Agri Punjab, Agriculture Department Agri Sindh, Agriculture Department University of Maryland Pakistan Space and Upper Atmosphere Research Commission U.S. Department of Agriculture Food and Agriculture Organization of the United Nations
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E-learning courses on agriculture monitoring and statistics, and environment impact assessment

Summary

elearning summary The project "Agriculture Information System - Building Provincial Capacity in Pakistan for Crop Estimation, Forecasting, and Reporting based on the integral use of Remotely Sensed Data", in linkage with the Global Strategy to Improve Agriculture and Rural Statistics program (GSAS) is supporting the development of e-learning courses on geospatial technologies for agriculture statistics, monitoring and environment impact assessment.

FAO and the Department of Statistical Sciences, University of Bologna, Italy, have signed a letter of agreement for the development of 3 comprehensive curricula/modules and learning materials related to geospatial information and technology for agriculture statistics, monitoring and environment impact assessment for multiple scopes, in collaboration with the Sindh Agriculture University Tandojam, University of Punjab and SUPARCO (Pakistan) and the ITC University of Twente (Netherlands).

Background

Field inventories, data collection and interpretation are essential activities for the agricultural sector for the monitoring and evaluation of crop status and yields. Statistical methods are key tools for the design of field surveys as well as for the analysis, interpretation and presentation of the field data collected. Also for disseminating results, statistical techniques are important for successfully informing decision makers and the public in general.

The three proposed training modules provide a selection from optional additional and/or advanced topics and studies, focusing on the fundamentals of agricultural statistics and how to generate valuable information on food production and security from field and satellite observations at national level.

Modules Detail

1. Module-1: Agricultural statistics and agricultural monitoring
Scope: To zoom-in on relevant statistical and remote sensing skills plus agricultural knowledge, needed to produce agricultural statistics, based on the combined use of geo-information and field surveys, with a focus on crop area and crop yield estimation. Module-1 aims to provide the full cycle: 'from concepts to method to reporting'.

2. Module-2: Advanced Remotely Sensed Survey processing and analysis
Scope: An approach for improved mapping and differentiating spatial-temporal facts at country-level (agro-environmental stratification), using the best remotely sensed data and most modern interpretation/ analysis methods, to create opportunities for improved mapping, interpretation, monitoring and modelling. Module-2 aims to zoom-in on a key improvement to 'method' including additional 'gains' when adopted.

3. Module-3: Monitoring agriculture and agro-environment and assessing the impact of intensification
Scope: To provide a comprehensive overview of available data and logic to detect, using spatial/temporal (near-real time) data/imagery through monitoring and assessments, trends and changes in practiced agricultural systems and their environments. Promote use of the latest open-source data, technologies and infrastructure. Module-3 provides a mix of remotely sensed data presently studied by academics to monitor and assess impacts/performance, plus a novel land cover change-detection method.

Curricula outline

1. CURRICULUM 1: Statistical methodology for using geospatial technology for agriculture statistics

sample design for area frame in Punjab, Pakistan (Figure: Monitoring of crops (wheat) during growing season by using MODIS NDVI and SPOT images, Pakistan)
The curriculum on statistical methodology for using geospatial technology for agriculture statistics addresses and explains the main statistical aspects behind the use of geospatial technology for producing reliable and timely agriculture statistics.

Different kinds of geospatial technology are taken into consideration, namely high and very high resolution satellite images, aerial photos and geographic position systems. Advantages, constraints and requirements are also highlighted.

This curriculum covers various statistical aspects, such as the objectives of agricultural surveys, main types of agricultural probability sample surveys and corresponding estimation methods, data processing and analysis and resources required. The use of remote sensing data at the design level (area frame construction and stratification) as well as at the estimator level is also addressed.

