Cambio climatico, agricultura y la entomologia

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Presentacion magistral en el 38th Congreso SOCOLEN en Manizales, 27 de julio 2011.

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  • For Lobell map: Values show the linear trend in temperature for the main crop grown in that grid cell, and for the months in which that crop is grown. Values indicate the trend in terms of multiples of the standard deviation of historical year-to-year variation. ** A 1˚C rise tended to lower yields by up to 10% except in high latitude countries, where in particular rice gains from warming.** In India, warming may explain the recently slowing of yield gains. For yield graph: Estimated net impact of climate trends for 1980-2008 on crop yields for major producers and for global production. Values are expressed as percent of average yield. Gray bars show median estimate and error bars show 5-95% confidence interval from bootstrap resampling with 500 replicates. Red and blue dots show median estimate of impact for T trend and P trend, respectively. **At the global scale, maize and wheat exhibited negative impacts for several major producers and global net loss of 3.8% and 5.5% relative to what would have been achieved without the climate trends in 1980-2008. In absolute terms, these equal the annual production of maize in Mexico (23 MT) and wheat in France (33 MT), respectively.Source:Climate Trends and Global Crop Production Since 1980David B. Lobell1,*, Wolfram Schlenker2,3, and Justin Costa-Roberts1Science magazine
  • Why focus on Food securityAnd climate change has to be set in the context of growing populations and changing diets60-70% more food will be needed by 2050 because of population growth and changing diets – and this is in a context where climate change will make agriculture more difficult.
  • La contribución de la agricultura al PIB ha estadoentre 10 y 14% en los últimos 14 años. 21% empleos
  • Retos y oportunidades el paisdeberia tener una estrategia par enfrentar ambos
  • Our basic methodological approach is ecological niche modeling, a process for: mapping the known distribution of pests and diseases, analyzing the environment where these pests and diseases have been found, developing ecological niche models by analyzing the environmental characteristics of the known locations of the pest and diseases. Validating the models producing statistic and maps showing the known and predicted distributionsof the pests and diseases.On the left side of the diagram, the steps to assess the known distribution of a pest or disease are shown. The first step is to collect information on the known distribution from one of four sources: databases from virology or entomology labs, online databases, such as the global biodiversity information facility, scientific articles and surveys. The known locations should be geographically referenced with latitude and longitude coordinates.On the right side of the diagram, environmental variables that are in some way related to the distribution of pests and diseases are organized. Different sets of variables are tested and methods for reducing collinearity are employed.At the bottom of the diagram, the known distributions are overlaid on related environmental variables to produce a data set for modeling.Ecological niche models are variations on logistic regression. We have been using six different models: Bioclim, environmental distance, climate space model, support vector machine, Garp and Maxent. These models are implemented in three different computer interface environments – DIVA-GIS, open modeler and Maxent. The next step is to assess errors, sensitivity and overall model performance. In this step usually some of the input data is held back to use it for validation.The final maps can be selected according to error and sensitivity statistics or by determining where different models agree.
  • En el caso del ácaro verde de la yuca, que hasta el presente ha poblado todas las regiones de cultivo de yuca en América (su centro de origen) y Africa. Es una amenaza latente para las zonas de cultivo de yuca en Asia.
  • El punto focal para el presente continua siendo Asia, donde la producción de yuca se ha incrementando. Centro américa, sur de brasil y porsupuestoafricacontinuan en amenazaporestaespecie. El chinchellegó a Asia en el 2010, dondefuecontrolado con la avispaAnagyruslopezi, sin embargo los planes de manejointegrado de plagasdebencontemplarmedidasquepermitanevitar un rebrote de la plaga en determinadaszonas de importancia
  • Collaborative effort to develop innovative phenology modeling and risk mapping to understand the effects of rising temperatures on the distribution and severity of major insect pests on important food crops in Africa is being carried out by: CIP, IITA, the African Insect Science for Food and Health (ICIPE) and the University of Hohenheim, and partners at NARI in Africa, funded by BMZ. - The ILCYM software will be further improved and adapted to cover a wide range of insects of different orders and families.
