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OMP Field Survey and the OMP Survey app
Preview: BMP Field Work Module
OMP Fertilizer Planner module
Preview of OMP-AMIS version 8.8.01

OMP Field Survey and the OMP Survey app

Any agronomic analysis tool such as OMP relies fundamentally on the quality and trustworthiness of the basic data that is input into the database. In an oil palm plantation, some kinds of data can be collected relatively reliably and easily using various kinds of automated or semi-automated measurements. Typical examples would include weighbridges with automatic digitalization of production data, automatic weather stations, or more recently automated palm counts using LIDAR, drone or satellite imagery. However, many crucial kinds of field data, in particular relating to field upkeep standards, harvesting performance, nutrient deficiency scoring or pest and disease monitoring still require manual evaluation and scoring by trained survey personnel in the field.

The large scale of a typical oil palm plantation offer up a number of specific challenges in this regard. As there may be multiple surveying teams within a plantation (and even more within a larger company or group with multiple plantations), one main issue is to ensure that questions, criteria and measurement techniques are standardized and clearly communicated to all surveyors. This is critical in order to reduce the subjective nature of the collected scores as much as possible. A second problem is to ensure that data is being collected at the correct locations within each block, e.g. some surveys should be carried out at designated LSU palm locations, others maybe at fruit collection points or along harvesting paths and so on. Closely related to this, in some cases it is necessary for management to be able to check that surveyors have really visited the prescribed locations and did not simply hand in surveys with fictional data, particularly for locations that are hard to reach. A third major issue is how to process and aggregate the raw data collected at many individual points within each block into meaningful data at block level. Many surveys are still carried out in paper form today, in which case the process of simply digitizing the results can be a significant amount of work, in addition to being prone to transcribing errors.

The new OMP Field Survey add-in (OMP-FS) and associated survey app for Android tablets or smartphones is a powerful tool to help tackle the issues outlined above. As data can be recorded in electronic form directly in the field, the need for any transcribing of paper survey forms is eliminated. Standardization is enhanced because all survey parameters are defined in a unified manner in the OMP-FS add-in. A key aspect in our program design has been a focus on flexibility, as field conditions and thus surveying requirements may vary widely for different plantations. The fundamental object in OMP-FS is an individual survey question. A question definition includes specification of the parameter to be surveyed, the type of entry control used to enter the data in the survey app, data types, bounds or allowed responses as well as information on how the results are to be aggregated up to block and higher spatial levels. When defining a question, it can be either based on an OMP field (see figure 1) or completely user-defined (figure 2). For OMP questions, all definitions, including any pick-up list values, are automatically taken from your main OMP data set. This guarantees that the resulting data will have the correct shape to be able to easily write the results into your main OMP block data set, which is the main advantage of an OMP question. User-defined questions on the other hand allow full flexibility in the definitions; however, the resulting data will not necessarily be directly importable into the main OMP block data. Of course, even if there is no matching OMP field you can still aggregate and analyse the results within OMP-FS.

Details of a question linked to an OMP field.

Figure 1: Details of a question linked to an OMP field.

Figure 2: Details of a user-defined question.

Once questions are defined, OMP-FS allows you to combine multiple questions into a so-called „Survey type“, which defines which questions are to be surveyed together during a single field visit. It is also possible to mark certain questions as „required“ within a survey type, in which case the surveyors will not be able to save a result with the OMP Survey App for this survey type unless all required questions have been answered. Surveyors may be grouped into surveyor groups, and it is possible to limit a list of surveyor groups that are assigned to any given survey type, to avoid that certain types of surveys are carried out by unqualified or unsuited personnel.

