Chapter27 - CM Box User Guide

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10.1. Introduction

Jan Jansonius, Clarence Sakamoto, Peter Hoefsloot

Crop monitoring in this context is a part of a Food Security Information and Early Warning System. How to set up such is system is out of scope for this tutorial. However, a good general manual is published by FAO. It is called “Handbook for Defining and Setting up a Food Security Information and Early Warning System (FSIEWS) FOOD AND AGRICULTURE ORGANIZATION OF THE UNITED NATIONS ROME – 2000” The PDF version can be downloaded here

Setting up field reporting for crop forecasting

Good crop forecasting will involve field reporting. Relying on models and satellite imagery only is not advisable.
Crop reporting is normally a regular, ongoing activity during the 12 months of the year. In each country the Ministry of Agriculture will have its own priorities for the reporting system. During the growing cycle of the main crops the reporting should be an important tool for crop forecasting. Reporting later in the agricultural year may focus more on indicators of actual crop production, such as marketing activity, prices and food supply conditions, in addition to monitoring of growing conditions during a second crop season. All major food crops should be covered, with the exception probably of industrial crops like rubber and sugarcane.
The Officer in-charge of crop monitoring at Provincial level is a key figure in the process. He will report on crop conditions, normally monthly and separately for each of the Crop Zones (strata) of his Province. His report will be based partly on the reports from his staff at District level and below, partly on other sources.
For this purpose a network of informers need to be built up, including large farmers, crop collectors/ assembly traders, millers, representatives of seed, fertilizer and pesticides companies, suppliers of agricultural credit. Their information on the crop situation needs to be tapped regularly by either visits on the spot or by telephone.
The required background information, that should be at the finger tips of all field staff includes:

the delimitation and description of Crop Zones (see chapter 3)
crop calendar, showing planting and harvesting period of crops
crop varieties and their characteristics
a description of the main pests and diseases in the area
a list of most used fertilizers, pesticides and other inputs

Variables to be monitored at the start of the planting season and during the early months of the crop cycle:

Weather conditions: early or late rains, rains insufficient, normal or excessive
Availability of seed and fertilizer as compared to last year and, if possible, actual quantities of inputs distributed or sold
Availability of tractors and fuel and/or draught animals for ploughing
Availability of labour, agricultural equipment, credit
Last years crop prices and current input prices acting as an incentive or disincentive for planting
Impact of population displacement, migration or civil strife on planting performance
Crop area planted as compared to last year (reasons for any significant change)
Crop varieties planted: short or long cycle, high or low yield potential, any changes compared to last year

Variables to be monitored, on monthly basis during the whole crop cycle:

Development of crops in the field (approximate distribution of crop area by crop stage, if planting is staggered over several weeks or months)
Crop condition as compared to normal (reasons for any significant deterioration)
Rainfall distribution as compared to normal and compared to crop water requirements
Degree of damage by pests and diseases
Effect of adverse weather conditions, dry spell, excessive rain, hailstorm, extreme temperatures
Area replanted after crop loss due to drought, severe pest attack or natural catastrophes
Water availability for irrigation

During the last month of the crop cycle an assessment is made of:

Crop area ready for harvest, i.e. planted area minus crop area lost, as compared to last year
The average expected yield level in the Crop Zone, as compared to the previous year

Standardized reporting forms are often used. Crop stage for example is reported as a code from 1 - 5 (1=emerging and early vegetative stage, 2= late vegetative stage etc.). Planted area and crop condition are also coded from 1 - 3 or 1 - 5 to indicate percentage change compared to normal or last year. Damage from insect pests and other adverse conditions is reported as “slight”, or ‘serious”.
A standardized format has both advantages and disadvantages In principle it is a convenient way to summarize crop conditions and makes it easy to compare the situation in all Districts of a Province or all Provinces of the Country. On the other hand it may lead field staff to fill out their forms superficially, crossing the box “normal” in most cases.
Therefore the reporting form should provide ample space for remarks and additional information in free format. The rule should be enforced that convincing reasons have to be given for any significant change in crop area and condition that is reported. On the other hand, if no change is reported at all, this will have to be justified as well.