  • Question remains whether effective management will be widely implemented, and whether management can be formulated so that it does not substantially reduce profitability or reduce other ecosystem services:Models of these processes are needed, though they are challenging to construct. the spatial and temporal correlation resulting from potential pest and disease spread means that decisions made by some parties will influence pest and disease problems experienced by other parties.
  • ANIMATED SLIDE. Example of systemic adjustments vs. structural adaptation with the coffee supply chain. Shading is one example of an adjustment, whereas larger scale, transformational, “structural adaptation” requires larger changes, which in this case can occur via certifications of climate-proofed coffee (C4 label). This creates an incentive for retailers and federations to invest in more sustainable coffee production (e.g., organic) and more resilient inputs (e.g., certain varietals). The result is adaptive change all along the supply chain.
  • ANIMATED SLIDE.
  • Cambio climatico, agricultura y la entomologia

    1. 1. Cambio climatico y agricultura en Colombia<br />Andy Jarvis, Julian Ramirez, Vanessa Herrera y Emmanuel Zapata<br />Program Leader, Decision and Policy Analysis, CIAT<br />
    2. 2. El Reto<br />
    3. 3. Concentraciones de gases de efecto invernadero<br />Implicaciones a largo plazo en el clima, y aptitud climática para producir cultivos<br />
    4. 4. Historical impacts on food security<br />Observed changes in growing season temperature for crop growing regions,1980-2008. <br />Lobell et al (2011) <br />% Yield impact <br />for wheat<br />
    5. 5. Crop suitability is changing<br />Average projected % change in suitability for 50 crops, to 2050<br />
    6. 6. Seguridad alimentario en riesgo<br />In order to meet global demands, we will need<br />60-70% <br />more food <br />by 2050.<br />
    7. 7.
    8. 8. Sources of Agricultural Greenhouse Gases<br />excluding land use change Mt CO2-eq<br />Source: Cool farming: Climate impacts of agriculture and mitigation potential, Greenpeace, 07 January 2008<br />
    9. 9.
    10. 10. Los modelos de pronostico de clima <br />
    11. 11. Variabilidad y linea base<br />+<br />Climate<br />Baseline<br />_<br />Timescale<br />Short(change in baseline and variability)Long<br />
    12. 12. Usando el pasado para aprender del futuro<br />
    13. 13. Modelos GCM : “Global ClimateModels”<br />21 “global climatemodels” (GCMs) basados en cienciasatmosféricas, química, física, biología<br />Se corre desde el pasado hasta el futuro<br />Hay diferentesescenarios de emisionesde gases<br />INCERTIDUMBRE POLITICO (EMISIONES), Y INCERTIDUMBRE CIENTIFICO (MODELOS)<br />
    14. 14.
    15. 15.
    16. 16. Entonces, ¿qué es lo que dicen?<br />
    17. 17.
    18. 18.
    19. 19.
