Climate Smart Agriculture - Reducing uncertainty on what, and when to grow rice in Colombia Camilo Barrios, Daniel Jimenez, Sylvain Delerce, Diana Giraldo.

Slides:



Advertisements
Similar presentations
S C I E N C E F O R D E C I S I O N M A K E R S D E P A R T M E N T O F A G R I C U L T U R E, F I S H E R I E S A N D F O R E S T R Y S C I E N C E F.
Advertisements

Dr. Adriana-Cornelia Marica & Alexandru Daniel
WEATHER, CLIMATE, AND FARMERS: AN OVERVIEW : Roger Stone Expert meeting November 2004.
Agricultural modelling and assessments in a changing climate
Soybean seed quality response among maturity groups to planting dates in the Midsouth Larry C. Purcell & Montserrat Salmeron MidSouth Soybean Board Meeting,
LA_06: Assessment of Impacts and Adaptation Measures for the Water Resources Sector due to Extreme Events under Climate Change Conditions in Central America.
Simulating Cropping Systems in the Guinea Savanna Zone of Northern Ghana with DSSAT-CENTURY J. B. Naab 1, Jawoo Koo 2, J.W. Jones 2, and K. J. Boote 2,
Climate Variability and Climate Change: Decision Making under Uncertainty Gerrit Hoogenboom Director, AgWeatherNet & Professor of Agrometeorology Washington.
Climate Smart Agriculture East Africa Regional Knowledge Sharing Meeting Thomas Cole June 11, 2012, Addis Ababa, Ethiopia.
All the tea in China Modeling crop production in a changing climate Presented by Rebecca Nemec 5 th Annual Friedman Fellows Symposium November 17, 2012.
Development of a rice growth model for early warning and decision support systems Agriculture and Food Research Organization (NARO) Japan National Agricultural.
APPLICATION OF CLIMATE PREDICTION IN RICE PRODUCTION IN THE MEKONG RIVER DELTA (VIETNAM) Nguyen Thi Hien Thuan Sub-Institute of Hydrometeorology and Environment.
Crop Yield Appraisal and Forecasting - Decision Support under Uncertain Climates.
USE OF SEA SURFACE TEMPERATURE FOR PREDICTING OPTIMUM PLANTING WINDOW FOR POTATO AT PENGALENGAN Rizaldi Boer Bogor Agricultural University
Mechanistic crop modelling and climate reanalysis Tom Osborne Crops and Climate Group Depts. of Meteorology & Agriculture University of Reading.
Crop yield predictions using seasonal climate forecasts SIMONE SIEVERT DA COSTA DSA/CPTEC/INPE Second EUROBRISA Workshop July.
Weather forecasting using MM5 in Peru and new perspective for climate modeling Centro de Predicción Numérica del Tiempo y Clima Instituto Geofísico del.
Climate Variability and Irrigation Water Use Joel O. Paz Extension Agrometeorologist Biological and Agricultural Engineering Department The University.
Application to the rice production in Southeast Asia Rice Production Research Program Agro-meteorology Division National Institute for Agro-Environmental.
Linking Seasonal Forecast To A Crop Yield Model SIMONE SIEVERT DA COSTA DSA/CPTEC/INPE – CNPq HOMERO BERGAMASCHI UFRGS First EUROBRISA.
INTRODUCTION Weather and climate remain among the most important variables involved in crop production in the U.S. Great Lakes region states of Michigan,
Climate and Food Security Thank you to the Yaqui Valley and Indonesian Food Security Teams at Stanford 1.Seasonal Climate Forecasts 2.Natural cycles of.
Agriculture and Agri-Food Canada Canadian Agriculture and Climate Change: Challenges and Opportunities.
Famine Early Warning Systems Network CURRENT CLIMATE SITUATION AND FORECAST June 16 Kabul, Afghanistan.
Application of seasonal climate forecasts to predict regional scale crop yields in South Africa Trevor Lumsden and Roland Schulze School of Bioresources.
Impacts of Climate Change on Corn and Soybean Yields in China Jintao Xu With Xiaoguang Chen and Shuai Chen June 2014.
Developing links with agricultural research centres Andrew Challinor and Tim Wheeler.
Vulnerability and Adaptation Assessments Hands-On Training Workshop Impact, Vulnerability and Adaptation Assessment for the Agriculture Sector – Part 2.
Clyde Fraisse Agricultural & Biological Engineering Climate Information & Decision Support Systems.
AgClimate: Web-based Climate Information & Decision Aid Tools Clyde W. Fraisse Climate Extension Specialist Agric. & Biol. Engineering – IFAS University.
Scaling up Crop Model Simulations to Districts for Use in Integrated Assessments: Case Study of Anantapur District in India K. J. Boote, Univ. of Florida.