All definitions are transferred between the OMP-FS add-in and the Survey App via encoded text files which can simply be sent via email. The same technique is applied in reverse to send survey results from the app back to the OMP-FS add-in. Once definitions have been imported into the app, surveyors can log in, choose a survey type and start surveying with just a few taps (see figure 3). The survey entry screen displays a list of all questions for the selected survey type (figure 4), with data validation built in and the entry control matching the question definition made in OMP-FS. Figure 4 shows only two options for question entry types („slider“ and „picker“), various other options are available including free entry textboxes, „steppers“ for counting things or simple buttons for yes/no questions.

Figure 3: OMP Survey App main menu.

Figure 4: OMP Survey App questions form.

For questions of type „picker“, responses are chosen from the list defined in OMP. All question details can be viewed using the details button, including detailed descriptions of bounds or the criteria that correspond to each possible allowed response value.

Apart from helping to standardize questions and responses and eliminating the need for transcribing paper results into electronic form, the OMP Survey App can also help with the other problem mentioned above, namely ensuring that surveys are carried out at the correct position and locations are recorded correctly. For cases where results are to be taken at certain predefined points within a block, OMP-FS allows you to predefine lists of such points for each block. There are four different categories of predefined points: palm points, fruit collection points, harvest paths and other special points (to be used for special points that do not fall into the previous categories like e.g. bridges, culverts etc.). Palm points can be further subcategorized into user-defined groups such as LSU palms, BBC palms and so on. All predefined points can be georeferenced and, depending on the point type, may have some additional information such as a palm row and number. The definitions file sent to the app includes the list of blocks and all predefined points.

When entering a new survey results at a predefined point, users can choose the division, field, block and point ID from the relevant lists, reducing errors of mistyping (see figure 5). This can be even further simplified if you label special palms with cards showing a QR code with the relevant location information (see figure 6) for a sample code. The OMP Survey App can then simply scan this QR code and fill in the information automatically. Because OMP-FS allows you to see whether a point was scanned or entered manually, a system using such QR cards has the additional advantage that surveyors are really forced to go to the palm location to scan the code, i.e. there is no possibility of entering results without actually visiting the palm.

Figure 5: Survey point location for a predefined palm point.

Figure 6: QR code for encoded text:
Center D01#MT04#302E#LSU0201.

Of course, for some types of surveys it is also possible that the survey points are not known in advance (e.g. surveys looking for palms affected by a certain disease). In this case you can switch to „free entry“ point mode, in which case the point can be identified by a freely typed palm row and number or by a unique name, as required. Finally, OMP-FS also supports a point mode „block“ which may be used whenever you want to take a single result valid for the block as a whole and does not belong to any particular point within the block (e.g. an overall count of diseased palms in the block).

If your survey device supports GPS, it is straightforward to also record the GPS position at which the survey result was taken using the „Set“ button visible in figure 5. If you want, a reminder can be activated that warns users if they are trying to save the survey result without having taken the GPS position. Note that this GPS information is supplemental and independent of any geocoordinates which may have been entered as part of the definition of a predefined point. This means they provide an independent check that the survey results were taken at the correct locations. If desired, survey point geolocation data can be easily exported from OMP-FS in Excel format for mapping with any type of GIS software.

No matter what type of points the survey data is collected at, having multiple data points per block raises the question of what value for a given parameter should be assigned to the block as a whole. This is particularly important as most field management as well as almost all analysis within OMP works with block-level values. One of the most important functionalities of OMP-FS is that all survey data is automatically aggregated up to block, field, division and estate level according to the aggregation modes defined for each question. Points are grouped for aggregation either by explicitly assigning them to named „scheduled surveys“ or simply based on the survey date spread in the case of unscheduled surveys. This automatic aggregation is one of the core features which sets OMP-FS apart from other survey apps which are not custom built for the purpose of field surveying in oil palm plantations.

Figure 7: Analyzing aggregated results.