    20. 20. Climate change<br />predictions for 2050<br />Analysis of 19 GCM Models from the Fourth IPCC Evaluation Report (2007) <br />Extracted Climate Data for Bogotá<br />By 2050 the annual temperature will rise on average 2.4 °C<br />The maximum annual temperature will rise 3°C <br />The minimum annual temperature will increase 2.3°C <br />By 2050 annual precipitation will increase by 65 millimeters. <br />“It will be hotter year-round and there will be more precipitation all over the year.”<br />
    21. 21. CCCMA-CGCM3.1<br />T47<br />BCCR-BCM2.0<br />CCCMA-CGCM2<br />CCCMA-CGCM3.1-T63<br />CNRM-CM3<br />IAP-FGOALS-1.0G<br />CSIRO-MK3.0<br />IPSL-CM4<br />MIROC3.2-HIRES<br />GISS-AOM<br />GFDL-CM2.1<br />GFDL-CM2.0<br />MIROC3.2-MEDRES<br />MIUB-ECHO-G<br />MPI-ECHAM5<br />MRI-CGCM2.3.2A<br />NCAR-PCM1<br />UKMO-HADCM3<br />
    22. 22. CCCMA-CGCM3.1<br />T47<br />BCCR-BCM2.0<br />CCCMA-CGCM2<br />CCCMA-CGCM3.1-T63<br />CNRM-CM3<br />IAP-FGOALS-1.0G<br />CSIRO-MK3.0<br />IPSL-CM4<br />MIROC3.2-HIRES<br />GISS-AOM<br />GFDL-CM2.1<br />GFDL-CM2.0<br />MIROC3.2-MEDRES<br />MIUB-ECHO-G<br />MPI-ECHAM5<br />MRI-CGCM2.3.2A<br />NCAR-PCM1<br />UKMO-HADCM3<br />
    23. 23. Mensaje 1<br />La incertidumbrecientificoSIesrelevantepara la agricultura: tenemosquetomardecisionesdentro de un contexto de incertidumbre<br />
    24. 24. Un Ejemplo<br />El susto de café en Cauca y las areas protegidas<br />
    25. 25. Desplazamiento de climas hacia altitudes mayores <br />Temperatura media reduce por 0.51oC por cada 100m en la zona cafetera. Un cambio de 2.2oC equivale a una diferencia de 440m.<br />
    26. 26. MECETA<br />Adaptabilidad para café en Cauca, Colombia<br />Cambios leves a 2020, y cambios drásticos a 2050<br />Se reduce el área cultivable. Algunas nuevas oportunidades<br />
    27. 27.
    28. 28.
    29. 29. Resultados: objetivo “Predecir la adaptabilidad”<br />D<br />
    30. 30. Un análisis sectorial<br />
    31. 31. The Model: EcoCrop<br />It evaluates on monthly basis if there are adequate climatic conditions within a growing season for temperature and precipitation…<br />…and calculates the climatic suitability of the resulting interaction between rainfall and temperature…<br />So, how does it work?<br />
    32. 32. Impactos en Colombia: cambio (%) en productividad a nivelNacional<br />
    33. 33. Cambiospromedios en adaptabilidadpordepartamento<br />
    34. 34. Dos casosdiferentes: Bolivar vs. Cauca<br />
    35. 35. Conclusionespreliminares<br />Cultivospermanentes (66.4% del PIB agropecuario de 2007) seriamenteafectados: y son cultivos de inversiones de largo plazo<br />Tema de seguridadalimentaria, y pobreza: muchas de los cultivosafectados son de agicultorespequeños (50-60%) <br />Clarasprioridadesnacionales (porejemplo. Costa Caribe, cultivosespecificos)<br />Prioridades locales: enfoquehaciaseguridadalimentaria<br />
    36. 36. Algo de insectos…<br />Y los insectos?<br />
    37. 37. ÁREA DE DISTRIBUCIÓN – NICHO ECOLÓGICO<br />El Modelo de Nicho Ecológico <br />caracteriza la distribución de una especie en un espacio definido por parámetros ambientales.<br />Describe la idoneidad del hábitat.<br />El nicho realizado < Nicho ecológico fundamental (distribución potencial).<br />A ∩ B ∩ M = P <br />P = Nicho Realizado efectivo<br />Soberón & Peterson, 2005, 3<br />Modificado de Pianka, 1976<br />
    38. 38. Methodology<br /><ul><li>Knowledge about pest and pathogens behavior - and occurrence records
    39. 39. Variable selection
    40. 40. Evaluation of niche models
    41. 41. Consensus distribution maps</li></ul>CLASSIFICATION<br />
    42. 42. Yuca: Acaroverde<br />
    43. 43. Yuca: piojoharinoso(mealybug) <br />
    44. 44. Picudo: actual, 2050 y cambio<br />
    45. 45. Distribucion del cultivo y cambio en adaptabilidad<br />
    46. 46. Haciamodelosmechanisticos<br /><ul><li>Temperature-driven phenology model for the potato tuber moth (Phthorimaeaoperculella) by CIP : good predictions of population growth.