Examples of crop monitoring systems

There are a number of examples of institutionalized forecasting systems. As far as the authors are aware, they are never referred to as “agrometeorological forecasting systems”, even if many are built around some form of agrometeorological core. Most forecasting and warning systems in agriculture, forests, fisheries, livestock, the health of plants, animals and humans, fires, commodity prices, food safety and food security, etc. do have an agrometeorological component. Some forecasting systems are operated commercially, for instance for high-value cash crops (coffee, sugarcane, oilpalm), directly by national or regional associations of producers. However, the majority of warning systems were established by governments or government agencies or international organisations, either because of the high costs involved (because the information serves the specific purposes of the government or organisation, e.g. for tax control systems), or because of lack of commercial interest (e.g. in food security). On the other hand, it is striking how few integrated warning and forecasting systems do exist. Clearly, fire forecasting, crop yield forecasting, pest forecasting and many other systems have a number of data and methods in common. Yet, they are mostly operated as parallel systems.
Good examples of pest and disease warning systems can be found in Canada, where pest warning services are primarily the responsibility of the provincial governments. In Quebec, warning services are administered under the Réseau d'Avertissements Phytosanitaires (RAP). The RAP was established in 1975 and includes ten groups of experts, 125 weather stations and covers 12 types of crops. Warnings and other outputs from the RAP can be obtained by E-mail, fax or internet. Warning and forecasting systems have recently undergone profound changes linked with the generalisation of the internet. The modern systems permit both the dissemination of forecasts and the collection of data from the very target of the forecasts. Agricultural extension services usually play a crucial role in the collection of data and thedissemination of analyses of forecasting systems. In addition to providing inputs, users can often interrogate the warning system. Light leaf spot (Pyrenopeziza brassicae) is a serious disease of winter oilseed rape crops in the United Kingdom. At the start of the season, a prediction is made for each region using the average weather conditions expected for that region. Forecasts available to growers over the Internet are updated periodically to take account of deviations in actual weather from the expected values. The recent addition of active server page technology has allowed the forecast to become interactive. Growers can input three pieces of information (cultivar choice, sowing date and autumn fungicide application information) which are taken into account by the model to produce a risk assessment that is more crop and location specific. Before they become operational, forecasting systems are often preceded by a pilot project to fine-tune outputs and consolidate the data collection systems. A good example is provided by PAFAS (Pilot Agrometeorological Forecast and Advisory System) in the Philippines because of the number of institutional users involved. The general objectives of the proposed PAFAS were to provide meteorological information for the benefit of agricultural operations (observation and processing data) and to issue forecasts, warnings, and advisories of weather conditions affecting agricultural production within the pilot area (Lomotan, 1988). This section emphasizes that few warning systems can properly assess the damage caused by extreme agrometeorological events to the agricultural sector. Such damage may be significant; it may reach the order of magnitude of the GNP growth. For many disaster-prone countries, agricultural losses due to exceptional weather events are a real constraint on their global economy. The indirect effects of disasters on agriculture may last long after the extreme event took place, when infrastructure or slow growing crops (e.g. plantations) were lost. The time needed to recover from some extreme agrometeorological events ranges from months to decades.
Some more examples of crop monitoring systems have been documented by Jan Jansonius in an Annex to the "Guidelines for Crop Forecasting". These examples are available here