While this aggregation is already a very powerful feature for data analysis, it can also be useful to go one step further and consider calculated data which is not surveyed explicitly but which instead is calculated from the aggregated results at each spatial level. OMP-FS allows the user to specify completely general expressions or calculation formulae that are used in this manner to compute derived values from the aggregated data. One main use case for expressions is to calculate summary scores from multiple questions, such as for example an overall harvesting score for the block which is calculated from three individually surveyed questions on loose fruit collection, bunch harvesting and bunch collection. Another use case is to convert raw survey data into a form suitable for import into OMP. An example of this type of expression is shown in figure 8, where the raw survey data is a count of the number of palms affected by a certain pest or disease outbreak, and the expression converts this into a severity score of 0, 1, 2 or 3 depending on which percentage of palms in the block is affected.

Figure 8: Sample expression for an effective severity.

Overall, OMP-FS and the Survey App offer a flexible integrated solution to the problem of collecting field data in oil palm plantations. With built-in aggregation and expressions, it even goes one step further to help you transform raw survey results into meaningful information that can be translated into explicit management actions for yield improvement.

Preview: BMP Field Work Module

Besides ensuring that harvesting and fertilizer application is being carried out according to schedule, maintaining field upkeep standards is one of the most important tasks in the day-to-day work of plantation and field managers. Field work in this sense can encompass any number of different activities including for example manual or chemical weeding, pruning, drainage implementation or repairs, road repairs etc. While of course some particular tasks may only need to be carried out on demand or at irregular intervals, the majority of field upkeep tasks are typically carried out at regular intervals throughout the year. By setting up a good work plan at the beginning of the year, field managers will be able to ensure efficient distribution of labor and other material requirements, derive corresponding field work budgets and of course monitor and react whenever work is falling behind schedule. The aim of the new BMP Field Work module is to give plantation managers a powerful and flexible tool to generate field work schedules or budgets, record field work actuals and actual material and labor usage, and monitor and reconcile differences between the two. While the module will be implemented first in the context of BMP, we have designed it with the requirements of OMP in mind and are planning to transfer the module to OMP in the future.

Defining jobs and material requirements

The new module is based upon the concept of individual field work “jobs” corresponding to particular field work activities. Jobs in BMP can be defined with a short ID which can for example match a budget activity code as well as a longer, more descriptive job name. Furthermore jobs are grouped into categories and subcategories, as shown in figure 1. While users will be able to define jobs and subcategories according to their individual requirements, the main categories will be hard-coded by Agrisoft Systems to facilitate data analysis later on.

Figure 1: Job definition screen.

Figure 1: Job definition screen.

Each job can be furnished with a longer description of the precise task to be carried out, as well as a detailed specification of the materials, chemicals, equipment and labor to be used in the job. This specification includes not only a list of the materials to be used but also the desired application rate on a per ha basis, in order to be able to work out the overall material requirements from the field work budget. Specifically for chemicals, it will be possible to define a particular fraction of the block area to be treated in addition to the application rate. This feature is designed to ensure that users can enter the “real” spraying rate for activities like for example path weeding and still obtain a correct budget for the total herbicide requirements even though only a fraction of the block area is to be sprayed. Of course, all drop-down lists of materials, chemicals, equipment and labor categories will be fully customizable by the user. Specifically for chemicals there will be an additional layer of detail in that it will be possible to individually define active ingredients and products as well as their composition in terms of active ingredients.

Figure 2: Job details with specification of materials.

Figure 2: Job details with specification of materials.

Field work groups and job frequencies

Having defined a list of jobs, the next step is to work out the actual work schedule for each job, i.e. when it is to be carried out in which cableway or block. The first thing to specify for each job is the frequency, i.e. how many times a year the job is to be carried out. We anticipate that it may be necessary to have different job frequencies in different parts of the plantation. For example, it might be necessary to carry out drainage repairs more frequently in a swampy part of the plantation whereas a more hilly part of the plantation may need less drainage work but more work on activities like terracing. To handle this, it will be possible to define multiple “field work groups” in BMP and to assign cableways to different field work groups as required. Each such field work group should correspond to a family of cableways which are similar in their characteristics and have the same field upkeep requirements. The field group assignment screen, shown in figure 3, includes a helpful summary of the number of cableways and the total area that has been assigned to each group. Of course, it will be possible to copy the field work group assignment from previous budget years and to import this assignment from Excel spreadsheets.

