Agricultural Production Analysis 1 
Last Updated: August 10, 2018; First Released: January 07, 2014
Author: Kevin Boyle, President, DevTreks
Version: DevTreks 2.1.4
A. Introduction
This reference explains how to start to collect, measure, and analyze agricultural production data (1*). DevTreks believes that all agricultural production data, from the net returns for a soybean crop to the cost of grazing cattle, has a story to tell and lessons to teach. Those lessons can only be learned when data about production is collected, measured, aggregated, analyzed, explained, and saved in online knowledge banks. Full, uniform, and accurate analyses of the returns from corn, alfalfa, lettuce, oranges, kiwis, roses, poultry, cattle, and striped bass, should be one or two links away for everyone. If a farmer, rancher, apple grower, organic poultry grower, puffed fish producer (2*), lender, government official, or nonprofit member, needs to make a decision involving agricultural production, they should have ready access to the best data and advice available.  This reference introduces another DevTreks way to build these types of knowledge banks.
Section
Page
Work Breakdown Structure
2
Net Present Value Calculation and Analysis
2
Inputs
2
Outputs
9
Operations
12
Outcomes
20
Operating Budgets
24
Agricultural Production Investment Analysis
33
Randomized Control Trial Data
35
National Data
36
International Agricultural Production Data
39
Agricultural Supply Chain Externality Analysis and SDGs
41
Multimedia and Stories
42
Knowledge Banks and Summary and Conclusion
46
B. Work Breakdown Structure (WBS)
An agricultural Work Breakdown Structure (WBS) was not available for classifying this data when the data was first entered (2004). Although the data has been logically structured, we recommend using a formal WBS to structure the data, rather than the “adhoc” type of WBS that was used with this data. The WBS will allow this club’s data to be combined with the data collected by other clubs in this network. DevTreks provides decision support by using WBSs to aggregate and analyze the data point estimates collected by clubs throughout a network.
C. Net Present Value (NPV) Calculation and Analysis
DevTreks’ NPV and Machinery Calculators and Analyzers were used to carry out most of the calculations and analyses documented in this reference (7*). These tools are documented in the Net Present Value 1, Benefit Cost Analysis 1, and Capital Input Analysis 1 tutorials. All of the calculators and analyzers available in DevTreks for analyzing agricultural production data can be located using the URIs found in the references used in each DevTreks tutorial (8*). Those analyzers include Life Cycle Assessment (LCA), Monitoring and Evaluation (M&E), and Resource Stock tools. Each of the base element sections that follow demonstrates representative calculations and analyses –use the referenced tutorials for a more thorough explanation of the calculations and analyses.
D. Inputs
https://www.devtreks.org/agtreks/select/crops/servicebase/Inputs, Iowa Corn and Soybeans, plot experiments/1829/none/ 
http://localhost:5000/agtreks/select/crops/outputgroup/Nashua, Research Plots, Corn and Soybeans/1936433230/none
http://localhost:5000/agtreks/select/crops/inputgroup/Farm Machinery, NASS/2126771336/none
1. Data Structure
The following image displays a typical Input Group with Inputs. Most farm machinery data was obtained by interviewing the farm managers. Time series machinery data was not needed in this analysis. DevTreks recommends that most base elements should include the year of the data in the name. Note that machinery input prices should include OC (Operating Cost), AOH (Allocated Overhead), and CAP (Capital) prices. Those prices should derive from a machinery calculator.

The following image displays a typical Input with children Input Series. The expendable input data was obtained from the experiment’s records. In general, NASS time series price data was used in the series. Note that the year has been included in the name. Although, in most instances, expendable inputs only need an OC Price, this example demonstrates that AOH and CAP Prices can be included if the input might be used as an Allocated Overhead or Capital, cost. As mentioned in several DevTreks’ tutorials, the role of networks is to define the data standards that clubs in the network should follow.

2. Input Calculations
An agricultural machinery calculator was run for all agricultural machinery. Calculations were run at the Input level and then automatically inserted into children Input Series by setting the Use In Descendants = True property. Setting the Overwrite Descendants = False property means that children Input Series agricultural calculator properties are not overwritten. The latter property can be set to True if the children do not have unique calculator properties (note that the base inputseries.CAPPrice is not overwritten by this particular calculator). Typical examples of unique calculator properties include fuel prices, interest rates, labor types, annual hours of use, and speed.

