Crop Modelling & Yield Forecasting
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1 Crop Modelling & Yield Forecasting AGRICAB FINAL MEETING 23-24/03/2015 Hendrik Boogaard Alterra, WUR
2 WP31 Crop Production Systems Agricultural statistics Agro-meteorological modeling & crop forecasting Use cases in Kenya Senegal Mozambique Early warning and crop mapping Irrigation agriculture Use case in North Africa
3 Main Objectives Crop Modelling / Agro-Meteo Crop Yield Forecasting
4 Crop Modelling / Agro-Meteo FEWS-NET Early warning systems for rainfed agriculture mainly use global data on weather, EO-based vegetation indices (e.g. NDVI) and simple water balances
5 Crop Modelling / Agro-Meteo Early warning systems for rainfed agriculture mainly use global data on weather, EO-based vegetation indices (e.g. NDVI) and simple water balances FEWS-NET ICPAC
6 Crop Modelling / Agro-Meteo Early warning systems for rainfed agriculture mainly use global data on weather, EO-based vegetation indices (e.g. NDVI) and simple water balances FEWS-NET ICPAC FAO-GIEWS
7 Crop Modelling / Agro-Meteo Early warning systems for rainfed agriculture mainly use global data on weather, EO-based vegetation indices (e.g. NDVI) and simple water balances FEWS-NET ICPAC FAO-GIEWS MARS
8 Crop Modelling / Agro-Meteo Dynamic process based models: Help evaluating the integrated effect of weather, soil, crop management Deliver useful indicators for monitoring (crop specific, direct response, indication of phenological stages, extend by using weather forecast) Provide advanced indicators for statistical crop yield forecasting Enable studies on land and water management scenarios, adaptation to climate change, yield gaps, etc.
9 Crop yield forecasting Regression analysis or scenario analysis (PCA, cluster analysis, prediction) CST (CGMS Statistical Tool): Streamlines process of analysis and allows to store results and store Key: and document settings used to make a specific forecast Use common sense, look for convergence of evidence Reliable yield statistics Meaningful indicators (integration with SPIRITS)
10 Crop Modelling / Agro-Meteo EU-JRC MARS toolbox & infrastructure as an important benchmark
11 Requirements Year 1: an analysis of the current capacity was analysed All partners have (some) experience in using operational monitoring services compromising EO-based vegetation indicators (NDVI-related), rainfall, simple water balance approaches and statistical yield forecast procedures. Capacity on set-up and use of process based crop models was lacking In most cases skills in data processing and analysis was limited (e.g. RDMB, Access, data retrieval, querying)
12 Training & capacity building Year 2-3: training, demonstration & capacity building on concepts, theory, methods and software combined with hands-on training Events: 3-weeks training in Europe (Wageningen, The Netherlands) Trainings in use-case countries: national stakeholder s workshops, national technical workshops, regional workshops for West and Eastern Africa On average each use case partner received at least 3 trainings Conferences Training material (WOFOST, CST) & reporting
13 Training & capacity building Participants of the Alterra training Training session at Alterra Field trip to land reclamation projects - dyke Field trip to land reclamation projects potato fields
14 Training & capacity building Hands-on training on crop yield forecasting CSE Hands-on training on crop growth modeling and crop yield forecasting DRSRS Hands-on training on crop growth modeling and crop yield forecasting INAM Hands-on training on regional crop modelling and crop yield forecasting DRSRS
15 Building regional crop model prototypes Daily weather data (archive and NRT-feed) are key For prototyping: readily available JRC MARS-ECMWF at 0.25 grid For operations: alternatives are needed especially for sustained NRT Availability of weather data in GEONETCast-EUMETCast dissemination systems: Rainfall: TAMSAT TARCAT, RFE, CHIRPS, TRMM/GPM (not available) Radiation: Down-welling Surface Short Radiation Flux (DSSF) MSG Other elements (air temperature, humidity, wind speed) not available More active involvement of national meteorological services More meteo data under WMO R&D license? Other global datasets from numerical models: POWER? MARS? Report: Down-welling Surface Short Radiation Flux (DSSF) Validation! e.g. best rainfall product => composite?
16 Building regional crop model prototypes Data quality e.g. Validation of MSG radiation (Roerink et al., 2012) Validation RFE Mozambique (Toté et al., 2015 ) 16
17 Building regional crop model prototypes Data quality e.g. Validation of MSG radiation (Roerink et al., 2012) Validation RFE Mozambique (Toté et al., 2015 ) 17
18 Building regional crop model prototypes Local crop data: Spatial distribution (arable land / crop specific mask) Sowing dates (local calendars) Future: EO-based sowing dates (+: spatial and annual variability; -: problems of crop specifity)? Observations for calibration: not easily accessible but scattered over many different organizations in different formats and varying supporting meta-data AGRICAB lacked partners from agronomy domain which complicated collection and provision of crop data Soil data from 1 to 5 million soil map Future: 1 km soil attribute data give by SoilGrids of ISRIC
19 Building regional crop model prototypes Calibration to define variety specific crop parameters Populate Access db with all data & run
20 Building regional crop model prototypes Calibration to define variety specific crop parameters Populate Access db with all data & run
21 Building regional crop model prototypes Simulated grain maize yields (left) averaged over including the coefficient of variation (right) under water-limited situation in Kenya
22 Building regional crop model prototypes Simulated grain maize yields (left) averaged over including the coefficient of variation (right) under water-limited situation in Senegal
23 Building regional crop model prototypes Simulated grain maize yield in Mozambique averaged over (left: fully irrigated conditions and right: rain fed conditions)
24 Crop yield forecasting - Senegal Indicators: Selected indicators from regional WOFOST Cumulative fapar (SPOT-VGT) from Start-Of-Season Imported into CST from SPIRITS Land cover specific aggregation to departments
25 Crop yield forecasting - Senegal Yields statistics from Department of Statistics of Ministry of Agriculture in Senegal (DAPSA) Grain maize, sorghum and millet Period , focus on period Added archive for new departments
26 Crop yield forecasting - Senegal Check outliers e.g. Department Kolda with high yields for High yields resulted from a special maize program stopped after 2005 => excluded
27 Crop yield forecasting - Senegal Tested a number of regression models based on single indicators at departmental level (for 6 dekads in August/September, ) Benchmarked against trend
28 Crop yield forecasting - Senegal Best performing single indicator models over Aug-Sep: Regional WOFOST (crop model) EO-based cumulated fapar Cumulated rainfall since 20 May Trend model
29 Impact & conclusions Major effort in training staff: Methods, data, software, operational aspects Better informed Developed ideas and plans e.g.: Kenya: stakeholders (KMD, MoA, JKUAT, DRSRS) initiated work group on regional crop models and crop yield forecast system Senegal: appointed specific staff on CST. Strong cooperation with DAPSA and EW community at large. Training material (presentations, exercises, user manuals, guidelines, prototypes,..) Robust software installation (WOFOST, CST, SPIRITS2CST)
30 Impact & conclusions Preliminary versions of regional crop models and crop yield forecast procedures but room for improvement Set-up & maintenance of regional crop model is complicated and requires in-depth knowledge on database management and use of crop models (input data, calibration): For all use cases this needs follow-up and involvement of specialized institutes (agronomy, soils, meteo) Possibly regional centres are best equipped to run such applications Use of CST tool and database for crop yield forecasting is relatively easy and can run independently from regional crop model. Key issues are preparation of proper indicators and availability of accurate yield statistics.
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