DEXi is a computer program for creating multi-attribute decision models (MADMs) that: MADMs, like DEXi, are well suited to use in assessing crop production systems that are reliant on complex networks of interactions and trade-offs between biotic, abiotic and economic components.

DEXi-CSC has been developed to assess systems such as the CSC's integrated cropping system directly against standard commercial practice over a six year croping sequence.

The effect of cropping treatment was assessed according to the responses of a suite of indicators which were used to parameterise a qualitative multi-attribute model. Scenarios were run to test whether the integrated cropping system achieved greater levels of overall sustainability relative to standard commercial practice.

This case study demonstrates the value of a whole-systems approach using qualitative multi-attribute modelling for the assessment of existing cropping systems and for predicting the likely impact of new management interventions on arable sustainability.
Top level DEXi-CSC tree

DEXi-CSC is comprised of 97 input variables representing the basic biotic, abiotic and financial indicators of the arable cropping system (see DEXi-CSC input attributes). Input variables are aggregated into a hierarchical tree with a total of 332 aggregate variables. Overall sustainability, the top level, is the aggregate of two, second level branches Environmental sustainability and Economic sustainability. Environmental sustainability is the aggregate of three, third level branches (Biodiversity, Losses and Resource use). Economic sustainability is the aggregate of two, third level branches (Viability and Real profitability).

These branches reflect the main goals of the CSC platform:

DEXi-CSC input attributes

1. Average market price 50. Price of crop seed
2. Average price of fuel 51. Price of desiccants
3. Baling fuel 52. Price of fungicide
4. Carting fuel 53. Price of FYM
5. Connectivity of semi-natural habitats 54. Price of herbicide
6. Crop competition 55. Price of insecticide
7. Compost fuel 56. Price of irrigation
8. Conditions for spray drift 57. Price of lime
9. Conditions for volatilisation 58. Price of mineral K fertiliser
10. Cover crop fuel 59. Price of mineral N fertiliser
11. Crop cover 60. Price of mineral P fertiliser
12. Crop fuel 61. Price of molluscicide
13. Crop rotation diversity/intensity 62. Price of seed treatment
14. Crop type 63. Price of soil sterilants
15. Deep inversion tillage fuel 64. Price of straw
16. Desiccants AI/rate 65. Price of trace elements
17. Desiccant fuel 66. Price of undersow seed
18. Environmental subsidies 67. Product quality
19. Field margin floral diversity 68. Production risk
20. Field margin structure 69. Proportion of gross margin due to main crop
21. Field properties 70. Rate compost
22. Fungicide AI/rate 71. Rate FYM
23. Fungicide fuel 72. Rate lime
24. FYM fuel 73. Rate mineral K fertiliser
25. Harvesting fuel 74. Rate mineral N fertiliser
26. Herbicide AI/rate 75. Rate mineral P fertiliser
27. Herbicide fuel 76. Rate straw
28. Insecticide AI/rate 77. Rate trace elements
29. Insecticide fuel 78. Rate undersow
30. Inversion tillage fuel 79. Ratio semi-natural: cultivated land
31. Irrigation availability 80. Requirement for agricultural equipment
32. Irrigation fuel 81. Seed cover crop
33. Irrigation requirement 82. Seed rate crop
34. Labour hourly wage 83. Seed treatment AI/rate
35. Lime fuel 84. Seed undersow
36. Mineral fertilisers fuel 85. Soil cover at pesticide application
37. Molluscicide AI/rate 86. Soil sterilants AI/rate
38. Molluscicide fuel 87. Soil sterilant fuel
39. N soil residual 88. Soil type
40. Non-environmental subsidies 89. Straw chopping fuel
41. Non-inversion tillage fuel 90. Straw selling price
42. Number of hours 91. Straw yield
43. P soil residual 92. Stubble/straw management
44. Pathogen pressure 93. Tillage intensity
45. Pest pressure 94. Trace elements fuel
46. Post-plant herbicide AI/rate 95. Undersow fuel
47. Precipitation 96. Weather conditions
48. Price of compost 97. Weed management strategy
49. Price of cover crop seed

The scales used for each input and aggregate function are available here.

An interactive Java implemntation of the DEXi-CSC model can be accessed here.

Interpreting DEXi Evaluations

Viewing and interpreting several evaluations of a DEXi tree with over 300 aggregate functions can be challenging. To aid interpretation we use two types of visual representation of the output i.e. heat maps and radar plots.

DEXi-CSC heat map

These heat maps show the top seven layers of evaluation output for each of the six crops in the CSC the rotation. Input data is aggregated across six years of the first crop rotation. As a contrast conventional potato performs poorly whilst Integrated bean performs well.

Spring barley
Winter barley
Winter oilseed rape
Winter wheat
Conventional DeXi heat maps

DEXi-CSC radar plots

These radar plots show selected attributes from layers three and four of evaluation output for each of the six crops in the CSC the rotation. Input data is aggregated across six years of the first crop rotation.

Bean Potato Spring barley
Radar plot of DEXi Output for beans Radar plot of DEXi Output for potato Radar plot of DEXi Output for spring barley
Winter barley Winter oilseed rape Winter wheat
Radar plot of DEXi Output for winter barley Radar plot of DEXi Output for winter oilseed rape Radar plot of DeXi Output for winter wheat
Integrated Overlap Conventional

DEXi web site