3. Input Analyses
The following image of a machinery analysis displays an Input Machinery Analysis. Many of the Input data services have scratch pad data and are not structured very well. Readers are reminded that IT perfection requires more resources than most small ngos have. 


E. Outputs
https://www.devtreks.org/agtreks/select/crops/outputgroup/Nashua, Research Plots, Corn and Soybeans/1936433230/none/
http://localhost:5000/agtreks/select/crops/outputgroup/Nashua, Research Plots, Corn and Soybeans/1936433230/none
1. Data Structure
The following image shows that crop Output series data was structured based on plot number. Yield data was obtained from farm records, but most price data was obtained from NASS. 


2. Output Calculations
No further Output calculations were completed for this dataset.
3. Output Analyses
The following image demonstrates a typical (Change by Year) Output analysis. Note the number of observations for 1990 and 1991. The actual experiment station data has one plot per year, not two. The two extra budgets result from the testing carried out with the data.


F. Operations
https://www.devtreks.org/agtreks/select/crops/servicebase/Operations, Iowa Corn and Soybeans, plot experiments/1820/none/
http://localhost:5000/agtreks/select/crops/servicebase/Operations, Iowa Corn and Soybeans, plot experiments/1820/none
1. Data Structure
The following image displays a typical Operation Group with children Operations. The crop operations are structured as time series data. Note that the year has been included in the name. Note the “needs description” descriptions. That’s one of the reasons why this data is development, rather than production, server grade.

2. Operation Calculations
All crop operations were linked to a NPV calculator. Good practice is to run the calculators and analyzers at the Operation Group level and insert them automatically into the children Operations. Since most children calculator and analyzer properties are identical among the Operation Group and the children Operations, the Overwrite Descendants = True property can be set. The Related C Type property identifies the specific calculator to insert or update in the children Operations. With these properties, individual calculations do not need to be run for each children Operation (but their “Desktop” and “Mobile” html views must be generated).
The following image displays a NPV calculation run at the Operation Group level for all children Operations. Each Operation’s calculation is a summation of all of the NPV calculations for the inputs in the operation. 

3. Operation Analyses
The following image displays a Machinery Analysis run at the Operation Group level for all children Operations. Each Operation’s analysis is a summation of all of the machinery inputs in the operation. Each Operation Group’s analysis is a summation of the operations’ machinery analyses. 

The following image displays the results of running the NPV Totals Analyzer. The NPV calculations in 25 crop operations were aggregated in the top Operation. 

The following image displays the results of running the NPV Statistics Analyzer. Labels were used as the aggregator. The image shows that the Operation Group found 10 Operations and carried out a statistical analysis of those 10 aggregated operations. 

The following image displays the results of running the NPV Change by Id Analyzer. No aggregators were used in this analysis, so each Observation equals 1. This analysis is a quick way to examine differences among the 30 Operations. This type of analysis is handy for discovering data entry errors.

G. Outcomes
https://www.devtreks.org/agtreks/preview/cropsconservation/outcomegroup/Commodity Plot Data/30/none/
http://localhost:5000/agtreks/select/cropsconservation/servicebase/Outcomes, Iowa Corn and Soybeans plot data/2617/none
1. Data Structure
When this dataset was first entered in 2004, DevTreks did not have an “Outcomes” service. The need for that service wasn’t proven until we started building health care industry datasets. Although it wasn’t hard to add Outcomes to this dataset, the data wasn’t structured correctly. They should have been structured similar to the Operations data. Instead of one Outcome Group containing 247 Outcomes, several Outcome Groups, based on date and plot, should have been used. 

2. Outcome Calculations
All crop Outcomes were linked to a NPV calculator. Good practice is to run the calculators and analyzers at the Outcome Group level and insert them automatically into the children Outcomes. Each child must then be opened and the Mobile and Desktop html view generated (we’re not going to open 247 Outcomes right now, but that’s why you should work in clubs that can share the data management requirements).