Figure 3: Field group assignment.

Figure 3: Field group assignment.

Generating realistic work schedules

Having completed the definition of jobs and the field work group assignment, the next step is to specify the actual budget settings for each field group. The BMP field work budget generator wizard, pictured in figure 4, combines a continuous form where users can specify settings for each job with a visual calendar representation of the work schedule. This visual representation is extremely useful to get an idea of whether the schedule will lead to a relatively even distribution of work throughout the year. While figure 3 shows the budget defined in terms of weeks, it will also be possible to switch to fortnight- or month-based budgeting.

Figure 4: Budget generator wizard.

Figure 4: Budget generator wizard.

Figure 4 shows various other settings for each job in addition to the aforementioned job frequency. The round length simply specifies the number of weeks (or months/fortnights) in which the field work round should be finished, i.e. in which all cableways in the field work group should be tackled. This setting will be used by the wizard to work out the area which needs to be covered in each week. For example, in a job in which the round length is 6 weeks it will be necessary to cover approximately one sixth of the total area of the field work group in each week. Knowing how many hectares to cover each week is of course naturally leads to the question: in which order should the cableways be tackled? To allow users to specify an order which makes sense in terms of the geographical location of the cableways in the field, it is possible to assign a field work index to each cableway. This index is defined on the field work group definition screen shown in figure 3 and specifies the order in which cableways are to be tackled. Furthermore on the budget generator wizard it is possible to specify custom start cableways for each job, which is important to ensure that different jobs can easily be scheduled to fall into different weeks for a given cableway. For example, assume we have the jobs “pruning”, “drainage repairs” and “weeding” which are all to be carried out with the same frequency and the same round length. Clearly it would be undesirable to schedule all three jobs to be carried out in the same week in each cableway. By choosing suitable different starting cableways in each job it is easy to “stagger” the schedules in such a way that the 3 jobs are scheduled in successive weeks in any given cableway.

Manual budget edits and field work actuals

Although we are confident that the budget generation wizard described above will enable users to create very flexible and effective budgets using relatively few settings, it is of course possible that particular aspects of the budget will need to be tweaked even further. To do this, the BMP field work module will include a screen to manually edit the field work schedule, partially shown in figure 5. Here users will be able to individually enter or edit the area to cover in each week for every cableway and jobs.

Figure 5: Part of manual budget entry form.

Figure 5: Part of manual budget entry form.

Once the field work schedule has been defined using the tools described above, the details of the job specification will allow the program to calculate the required amounts of materials, chemicals, tools and labor for each cableway, week and job. Suitable data analysis forms and reports will be built into the program in order to be able to analyze and extract this information.
In addition to the field work budget, the new field work module will of course also be used to store records on the actual field work carried out and the materials used. The corresponding data entry form, shown in figure 6, allows users to define the area covered by job, day and cableway. Furthermore users will be able to enter the actual materials, chemicals, tools and labor used. To simplify data entry for this part, all materials, chemicals etc. that are part of the job definition will automatically be listed on the screen so that users only have to enter the amounts used. In addition, for additional flexibility it is possible to manually add records for additional or alternative materials that were used without being specified in the original job definition. Of course, it will also be possible to import all this data in the form of Excel spreadsheets.

Figure 6: Entry form for field work actuals.

Figure 6: Entry form for field work actuals.


While we have not yet started on designing the data analysis features for the field work module, it is clear that the data structure described above will allow for many exciting possibilities. For example, it will be possible to have analysis and comparison of material budgets and actuals at different time and spatial levels, reports highlighting situations where we are lagging behind schedule, GIS maps of cableways scheduled for work in the next weeks by job, and much more. We are looking forward to continuing the design and development of this new module and are confident that it will prove to be a very useful addition to both BMP and OMP in the future.

