
FOO D
MKT CS H
CNS
HOM E
O F F -F A R M
Wealthier households
OE
Mid-class to poor households
CS H W OOD
LV S TK
T yp e 2
Resource HO M E
CSH
allocation CNS
W OOD
strategies
MK T
LV S T K
T yp e 4
MKT LV S T K
C NS
C NS FO O D
HO ME FO O D
HO M E
O F F -F A R M
W OOD
W OOD
T yp e 5
C a sh MKT FO O D
HOM
Labour CNS E
O F F -F A R M
N u trie n ts W OOD
CSH
Tittonell et al., AGEE 2005a,b; AgSys 2010
9. Functional farm types and system states
Performance (well-being)
T2
T1
‘Stepping out’
P’’
‘Stepping up’
T3
P’
T4
‘Hanging in’
T5
R’’ R’
Resources (natural, social, human)
Tittonell (2011) Farm typologies and resilience: The diversity of livelihood strategies seen as alternative system states
11. Phot
Expected response (on-station)
Cu
Crop yield
0 0
Aboveground biomass (t ha-1) organic C (t
230 250 270 290
Building soilfrom (Kenya) (2007)
310 0
Data C Solomon et al.
Market
0 200 400 600 800 0
0 30 60 90 1
Julian day Cumulative rainfall (mm)
Saturation
Long-term soil C changes
C Effect
D of long-term manuring
Period of cultivation (years)
200
Root mean square error: 13.3 t ha-1
40 EControl F
y
Soil
25
NPK
c
Decision1.23
y = 1.01x + rule
Soil organic C (t ha-1)
ien
fic
Response
ΔY 5 t manure
Soil organic C (t ha-1)
Ef
160 2
ΔN Simulated
30
20
r 10 0.71
= t manure
NPK
120 Measured Yield response > NPK
15 cost of fertiliser
20
80 Excess
Intercept
10
10
40
5
Nutrient input
‘Sensible’ input et al.
Data from Solomonrates (2007) Data from Micheni et al. (2004) All treatments pooled
0 0
0 30 60 90
0 1 6 11 16 21 26
0 5 10 15 20 25 5
Variable of cultivation(on-farm)
Period responses (years) Period of cultivation (seasons)
Aboveground biomass (t ha-1)
Crop yield
E F Home fields
25 Poorly-responsive fertile fields
Aboveground biomass (t ha-1)
y = 1.01x + 1.23 Measured on NPK plots
r 2 = 0.71 Simulated water-limited yield
20 Responsive fields
Middle fields
Yield without nutrient inputs
15
ient
Outfields
10
grad
til i ty
5 Poorly-responsive infertile fields
il fer 2
All treatments pooled So
Water capture efficiency = 0.093*SOC + 0.016 (r 0.99)
2
Water conversion efficiency = 0.79*SOC + 86.8 (r 0.98)
0
0 5 10 15 20 25 5 10 15 20 25
Aboveground biomass (t ha )
Nutrient input
-1 Tittonell and Giller (2012)kg-1) Crop Res.
Soil organic C (g Field
12. Where do organic resources come from?
Livestock-mediated nutrient transfers
Village land
Variation in
(600 ha) manure quality across farms in western Kenya
Wealthier farmers’ cropland
Manure origin Content (%)
Dry matter FZ4 CFZ2 FZ2 N P K
(25 ha) (46 ha) (43 FZ2
ha)
-1
Experimental Farm 82 39 3 t ha 5 t ha-1
2.1 0.22 4.0
Wet and dry
Maseno FTCφ 80
season 35 1.4 0.18 1.8
grazing
Farm A 56 30 1.2 0.32 2.0
Farm B Communal grazing land 59 29
Livestock 1.0 0.30 1.6
Cattle densities
Farm C 77 25 1.0 0.10 0.6
400 ha
Farm D 43 35 1.5 0.12 3.3
Grazing of crop
Farm E 41 23 0.5
residues 0.10 0.6
φManure from the farm at Maseno Farmer Training Centre, Maseno, western Kenya; n/a: Not available
Poorer farmers’ cropland
Fodder
FZ4
Manure 86 ha
Diverse livestock
Zingore et al., 2010 production systems
13. Complexity/organisation of crop-livestock systems
Table 2: Some of the indicators used in the network analysis of N flows in agroecosystems of the highlands of East and Southern
Africa by Rufino et al. (2009) + seeds 3 3
Indicator
Fertiliser Grain
(Wealthier)
Calculation Reference
Fertiliser + seeds Grain (A) (B)
Biomass production
IndicatorsMaize
of network size, activity and integration Maize-
Maize Maize Vegetables
Sweet Ground Feed
beans potatoes Sorghum Maize Maize Vegetables
n nuts
Imports 2
IN z io crops
Food 2
(t capita-1)
Food crops
12 i 1
14
Effective # of nodes
