About the project
The data presented in this dashboard is the output of a series of workshops and online consultations conducted as part of a Food and Agriculture Organisation of the United Nations (FAO) led, and Alliance of Bioversity International & CIAT designed project on Climate-Smart Agriculture in Pakistan. The project developed a series of District Climate Risk Profiles (DCRP's) and Climate Smart Village (CSV) plans, covering 13 districts and 43 villages from across Punjab, Sindh, and Khyber Pakhtunkhwa. The implementation of the project was supported by national partners in each of the three provinces, namely the Pir Mehar Ali Shah (PMAS) Arid Agriculture University Rawalpindi; the University of Agriculture Peshawar; and the Directorate Training and Research for Agriculture Engineering and Water management Sindh.
This dashboard acts as a centralised data store and visualisation tool for decision makers, researchers and agricultural practitioners to quickly access and review information gathered through this study, based on consultations with over 1,000 agricultural experts and farmers from across the three provinces. We see this as an important tool in helping decision makers get a quick snap shot of the major hazards, their impacts, and recommended response strategies, supporting future policy and programming around CSA in Pakistan.
The authors of this study are extremely grateful for the contribution of all of those who took part in the study and helped to shed light on the importance of CSA in supporting Pakistan's agriculture sector.
How to use the dashboard
Site selection - The first step in navigating through the dashboard is to select the region you would like to look at. This will allow you to either look at Pakistan as a whole, each of the three provinces or a single district. It is worth noting that depending on the type of information you are looking for some will be available aggregated at national or provincial levels, others will be available only at district or village level.
Cropping systems - This tab displays district level data on production area and volume for the major crops, along with the livestock headcount. It also shows the cropping calendar and hazard calendar for the villages from that district covered in the study.
Climate - The climate tab presents the results of a number of modelling exercises conducted by the Alliance of Bioversity International and CIAT's modelling team. This includes climatology data on monthly mean precipitation, minimum and maximum temperatures, modelling of future annual mean temperatures and precipitation using an ensemble of Global Circulation Models (GCM's), and the historic and future time series of a number of climatic indicators that impact agricultural production.
Climate risk - This tab presents a risk matrix generated through responses from agricultural experts in the districts on the frequency and severity of major hazards.
Climate Impacts - This tab presents the results of the climate impact analysis where value chain experts in each of the districts identified the impacts of certain hazards on key commodity value chains for the district.
Practices - Presents the types of CSA practices prioritised by experts in the district, along with the areas were they have an impact, the hazards they address, and the barriers to adoption.
Enablers - CSA enablers provide essential services and build core capacities, empowering individuals and agrarian communities to better manage their response to climate related pressures. This section looks at which types of enabler are prioritised in each of the districts.
Production
Planted area
Production
Livestock
Village
Cropping calendar
Breeding | Growing | Harvesting | Planting |
Crop / Livestock | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec |
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Hazard calendar
Hazard | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec |
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For the figure below historical data is used to measure monthly mean precipitation and maximum and minimum temperatures in the district.
Future climate data in the form of projected annual mean temperatures and precipitation was modelled using the CMIP5 model ensemble.
Given the sensitivity of crops to adverse climatic conditions modelling of future climatic conditions needs to be more targeted to the growth requirements of crops, going beyond yearly averages for temperatures and precipitation. To achieve this modelling was conducted that focused on a set of indicators critical for plant and animal health. The indicators used are described in the below table, with the analysis conducted for each of the Rabi and Kharif seasons:
Acronym | Description | Hazard |
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CDD | Drought spell. Maximum number of consecutive dry days (precipitation < 1 mm day-1). | Drought: there is a long drought spell during the growing season which reduces productivity or causes crop failure. |
NDWS | Moisture stress. Number of days with ratio of actual to potential evapotranspiration ratio below 0.5. | Drought: crops experience wilting due to constantly low soil moisture levels during the growing season. |
IRR | Irrigation water requirement. Total amount of required irrigation water to satisfy crop demand | Water scarcity: greater water requirements puts greater pressure on aquifers and rivers. |
P5D | Flooding. Maximum 5-day running average precipitation. | Flooding: too much rainfall during a week causes flooding, which causes crops to wilt |
NT35 | Heat stress. Number of days with temperature above 35ºC. | Heat stress: many hot days during the growing period slow crop growth, hinder grain filling and can lead to low productivity. |
P95 | 95th percentile of daily precipitation. Sows the level of precipitation recorded for extreme events | Flooding: Peak rainfall events increasing in intensity may result in flash flooding |
Indicators
1. Input | 2. On-farm | 3. Harvesting, storage & processing | 4. Marketing |
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Severity / Frequency | Minor severity (<10% losses) | Moderate severity (10-30% losses) | Major severity (30-50% losses) | Severe severity (>50% losses) |
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Every 10 years | ||||
Every 5 years | ||||
Every second year | ||||
Every year |