The OMP Ten Year Crop Budget

Access to accurate predictions or budgets of future production is extremely important for oil palm plantation companies. Short-term forecasts based on black bunch counts help companies to plan sales and arrange CPO transport and mill maintenance. Yearly crop budgets form an important component of the overall financial budget and help companies to plan labor allocation and timing of fertilizer application or other field work. However, beyond this also longer term crop budgets are highly important, for example to plan replanting dates, strategic sales contracts or construction of new palm oil mills.
While the first two aspects have been covered in the OMP-AMIS suite for a while with the OMP Crop Forecast and OMP Crop Budget add-ins, Agrisoft Systems now also offers a tool for the longterm budgets with the recent release of the OMP Ten Year Crop Budget (OMP-TYCB). OMP-TYCB was developed in close cooperation with our customers and has particular strengths in taking replanting plans into account.
The way in which OMP-TYCB generates crop budgets can be split into two distinct steps. In the first step, the program evaluates the predicted plantation age spread, i.e. the number of hectares per tree age and division, for each of the next ten years. In the second step, it uses user-defined yield profiles to estimate the yearly production and yield. The calculations in the first step are based on the “current” plantation age spread, i.e. the age spread in the year the budget is being generated. The program offers great flexibility both in setting up the initial plantation age profile and in entering and maintaining yield profiles and replanting plans. For further details please refer to the full length article in the 14th edition of the Agrisoft Systems Newsletter from June 2015.

OMP fertilizer planner module

Site-specific nutrient recommendations

Generating effective fertilizer recommendations is arguably the single most important task for an oil palm agronomist. This is because fertilizers form one of the biggest cost factors in an oil palm plantation, and at the same time nutrient deficiency is often the most important yield-inhibiting factor that can be affected by plantation managers.
It is clear that rough “blanket” approaches to fertilizer application, where the same fertilizer doses are applied to large portions of the plantation, are neither agronomically nor economically effective. On the other hand, every agronomist knows that generating site-specific detailed fertilizer recommendations is typically a difficult and time-consuming task. In many cases it is very important to take into account site-specific knowledge and personal experience from previous years when creating fertilizer recommendations. At the same time it is obvious that at least conceptually a block’s fertilizer requirements should be calculable from measurable parameters including site specific details like soil type and topography, environmental parameters and limitations as well as measurements of plant and soil nutrients. Various scientific studies over the years have uncovered statistically significant correlations that can be used to derive fertilizer requirement indicators, and I believe this type of scientific approach to fertilizer recommendations will only become more important in the future.

A data-driven approach to generating nutrition programmes

The key to generating block-specific fertilizer recommendations using a scientific approach as outlined above is of course the availability of the relevant background data. In this respect OMP users are at a unique advantage as the OMP database already includes a huge amount of information that can flow into the generation of fertilizer recommendations. The logical next step is therefore the inclusion of a tool into the OMP-AMIS software suite which can be used to generate fertilizer recommendations based on the available OMP data. In the shape of the OMP Fertilizer Planner, Agrisoft Systems have now begun developing exactly such a tool, which will be described in more detail in the following. In this development project we are collaborating closely with Tropical Crop Consultants Ltd., led by the well-known agronomist Dr. Thomas Fairhurst.
The OMP Fertilizer Planner Add-In will help users in creating planning fertilizer application in two largely distinct steps. In the first step, the application will generate nutrient recommendations for each block and nutrient using scientific criteria evaluated on the OMP agronomic data set. In the second step, the application will run an optimization routine to find the least costly combination of fertilizers that can be used to fulfill these nutrient recommendations. The program is explicitly designed to give users the option to work with and compare various different recommendation scenarios, making it possible to evaluate different options before deciding on the final fertilizer recommendation for the coming year.