3. Outcome Analyses
The following image displays the results of running the NPV Totals Analyzer. The NPV calculations in 247 crop outcomes were aggregated in the top Outcome. The first Outcome, “none”, should have been deleted or completed. This dataset mixes up Outcomes from several sources –Iowa, national, Alaska, El Salvador. The lesson is to work with your club members to clean up the data before saving the analyses. Each “group” base element (i.e. Budget Group, Outcome Group, Input Group) has a property, Data Status, that allows clubs to notify data users of the current status of the data in the group –approved, under review, under revision, or not reviewed.

H. Operating Budgets
https://www.devtreks.org/agtreks/select/crops/servicebase/Profits, Iowa Corn and Soybeans, plot experiments/1819/none/ 
http://localhost:5000/agtreks/select/crops/servicebase/Profits, Iowa Corn Soybeans, plot experiments/1819/none
1. Data Structure
The amount of data that can be analyzed and displayed in any data structure has limits (4*). This club found that the time needed to run calculations and display results was reasonable with less than 50 full crop budgets (Time Periods) in a Budget Group. Other clubs should experiment until they find a size that meets their requirements.
Each Budget Group holds three Budgets (three plots):

Most Budgets hold 14 Time Periods (time series crop budgets). As the statistics that follow show, Plot 01 has 16 Time Periods, two of which are strictly software testing additions:

Each Time Period holds Outcomes (which hold Outputs) and Operations (which hold Inputs):

2. Operating Budget Calculations
The following image displays a NPV calculation run at the Budget level for all children Time Periods. The Budget Revenues and Costs are a summation of the 16 Time Periods.

3. Operating Budget Analyses
The following image displays the results of running the Machinery Analyzer. The budget is a summation of all the 16 Time Periods’ machinery Inputs. Each Time Period is a summation of all of its crop operations.

The following image displays the results of running the NPV Totals Analyzer. This view of the totals offers a more compact display than the base NPV calculations.

The following image displays the results of running the NPV Change by Year Analyzer. Running this analysis on localhost shows that a lot of the crop operations do not line up neatly (as contrasted to the Benefit Cost Analysis 1 reference data). The NPV Analyzers have fairly stringent data classification requirements. Dates, WBS labels, Alternative Type, and Target Type, properties must all be set correctly (i.e. understood) before entering data into DevTreks. We expect professional audiences to appreciate the stringency. We recommend experimenting with smaller datasets on development servers first. Once the data requirements are understood, move to production.

I. Agricultural Production Investment Analysis
The NPV Calculation 2, Capital Budgets, tutorial includes an example of an investment analysis of alternative irrigation systems. The Life Cycle Analysis 1 and 2 references demonstrate general investment analysis. Further examples of the data structure, calculations, and analyses for Components and Capital Budgets can be found in the Construction Analysis 1 reference. 
J. Randomized Control Trial (RCT) Data
https://www.devtreks.org/agtreks/select/cropsconservation/devpack/Iowa, ARS-NRCS 2, Treatments 1 through 35, Full Set/80/none/
http://localhost:5000/agtreks/select/cropsconservation/devpack/Iowa, ARS-NRCS 2, Treatments 1 through 35, Full Set/80/none
Additional techniques for analyzing RCT data can be found in Examples 7 and 8 in the Social Performance Analysis 3 reference.
1. Data Structure
The following images demonstrate one way to structure agricultural production data using a randomized control trial (RCT) design:


2. Calculations and Analyses
This dataset, and further information about calculating and analyzing RCT data, can be found in the DevPack Analysis tutorial (3*). 
K. National Data
https://www.devtreks.org/agtreks/select/crops/servicebase/Profits, Texas AM and NRCS national crop rotations/1076/none/
http://localhost:5000/agtreks/select/crops/servicebase/Profits, Texas AM and NRCS national crop rotations/1076/none
1. Data Structure
The following images demonstrate one way to structure national agricultural production data. Agricultural economists working for the USDA collected this data in the 1990s by surveying state cooperative extension economists. 