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 [http://www.hoefsloot.com/downloads/Manual%20of%20FSIEWS.pdf here]
-===Crop forecasting, how it is often done.===
+===Setting up field reporting for crop forecasting===
+Good crop forecasting will involve field reporting. Relying on models and satellite imagery only is not advisable.
 Crop reporting is normally a regular, ongoing activity during the 12 months of the year. In each country the Ministry of Agriculture will have its own priorities for the reporting system. During the growing cycle of the main crops the reporting should be an important tool for crop forecasting. Reporting later in the agricultural year may focus more on indicators of actual crop production, such as marketing activity, prices and food supply conditions, in addition to monitoring of growing conditions during a second crop season. All major food crops should be covered, with the exception probably of industrial crops like rubber and sugarcane.
-The Officer in-charge of crop monitoring at Provincial level (PCO) is a key figure in the process. He will report on crop conditions, normally monthly and separately for each of the Crop Zones (strata) of his Province. His report will be based partly on the reports from his staff at District level and below, partly on other sources.
+The Officer in-charge of crop monitoring at Provincial level is a key figure in the process. He will report on crop conditions, normally monthly and separately for each of the Crop Zones (strata) of his Province. His report will be based partly on the reports from his staff at District level and below, partly on other sources.
 For this purpose a network of informers need to be built up, including large farmers, crop collectors/ assembly traders, millers, representatives of seed, fertilizer and pesticides companies, suppliers of agricultural credit. Their information on the crop situation needs to be tapped regularly by either visits on the spot or by telephone.
-A small annual budget is needed to cover maintenance and operation of a few vehicles and motorcycles, travel allowances and modest requirements for office equipment and stationary.
 The required background information, that should be at the finger tips of all field staff includes:
 ===Examples of crop monitoring systems===
-There are a number of examples of institutionalised forecasting systems. As far as the authors are aware, they are never referred to as “agrometeorological forecasting systems”, even if many are built around some form of agrometeorological core (Glantz, 2004). Most forecasting and warning systems in agriculture, forests, fisheries, livestock, the health of plants, animals and humans, fires, commodity prices, food safety and food
+There are a number of examples of institutionalized forecasting systems. As far as the authors are aware, they are never referred to as “agrometeorological forecasting systems”, even if many are built around some form of agrometeorological core. Most forecasting and warning systems in agriculture, forests, fisheries, livestock, the health of plants, animals and humans, fires, commodity prices, food safety and food
 security, etc. do have an agrometeorological component. Some forecasting systems are operated commercially, for instance for high-value cash crops (coffee, sugarcane, oilpalm), directly by national or regional associations of producers. However, the majority of warning systems were established by governments or government agencies or international organisations, either because of the high costs involved (because the information serves the specific purposes of the government or organisation, e.g. for tax control systems), or because of lack of commercial interest (e.g. in food security).
-On the other hand, it is striking how few integrated warning and forecasting systems do exist. Clearly, fire forecasting, crop yield forecasting, pest forecasting and many other systems have a number of data and methods in common. Yet, they are mostly operated as parallel systems. For a general overview of the technical and institutional issues related to warning systems, refer to the above-mentioned volume by Glantz.
+On the other hand, it is striking how few integrated warning and forecasting systems do exist. Clearly, fire forecasting, crop yield forecasting, pest forecasting and many other systems have a number of data and methods in common. Yet, they are mostly operated as parallel systems.
-Good examples of pest and disease warning systems can be found in Canada, where pest warning services are primarily the responsibility of the provincial governments. In Quebec, warning services are administered under the Réseau d'Avertissements Phytosanitaires (RAP). The RAP was established in 1975 and includes ten groups of experts, 125 weather stations and covers 12 types of crops. Warnings and other outputs from the RAP can be obtained by E-mail, fax or internet (Favrin, 2000). Warning and forecasting systems have recently undergone profound changes linked with the generalisation of the internet. The modern systems permit both the dissemination of forecasts and the collection of data from the very target of the forecasts. Agricultural extension services usually play a crucial role in the collection of data and thedissemination of analyses of