Support for site-specific nutrient rules

Although some scientific relationships between plant tissue nutrient levels and potential yield response have recently emerged which seem to have quite broad applicability, these general rules must of course be supplemented with site-specific criteria to obtain truly realistic recommendations. To handle this, OMP Fertilizer Planner will give the user very extensive possibilities to edit and customize the assumptions and settings used in the generation of the nutrient and fertilizer recommendations. As a case in point, users will be able to define various possible doses for each relevant nutrient as well as a set of rules for when each dose should be applied. Sets of rules and doses can be saved for re-use in different scenarios, so that users will be able to quickly generate and compare several scenarios that have the same general underlying logic but differ slightly in the specific dosages and application rules. With regards to fertilizer recommendations, users will be able to enter location-specific information such as the fertilizer purchasing, transportation and application costs as well as various other settings including minimum amounts of fertilizer to apply and rounding of fertilizer doses.

Reviewing and editing recommendations

Owing to the enormous costs and importance associated with fertilizer applications, we strongly recommend that all recommendations generated by a computer algorithm should be reviewed and double-checked by agronomists before being applied in the field. Of course this applies also to the recommendations generated by the OMP Fertilizer Planner. To help with this, the program will include a variety of data analysis forms, reports and charts to show relevant information such as averaged nutrient and fertilizer recommendation rates by division, field, tree age etc. Based on this information, users should evaluate whether the current recommendations make sense or whether the assumptions used need to be reviewed and edited. Besides changing the underlying scenario assumptions, users will also have the option of manually editing recommendations for single blocks or groups of blocks, giving them full control over the resulting fertilizer recommendations.
In connection with the reviewing of recommendations mentioned above, it will be possible to flag blocks for particular attention depending on criteria defined by the user. This tool can be used to quickly highlight blocks which display certain characteristics that suggest that an agronomist should take a closer look before the program’s recommendations are blindly applied. Examples of such criteria could be wildly fluctuating yield gaps over the previous years, unrealistically high or low leaf nutrient levels, severe erosion scores or insufficient pruning.

We believe that with the features roughly outlined above the OMP Fertilizer Planner will prove to be a great addition to the OMP-AMIS software suite and will turn into an important tool for agronomists looking to generate effective site-specific nutrient and fertilizer recommendations.

Preview: OMP-AMIS 8.8.01

Despite the latest OMP-AMIS release lying just three months in the past, the Agrisoft programmers have been extremely busy and the next version of the program is nearing completion. As usual, the upcoming release will combine the addition of new program features with bug fixes and other small improvements. The present article outlines a selection of the most important changes you can look forward to in the new version, which will carry version number 8.8.01.

A long-running development project that is nearing completion concerns the development of a version of OMP-GIS compatible with ESRI ArcGIS. Together with the existing OMP-GIS application for MapInfo, this means that OMP’s thematic mapping features can be used with the two most widely-used GIS programs in the world. OMP-GIS for ArcGIS will include most of the main features familiar from the MapInfo version, including menu-driven thematic mapping, support for adding layers, exporting and printing of maps as well as multi-map layouts.

Rachis magnesium recording and improved data verification tools

Within the OMP-DBMS main program, we have aimed to round off the pest and disease reporting module introduced in version 8.7.03 by adding further relevant information to the data analysis forms and reports. With these additions, we feel that OMP’s pest and disease module is now ready to be an important tool both for yield gap analysis purposes and to investigate which planting materials and environmental conditions lead to blocks that are particularly vulnerable or resistant towards pest or disease attacks. In the OMP leaf analysis section we have added the possibility to record rachis magnesium levels, which have been recently shown to be highly relevant in the analysis of nutrient deficiencies and fertilizer recommendations. Rachis magnesium has also been added to the various leaf analysis forms, reports and charts.

Data analysis form showing pest and disease data by year and division.

Data analysis form showing pest and disease data by year and division.