2. Calculations and Analyses
NPV calculations have not been run for the 1,700+ crop rotations in this dataset. This dataset may be calculated and analyzed in a future release that deals with analyzing national-level data (4*, 5*). 
L. International Agricultural Production
https://www.devtreks.org/agtreks/select/agricultura/resourcegroup/Irrigation, Ahuachapan, El Salvador/9/none/
https://www.devtreks.org/agtreks/select/agricultura/servicebase/Sistemas de Produccion/1934/none/
http://localhost:5000/agtreks/select/agricultura/servicebase/Sistemas de Produccion/1934/none
1. Data Structure
The following images demonstrate that international agricultural production datasets can be structured similar to other datasets:

2. Calculations and Analyses
The Social Performance Analysis tutorial introduces several new algorithms that employ international agricultural examples. DevTreks can be deployed in any Azure region in the world besides regular deployment on any IIS web server or the internal, cross platform, Kestrel web server. DevTreks support for agricultural production in developing countries is one good information technology team away from reality (6*) [or a visit from the author].
M. Agricultural Supply Chain Externality Analysis and the Sustainable Development Goals (SDGs) (9*)
The Performance Analysis and Social Performance Analysis (SPA) tutorials demonstrate how to use the Resource Stock and Monitoring and Evaluation tools to analyze the externalities associated with agricultural supply chains. 
The following dataset demonstrates the technique employed for this purpose in the 1990s.
https://www.devtreks.org/agtreks/select/crops/outputgroup/Nashua, Research Plots, Nitrogen in Tile Lines/1936433242/none
The following URLs demonstrate the Life Cycle Assessment techniques introduced in 2018 and demonstrated in Example 3 of the SPA2 reference. The new recommendations employ Indicators as the metadata summary for their background TEXT datasets which have been analyzed using custom algorithms tied into mathematical and statistical libraries. IT evolves.
Resource Stock Assessment
https://www.devtreks.org/greentreks/preview/carbon/input/Coffee Firm RCA3 Stock/2147397559/none
http://localhost:5000/greentreks/preview/carbon/output/Coffee Firm RCA3 Stock/2141223486/none
Monitoring and Evaluation Assessment
https://www.devtreks.org/greentreks/preview/carbon/input/Coffee Firm RCA3 M and E/2147397561/none
http://localhost:5000/greentreks/preview/carbon/output/Coffee Firm RCA3 M and E/2141223488/none
The tutorials include examples demonstrating how to use Performance Monitoring and Impact Evaluation to account for SDG goal accomplishment.
N. Multimedia (Resources)
https://www.devtreks.org/agtreks/preview/crops/resourcegroup/Conservation Tillage, Case Studies/4/none/
http://localhost:5000/agtreks/preview/crops/resourcegroup/Dictionary, Agricultural economics/5/none
Multimedia is stored in Resources Services. Multimedia is linked to “stories” (Linked View Services) which are then linked to economic content. The following images display multimedia that is used to support a basic agricultural development story.

O. Stories (Linked Views)
https://www.devtreks.org/agtreks/preview/crops/linkedviewgroup/Tillage Case Studies/15/none/ 
http://localhost:5000/agtreks/preview/crops/linkedviewpack/Randomized complete crop block, tillage treatment analysis, 1990-1992 crops/32/none
Stories are built using Linked View Services. The following image shows that four pages (i.e. 4 linked views) have been built to support the dataset. The four pages summarize one statistical analysis carried out using this dataset. The Boyle et al reference introduces a more comprehensive statistical analysis completed using this dataset (3*). 