  1. AGRICULTURAL SURVEY OBJECTIVES
  2. MAIN TYPES OF AGRICULTURAL PROBABILITY SAMPLE SURVEYS
  3. ESTIMATION METHODS
  4. DATA PROCESSING AND ANALYSIS OF SURVEY RESULTS
  5. IMPROVING THE PRECISION OF ESTIMATES WITH AUXILIARY VARIABLES
  6. RESOURCES REQUIRED

2. CURRICULUM 2: Statistical methodology for using geospatial technology for agri-environmental assessment

remote sensing from satellite The curriculum on statistical methodology for using geospatial technology for agri-environmental assessment describes the main statistical aspects behind the use of geospatial technology for producing reliable and timely agri-environmental statistics.

Several types of agri-environmental parameters have been proposed in the literature, but only for some of them the geospatial technology is a valuable source of information; for some other parameters, ground observation, and/or farmers' interviews are needed.

This curriculum specifies the kind of data collection needed for the different typologies of agri-environmental parameters, what can be achieved using the different kinds of geospatial technology, and how this information can be combined with other data sources for producing agri-environmental statistics. Advantages, constraints and requirements are also highlighted. The main types of probability sample surveys for agri-environmental parameters and corresponding estimation methods are described, including the use of geospatial technology at the estimator level.

  1. AGRI-ENVIRONMENTAL PARAMETERS
  2. KINDS OF DATA SOURCE DERIVED FROM DIFFERENT KINDS OF ELABORATION OF GEOSPATIAL TECHNOLOGY
  3. MAIN TYPES OF AGRI-ENVIRONMENTAL PROBABILITY SAMPLE SURVEYS
  4. ESTIMATION METHODS
  5. IMPROVING THE PRECISION OF ESTIMATES WITH AUXILIARY VARIABLES

3. CURRICULUM 3: Statistical methodology for using geospatial technology for agricultural and agri-environmental monitoring
The curriculum on statistical methodology for using geospatial technology for agricultural and agri-environmental monitoring describes the main statistical aspects behind the use of geospatial technology for monitoring agriculture and agri-environment and focuses particularly on land cover change detection and yield monitoring and forecasting.

monitoring of crops (wheat) during growing season, Pakistan (Figure: Monitoring of crops (wheat) during growing season by using MODIS NDVI and SPOT images, Pakistan)

The land cover change detection is addressed through photointerpretation of the whole area at two different dates or of a subset of areas. The qualitative yield monitoring as well the quantitative approach to yield estimation and forecasting are described.

  1. LAND COVER CHANGE DETECTION
  2. YIELD MONITORING AND FORECASTING

Target

The modules target a broad range of producers and users of spatial and non-spatial agricultural statistics. The intended audience is analyst and/or technician in geo-information organizations and statistics assessment units of ministries of agriculture as in early warning units. Also managers and decision makers can find interesting ideas and examples amongst the provided training materials.

All modules are going to be finalized before the end of 2016.

  main links

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CASE STUDIES...


  ANNOUNCEMENT

Sep 2, 2015 - A new amendment (no. 3) was signed by USDA and FAO to formalize a no-cost extension of the project GCP/PAK/125/USA from Oct 1, 2015 to Sep 30, 2016.


  recent updates

Aug 8, 2016. E-bulletin 11, Apr-Jun 2016.


Aug 5, 2016. E-learning courses on agriculture monitoring and statistics.


Aug 5, 2016. FAO's ECONET approach and land cover change in Pakistan.


Aug 5, 2016. Training on Carbon Sequestration.


Aug 5, 2016. CRS Punjab bulletins Apr 2016.


Apr 22, 2016. Cotton Production & Analytics.


Mar 1, 2016. Testing Sentinel-2 images for crop monitoring in Pakistan.


Feb 20, 2016. Crop Information Portal training.


Jan 20, 2016. Sugarcane Production & Analytics.


Jan 20, 2016. Training on REDD+ Technology for Forest Department Officers of Punjab.


Oct 29, 2014. Agriculture Information System project: roll-out workshop.


Aug 13, 2015. RS/GIS Training in Forest Management, Lahore, 4-8 May 2015.


FAO Pakistan website >>>>

 

Forum: "Pakistan Agriculture Sector". >>>>

 

SUPARCO, the National Space Agency of Pakistan >>>>

 

Government of Sindh: Agriculture Department >>>>

 

Agri Punjab >>>>

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