Climate Related Problem at Indramayu Kusnomo Tamkani and Rizaldi Boer Dinas Pertanian Indramayu, Bogor Agricultural University.
Approaches to Seasonal Drought Prediction Bradfield Lyon CONAGUA Workshop Nov, 2014 Mexico City, Mexico.
Use of Climate Forecast as a Tool to Increase Nitrogen Use Efficiency in Wheat Brenda V. Ortiz 1, Reshmi Sarkar 1, Kip Balkcom 2, Melissa Rodriguez 3,
© Crown copyright Met Office Case Study: Real world application of crop model impacts projections.
How USDA Forecasts Production and Supply/Demand. Overview  USDA publishes crop supply and demand estimates for the U.S. each month.  Because of the.
An Application of Field Monitoring Data in Estimating Optimal Planting Dates of Cassava in Upper Paddy Field in Northeast Thailand Meeting Notes.
Some Climate Change Adaptation Options to Agricultural Production in Uruguay The aim of proposal was to outline an Action Plan with recommendations of.
Agriculture and Water Resources Cynthia Rosenzweig and Max Campos AIACC Trieste Project Development Workshop
CLIMATE CHANGE AND PRECIPITATION NEEDS OF WINTER WHEAT Éva Erdélyi, Corvinus University of Budapest Levente Horváth, University of Debrecen
Impact of Climate Change on Water Availability Historical climatological data indicates warming in upper snow covered parts of the Indus basin and some.
Needs for research and systematic observation in Africa Bonn,3 June Birama DIARRA, Chief Division Research and.
Global Change Impacts on Rice- Wheat Provision and the Environmental Consequences Peter Grace SKM - Australia Cooperative Research Centre for Greenhouse.
Maria del Carmen Vera-Diaz Robert K. Kaufmann Daniel C. Nepstad Peter Schlesinger MODELING SOYBEAN EXPANSION INTO AMAZON BASIN Institutions: Funding: Conference.
Exploring management options for more resilient and efficient systems Southern Region Extension Climate Academy (SRECA) September 3-5, 2014 Athens, GA.
Integrating Climate Variability and Forecasts into Risk- Based Management Tools for Agricultural Production and Resource Conservation Jean L. Steiner Jurgen.
1.How weather forecasting is useful? 2.What is the aim of weather forecasting? 3.What is weather forecasting? 4.How weather is predicted? 5.How reliable.
Sirius wheat simulation model: development and applications Mikhail A. Semenov Rothamsted Research, UK IT in Agriculture & Rural Development, Debrecen,
AgClimate View Conference/Meeting Name Location Date Presenter University
Dr. Joe T. Ritchie Symposium : Evaluation of Rice Model in Taiwan Authors : Tien-Yin Chou Hui-Yen Chen Institution : GIS Research Center, Feng Chia University,
U2U Tools and Educational Resources U2U Training Webinar May 6, 2015 Chad Hart Iowa State University
Nyo Mar Htwe Ministry of Agriculture and Irrigation Yezin Agricultural University Ministry of Agriculture and Irrigation Yezin Agricultural University.
1. Session Goals 2 __________________________________________ FAMINE EARLY WARNING SYSTEMS NETWORK Understand use of the terms climatology and variability.
Page 1 Rice innovation Practices in Bac Lieu province 19 th December 2013 Project: Adaptation to climate change through biodiversity promotion in Bac Lieu.
Famine Early Warning Systems Network Agroclimatic Outlook April 12, 2016 / EWIWG meeting Kabul, Afghanistan.
Climate Smart Agricultural Development in the Goulburn Broken A regional climate change adaptation partnership project.
Climate Smart Agricultural Development in the Goulburn Broken
Bangkok, ECCA Training, September 1, 2017
Impact of climate change on agriculture An overview!
Climate Change and Sustainable Agricultural Intensification
An Agriculture Perspective
CLIMATE CHANGE – FUNDAMENTALS
CLIMATE AND AGRICULTURE: AGRO-CLIMATOLOGY WATER BUDGET AND CROP CALENDAR MADE BY-S hounack Mandal M.Sc Geography, SEM-1 ADAMAS UNIVERSITY TO:- Dr. Anu.
Economics of Farm Enterprises II. (Farm Management II.) MSc level
S C I E N C E F O R D E C I S I O N M A K E R S
The Country Report Title: “Technology Dissemination of Virus-free Seed Potato Production using Hydroponic Production Systems” Principal Investigator:
Climate Smart Agricultural Development in the Goulburn Broken
Upgrading production, quality and marketing – fresh herbs- Georgia
TX Envirothon Teacher Training Agriculture and Climate Change
Overview Exercise 1: Types of information Exercise 2: Seasonality
Presentation transcript:

Climate Smart Agriculture - Reducing uncertainty on what, and when to grow rice in Colombia Camilo Barrios, Daniel Jimenez, Sylvain Delerce, Diana Giraldo Crop-Climate modeling team CIAT’s Big Data team Montevideo - Uruguay 12/10/2014

Climate Smart Agriculture - Reducing uncertainty on what, and when to grow rice in Colombia Relationship between annual climate anomalies and rice yield in Colombia. We are victims of Climate variability! Arroz Rendimiento T.Máxima

How the climate variables have affected rice crop during the latest years? In the last six years the Colombian rice sector has not been exempt to low yield problems. Bajo Cauca 2007Centro Llanos 2011  High level of spikelet sterility.  Low solar radiation, increasing temperatures, low rainfall and irregular distribution.  New pests and diseases. High production costs Decreased yields more than 50% (Hernández L. 2012)

Site-Specific Management approach for reducing yield gaps

Identify limiting factors by phenological stages: Commercial data + daily weather data + machine learning (Cal/Cm2) Analysis based on phenological stages: Saldaña F733 (FEDEARROZ) Period 2007 – 2012

Identify the periods across the year with the best solar radiation demand Del resultado al impacto Adjust planting dates to get a better environmental supply The accumulated solar radiation in the grain filling stage is the limiting factor for F733 variety, in Saldaña - Tolima (Using neural Network analysis) Key message! What to do? The change!

Para FEDEARROZ 733, el clima explica aproximadamente el 35% del rendimiento Para Cimarrón Barinas, el clima explica aproximadamente el 55% del rendimiento Factores diferentes! Las variedades responden de manera diferente al clima ! Arroz Saldaña - riego Análisis basado en etapas fenológicas Saldaña (FEDEARROZ) – Periodo 2007 – 2012 Cimarron Barinas Cforest 2014 (N=78) Repuesta al clima de cada material en cada región = insumos para Fito mejoradores + ayuda a la elección para los agricultores Respuesta diferencial entre materiales Fedearroz 733 Cforest 2014 (N=267)

Generating agroclimatic seasonal forecast for rice productive regions in Colombia 8 What it consist? Establish agro-climatic forecasts using seasonal climate prediction models and crop models (mechanistic models). Agro-climatic rice productive regions Future climate conditions enter in the crop model. Crop growth projection Crop yield forecasts Crop model calibrated and evaluated

What do farmers need to know? Agroclimatic forecast Case: Monteria - Cordoba Identify the most appropriate planting date (with best environmental supply) for rice crop in the period May - Dec Actions to implement Implement seasonal weather forecasts + historical events of "El Niño“+ mechanistic crop models Projected crop performance to future climate conditions

Pronóstico de precipitación Mayo – Diciembre de 2014 Precipitación (mm) Decreased monthly rainfall Increased monthly temperatures and solar radiation Fecha de siembra 5 May25 May19 Jun14 Jul08 Jul Rendimiento (kg/ha) Select the best planting date, as a preventive measure. If farmers make the decision to plant until June 20, the yield obtained can be around 4500 kg/ha. If the crop sowings are delayed, yields will decrease. By this measure: Great economic losses to 170 rice farmers was avoided. 1,800 hectares of rice were saved to being destroyed by the intense summer.

"This weather so strange, I don't know which variety to plant" In addition to know when sow, You can also know the best cultivar to sow! Precipitation Forecast Mayo – Diciembre de 2014 Precipitación (mm) 5 May25 May19 Jun14 Jul08 Ago Rendimiento (kg/ha) Seasonal climate forecast + Crop models Selecting the best variety With sowings until early June, the yield difference between varieties will not exceed 500 kg/ha. If farmers decide to sown after June 15, the best choice will be the variety Fedearroz 733. According to this recommendation, pilot plots were established to validate the agroclimatic forecasts. Field results: Fedearroz 733: 6860 kg/ha PS Fedearroz 60: 4600 kg/ha PS

Generating new knowledge at the service of farmers Knowledge transfer to technical staff of rice producers’ association in Colombia. Knowledge transfer to farmers

Adaptation to Progressive Climate Change 1 one >> Spotlight on: The Climate Analogue Tool Thanks.