P. Knowledge Bank Standards
All agricultural production data should be entered into online knowledge banks (i.e. production servers as contrasted to development servers) that can be used to analyze agriculture. That structured evidence must be passed down to future generations. These knowledge banks aggregate and analyze all of the data in a network. Future references will discuss how these knowledge banks will evolve (i.e. semantic data, forecasts) to support future decision making needs. The flexibility offered by DevTreks in documenting agricultural production means that networks need to develop “rules” explaining the “standards” that should be followed by clubs in their network. The “standards” make it possible to build knowledge banks. 
Summary and Conclusions
Clubs using DevTreks can start to carry out the basic analysis of agricultural production. Clubs can solicit help understanding production better and share structured evidence explaining agricultural production. Networks can build knowledge banks that explain agricultural production and pass that knowledge down to future generations. The result may be corn farmers that get higher returns, cattle ranchers that learn how to weather price downturns, organic growers who learn from successful mentors, subsistence farmers who make the successful transition to production farmers (10*), farmworkers who learn how to achieve higher salaries, governments that understand sustainable food security, urban policy makers that understand rural economies, and people who improve their lives and livelihoods.
Footnotes
1. Analysts have developed a large number of techniques for analyzing agricultural production. This reference introduces basic agricultural production analysis. The Social Performance Analysis tutorial introduces more advanced techniques for analyzing agricultural production and improving sustainable agricultural development.
2. The author helped to finance a puffed fish/striped bass aquaculture enterprise in eastern Long Island, New York, USA, around 1981 (along with enterprises that grew potatoes, cauliflower, rye, broccoli, strawberries, organic vegetables, apples, nursery stock, flowers, ducks …). And that was before working and studying in California. In 2017 and 2018 he released new automated tools, documented in the Social Performance references, for improving the accountability of sustainable agricultural production throughout the world.
3. The DevPacks application allows content to be structured in arbitrary hierarchies, such as randomized control trial content. It was built specifically to complement the more rigid hierarchies used to store base element content. A reasonably functioning online version was first available in 2004 (when the author worked for the USDA). Examples of Impact Evaluation that use formal RCT data can be found in Examples 7 and 8 of the SPA3 reference.
4. The main limitation in analyzing any dataset is the amount of html that must be generated and reasonably displayed. Another issue is the amount of cloud computing server resources that a small ngo can afford to pay. Since these are technical issues, they are amenable to technical solutions, such as the Indicator metadata-TEXT dataset-custom algorithm-mathematical library techniques introduced in 2017 for agricultural sustainability analysis,
5. Many economists believe that an important role of their profession is to analyze, at least to some degree, supply and demand. National datasets of agricultural production are sometimes used for that purpose. These types of analysis may appear in future releases (also read Footnote 4).
6. The author has a M.S. degree in International Agricultural Development and remains committed to assisting agriculture in developing countries. The author thinks that the science supported by DevTreks transcends international boundaries and that locals know the best way to adapt the science to local cultures. As usual, we encourage source code users to produce their own tutorials, datasets, and algorithms that target the needs of their own stakeholders.
7. DevTreks has not opened each and every URI in each dataset and run all calculations and analyses. Our primary role is software, rather than content, development. We ran just enough calculations and analyses to test the software and produce this reference. In other words, do what we say, not what we do. 
8. We included the localhost URIs in the Version 2.1.4 release because many rural areas and developing countries do not have good Internet access. On a side note, why do URIs terminate in “/none/”? Because that feature was built to support more advanced pagination than currently exists in DevTreks. IT evolves.
9. DevTreks endorses the development of a consumer-focused product and service purchasing guide that informed stakeholder can use to ensure that their money is only going towards companies and communities who share their values. Although Version 2.1.6 began investigating the development of lighter weight versions of DevTreks for this purpose (i.e. document databases, blockchain supply chain analysis, curated shopping lists), consumer-oriented software for conducting consequential digital activism may be more appropriate for better funded and networked organizations (i.e. but then again).
10. At least one, and possibly both, of the author’s grandparent families could be described as close to subsistence –growing their own potatoes and pasture, raising their own cattle, sheep, dairy, and poultry at acreages and herd sizes that might be described as subsistence. It’s not clear to the author whether either family would have been happier engaged in modern production farming (although their children may not have had to emigrate). Agricultural production is not the only dimension that needs to be analyzed in efforts to improve the lives and livelihoods of rural families.
References
Hallam, Eidman, Morehart and Klonsky (editors). Commodity Cost and Returns Estimation Handbook, Staff General Research Papers, Iowa State University, Department of Economics, 1999
K. P. Boyle, P. Heilman, R. W. Malone, L. Ma, and R. S. Kanwar. Using Social Budgeting Web Software and Natural Resources Software Models to Improve Agricultural Economics Data Collection, Dissemination, and Analysis. DevTreks Working Paper 01. May 21, 2011 (can be found as a pdf file in the DevPacks Analysis tutorial)
US Government Accountability Office. Applied Research and Methods. GAO Cost Estimating and Assessment Guide. Best Practices for Developing and Managing Capital Program Costs. March, 2009.
References Note
We try to use references that are open access or that do not charge fees.
Improvements, Errors, and New Features
Please notify DevTreks (devtrekkers@gmail.com) if you find errors or can recommend improvements.
Video tutorials explaining this reference can be found at:
https://www.devtreks.org/commontreks/preview/commons/resourcepack/Ag Production Analysis 1/464/none/


DevTreks –social budgeting that improves lives and livelihoods




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