    47. 47. Linked with GIS and atmospheric temperature the model allows simulating risk indices on a worldwide scale to predict future changes of the species distribution due to global warming.
    48. 48. Development of Insect Life Cycle Modeling software (ILCYM) to facilitate the development of insect phenology models.  </li></ul>Fuente: Kroschel et al. (in press)<br />
    49. 49. Generation index (generations/ year) under present temperature conditions<br />Fuente: Kroschel et al. (in press)<br />
    50. 50. Change in # of generations/yr by 2050 using the atmospheric general circulation model<br />Fuente: Kroschel et al. (in press)<br />
    51. 51. Sistemas complejos<br />Fuente: Garrett et al. (in press)<br />
    52. 52. Una agenda de modelacion multi-escala de plagas y enfermedadesfrentecambioclimatico<br />Retos: <br /><ul><li>Incorporacion de modelos de cultivos y ganaderia con modelos de plagas y enfermedades
    53. 53. Alta incertidumbre en la forma que los agricultoresmanejanplagas y enfermedadeshacia el futuro</li></ul>Fuente: Garrett et al. (in press)<br />
    54. 54. HaciaSoluciones<br />
    55. 55. Adaptive Adjustments<br />Structural Adaptation<br />Action: Common Code for the Coffee Community (C4) introduces an add-on climate module that would indicate when coffee producers have adapted their production system to a changing climate.<br />Result: Retailers agree to buy only C4-certified “climate-proofed” coffee. Accordingly, changes occur down the coffee supply chain, with collaborative efforts to create a more adaptive structure.<br />Action:<br />a) Shading<br />b) Changing varietals<br />c) Changing inputs<br />d) IPM<br />a) Shading<br />Result: Improved risk management at the farm level, allowing for long-term adaption.<br />C4<br />Input Providers<br />Wholesale/Retail<br />Coffee Federation<br />Consumer<br />Coffee Producers<br />Other Crops<br />
    56. 56. Transformational Adaptation<br />Action: <br />Migrate to keep farming<br />Change farming systems (agricultural)<br />Switch livelihood sources (non-agricultural)<br />Result: Long-term adaptation, but requires significant up-front transition costs.<br />Coffee Producers<br />
    57. 57. Como adaptamos?<br />Necesitamos saber quehacemos, como lo hacemos, cuando lo hacemos y donde?<br />Primer pasoesanalisar el problema<br />Segundo, analisaropciones de adaptacion<br />Evaluarcosto-beneficiopara el sector<br />Implementar<br />INVESTIGACION Y DESARROLLO TECNOLOGICO<br />POLITICAS PUBLICOS Y PRIVADOS<br />BUEN AGRONOMIA<br />
    58. 58. Conclusiones pseudo-entomologicos<br />Casserola de cambioshaciafuturo: Cambios en la geografia de cultivos, plagas, enemigosnaturales, polinizadores etc., y insectosaltamente sensible al clima<br />Pero mucho anecdoto, y pocoevidenciacientifica<br />La importancia de datos:<br />Experimentales, bajocondiciones de control pararelacionarbiologia de especies con variables climaticas<br />Captura de presencia/ausencia y/o prevalencia en campo paramonitoreo<br />Integracion de modelos y analises (relacioncultivo-manejo-insecto-clima)<br />Datos y cienciallevando a la identificacion de medidasaptas de adaptacion<br />
    59. 59. a.jarvis@cgiar.org<br />http://dapa.ciat.cgiar.org<br />http://www.slideshare.net/ciatdapa/<br />

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