Compost Food Random networks
n n Compost Food
Total Inflow TIN z io
Natural ecosystems
xi Finn (1980)
10 i 1 i 1 12
1 n
AgroecosystemsFood Manure 1
Household Food
Manure Waste storage Waste
Roles (#)
Pasture Household
storage
Compartmental Throughflow Ti f ij z io
Excreta x i 10
8 Excreta j 1
Excreta
Animal products Animal products
n
8
6
Fallow
Total System Throughflow 0
TST Ti 0
Excreta 0n
i 1
20 40 6 60 80
Excreta 0.00 0.05 Goats 0.10
Chicken 0.15 0.20
Feed
4 Pasture Chicken Cattle Natural ecosystems
Total System Throughput T .. T ij Patten and Higashi (1984)
Feed Livestock, j 1
i N import (kg N capita ) 4
(Medium-poor)
-1 Livestock
Finn’s cycling index
Agroecosystems
Fodder crops Feed Products N flows=30
2 Feed Products
TST c flows=43
N
2
Finn’s Cycling Index FCI Finn (1980)
Food self-sufficiency ratio
TST
0
4 0 4
Dependency Fertiliser + seeds Grain D IN / TST (C) Tigray
(D)
0 2 4 6 8 10 12 14 0 5 10 15 20 25 30 35 40 45
Indicators of organisation and diversity
Maize Maize Maize Vegetables
3 Ground Feed 3
Fertiliser + seeds Murewa
Connectivity (flows noden-1T
nuts
n 2 ) ij T ij T .. Effective # of flows
Ulanowicz (2001), Latham and
Average Mutual Information AMIFoodkcrops log 2 Feed
Scully (2002)
Maize-
Maize Maize
Kakamega
Vegetables
Ground
nuts-
i 1 j 0 T .. T i .T . j sunflower beans
2 2
Compost Food Food crops
n T T. j
(Medium-wealthy)
Statistical uncertainty (Diversity) HR
.j
log 2 Excreta
T .. T ..
Manure Waste
1 j 0 Food 1 Food
Notation: zio are N Household
inflows to each system compartment
(H i) from the external environment, xi represents the change in storage of a compartment
Waste Food
storage
and fij represents internal flows between compartments (e.g., fromExcretaHi) Excreta
H j to Chicken Household
Products Excreta
Animal products
0 Livestock
0
0 50 100 150
N flows=21
0 0.5
(Poor) 1.5
1 2
Excreta
Pasture Chicken Cattle Goats
Feed
Total system throughput Average mutual
Livestock
Fodder crops
Feed Products (kg N capita-1)
N flows=43 information (bits-1)
Ecological Network Analysis
14. Integrated soillosses
Manure storage:
fertility management
100 Improving livestock feeding and
Mineral nitrogen SUSU-1)
Pit open air
Farmers’ try-outs and adaption plots
Heap open air
manure ‘production’
Nitrogen (kg (g -1)
80 Heap under roof
60
40
20
0
0 30 60 90 120 150 180
0.6
Phosphorus (kg SU-1)
0.5
Long rains Short rains
0.4
(cropping seasons)
0.3
On-farm trials managed by researchers Rainfall Improving compost management
0.2
0.1
0
0 30 60 90 120 150 180
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1.2
Potassium (kg SU-1)
Manure (compost)
0.9 CR CR
management
A+M Addition + Maturing Addition + Maturing
0.6
0.3
Application Application
to crops Market to crops Market
0
0 30 60 90 120 150 180
Days of storage
15. Maize pr
Napier gras
Allocation of manure to different crops
20
2
10
0 0
20 40 60 80 100 120
Productivity Soil organic Cand Napier
of Maize (t ha-1)
Sweet potato 1
B 1
Maize field 3
field 1
(0.18 ha) (0.24 ha) Effects on soil fertility
Relative Napier grass yield
10
0.8 A 70
0.8
Relative maize yield
Napier grass production (t farm-1)
Napier grass Napier grass production
Maize
Maize production (t farm-1)
0.6 60 0.6
Napier grass 8
field 2
(0.15)
Manure 50
allocation 6
0.4 0.4
40
Maize field 2
strategies
(0.25 ha) (10 year 4 0.2 Maize production
30 0.2
simulations) 20
2 0 0
Napier grass 1 2 3 4 5 6 7 8 9 10
Maize field 1 Even spread Concentration
field 1 (0.15 ha)
(0.06 ha) 0 0
20 40
Manure allocation strategy
60 80 100 120
Soil organic C (t ha-1)
Manure 1
B 1
heap
pier grass yield
0.8 0.8
maize yield
Homestead
2 cows Napier grass production
0.6 0.6