forecasting systems (Gommes, 2001b, 2003a). In addition to providing inputs, users can often interrogate the warning system. Light leaf spot (Pyrenopeziza brassicae) is a serious disease of winter oilseed rape crops in the United Kingdom. At the start of the season, a prediction is made for each region using the average weather conditions expected for that region. Forecasts available to growers over the Internet are updated periodically to take account of deviations in actual weather from the expected values. The recent addition of active server page technology has allowed the forecast to become interactive. Growers can input three pieces of information (cultivar choice, sowing date and autumn fungicide application information) which are taken into account by the model to produce a risk assessment that is more crop and location specific (Evans et al., 2000). Before they become operational, forecasting systems are often preceded by a pilot project to fine-tune outputs and consolidate the data collection systems. A good example is provided by PAFAS (Pilot Agrometeorological Forecast and Advisory System) in the Philippines because of the number of institutional users involved. The general objectives of the proposed PAFAS were to provide meteorological information for the benefit of agricultural operations (observation and processing data) and to issue forecasts, warnings, and advisories of weather conditions affecting agricultural production within the pilot area (Lomotan, 1988). This section emphasizes that few warning systems can properly assess the damage caused by extreme agrometeorological events to the agricultural sector. Such damage may be significant; it may reach the order of magnitude of the GNP growth. For many disaster-prone countries, agricultural losses due to exceptional weather events are a real constraint on their global economy. The indirect effects of disasters on agriculture may last long after the extreme event took place, when infrastructure or slow growing crops (e.g.
+Good examples of pest and disease warning systems can be found in Canada, where pest warning services are primarily the responsibility of the provincial governments. In Quebec, warning services are administered under the Réseau d'Avertissements Phytosanitaires (RAP). The RAP was established in 1975 and includes ten groups of experts, 125 weather stations and covers 12 types of crops. Warnings and other outputs from the RAP can be obtained by E-mail, fax or internet. Warning and forecasting systems have recently undergone profound changes linked with the generalisation of the internet. The modern systems permit both the dissemination of forecasts and the collection of data from the very target of the forecasts. Agricultural extension services usually play a crucial role in the collection of data and thedissemination of analyses of forecasting systems. In addition to providing inputs, users can often interrogate the warning system. Light leaf spot (Pyrenopeziza brassicae) is a serious disease of winter oilseed rape crops in the United Kingdom. At the start of the season, a prediction is made for each region using the average weather conditions expected for that region. Forecasts available to growers over the Internet are updated periodically to take account of deviations in actual weather from the expected values. The recent addition of active server page technology has allowed the forecast to become interactive. Growers can input three pieces of information (cultivar choice, sowing date and autumn fungicide application information) which are taken into account by the model to produce a risk assessment that is more crop and location specific. Before they become operational, forecasting systems are often preceded by a pilot project to fine-tune outputs and consolidate the data collection systems. A good example is provided by PAFAS (Pilot Agrometeorological Forecast and Advisory System) in the Philippines because of the number of institutional users involved. The general objectives of the proposed PAFAS were to provide meteorological information for the benefit of agricultural operations (observation and processing data) and to issue forecasts, warnings, and advisories of weather conditions affecting agricultural production within the pilot area (Lomotan, 1988). This section emphasizes that few warning systems can properly assess the damage caused by extreme agrometeorological events to the agricultural sector. Such damage may be significant; it may reach the order of magnitude of the GNP growth. For many disaster-prone countries, agricultural losses due to exceptional weather events are a real constraint on their global economy. The indirect effects of disasters on agriculture may last long after the extreme event took place, when infrastructure or slow growing crops (e.g. plantations) were lost. The time needed to recover from some extreme agrometeorological events ranges from months to decades.
-plantations) were lost. The time needed to recover from some extreme agrometeorological events ranges from months to decades.
 Some more examples of crop monitoring systems have been documented by Jan Jansonius in an Annex to the "Guidelines for Crop Forecasting". These examples are available [http://www.hoefsloot.com/downloads/Guidelines-JJ%20-%20ANNEX3.pdf here]