In practice, data maintenance and data validity checks are amongst the most important tasks for OMP operators to regularly carry out, to help avoid incorrect management decisions being made due to wrong data recorded in OMP. To make this task easier, we have greatly expanded and improved the capabilities of the OMP Data Verification tool. With the new tool, users will be able to easily check for unrealistic or missing values of in various production-related quantities including yield, average bunch weight or harvester productivity. Besides production quantities the data verification tool also covers fertilizer application as well as various other block characteristics. Based on the criteria entered by the user, the data verification tool outputs an overview report showing which percentage of blocks fail which criteria as well as a detailed list of all blocks where data needs to be reviewed.

Data verification settings form with user-defined verification criteria.

Data verification settings form with user-defined verification criteria.

New weekly and fortnightly budget vs actual comparisons

The capabilities of the OMP Harvest Round Recording (HRR) Add-In, which is used for detailed production reporting, have been extended with the addition of a new class of reports showing a comparison of actual and budgeted production. The new reports are available at weekly, fortnightly, 4-weekly (periodical) or monthly aggregation level, and allow managers to get a detailed view of how well they are on the way to meeting their production targets.

Weekly budget vs. actual report in OMP-HRR.

Weekly budget vs. actual report in OMP-HRR.

The new version 8.8.01 will also see major improvements to the OMP-SIS Add-In, which focuses on the management of oil palm smallholder areas. The entire yield and production reporting section of the add-in has been re-coded to achieve a significant speed increase compared to the previous version of the program. Furthermore, budget vs actual reports similar to those now built into the OMP-HRR Add-In have also been added to SIS on a fortnightly and monthly level. These reports will give plantation managers a quick overview of whether the smallholder areas are producing as expected, and whether the yield reported on a fortnightly basis by each smallholder is realistic or whether further investigation is needed.

Smallholder fruit pickup schedule

Another new tool which we think will prove very helpful in the management of oil palm smallholder production is the new fruit pickup schedule module built into OMP-SIS. The revamped data entry system makes it easy to generate a pickup schedule based on an initial harvesting date and a planned harvest rotation length for each block in which public holidays and Sundays can be automatically excluded. It is possible at any time to reset the schedule for the coming months, for example if the pickup process has already fallen behind the schedule made at the beginning of the year and the manager wishes to restart the counting based on what the current harvesting dates are. The new features are complemented by a selection of data analysis forms and reports that allow managers to see how well the pickup schedule is being implemented. The screenshot below shows an example of such a report which displays information on which percentage of blocks is being picked up how many days late.

Fruit pickup schedule variance report in OMP-SIS.

Estate statistics on variance between actual and scheduled pick-up days by fortnight.

The possibility of filtering globally is one of the most powerful data analysis features OMP. This feature has now been added and adapted to the case of the OMP-SIS Add-In. In addition to some of the usual filter fields familiar from the OMP-DBMS main program such as Division, Field, Year, Soil class etc., the OMP-SIS filter allows users to filter for specific farmer ID’s or smallholder groups. These filtering features will greatly help in particular with data analysis of farmer questionnaires of smallholder block data. Finally, the data analysis forms and reports showing the area distribution of smallholder areas by tree age or planting year have been improved.

Rounded budgets and data importing for crop budget and forecast

A new feature in the OMP Crop Budget Add-In is the option to work with rounded monthly production values in the budget. This option is intended for users who prefer to work with budgets of a round number of tons per month rather than directly using the result of multiplying the budgeted yield with monthly percentage and block size. This method is advantageous if users wish to compare or cross-correlate the OMP results with other budget calculations carried out with round monthly production budgets e.g. in MS Excel.

OMP-AMIS version 8.8.01 will make it possible to import data from Excel format in the OMP Crop Budget and OMP Crop Forecast (Black Bunch Count) Add-In programs, where no importing was possible so far. This is particularly useful in the case of the black bunch count add-in, where census data from a large number of blocks has to be recorded every month or every few months at the latest. Finally, as usual the new release will include a number of smaller bug fixes and tweaks to improve the user experience.