4. Production planning and allocation model
4.3. Numerical results
Appendix D shows historical variability of wheat yields by selected Ukrainian regions. Similar histograms for all regions, districts, and major crops were used in the model to characterize weather related eventualities. The distribution of actual production may not be optimal and robust (Appendix B) against e.g. weather uncertainties. Actual production allocation does not always coincide with the best conditions for crops productivity. I.e., if variability of yields is due to climatic conditions, then in many regions it may be more rational from food security considerations to produce other crops.
The model derives optimal cost minimizing crop composition by locations and shows those crops that are robust against uncertainties which we compare to actual in Figure 6. The model can estimate additional financial support necessary to ensure transition of farmers from current primarily cash crops production to the optimal combination of crops derived by the model. The results fulfill food security goals and natural resource constraints.
The model allows analyzing alternative criteria to minimize yield gaps (between the actual and the potential production) at minimal costs, as shown in Appendixes E, F. To simplify visualization, the results are aggregated to regional level (25 regions). In Figure 6 we compare the current and the derived crops production allocation. Appendixes E, F summarize the result in the form of tables.
Current distribution Model decision Legend
Wheat
Corn
Sunflower seed
Rape seed
Figure 6. Comparison of current and model designed distributions.
As Figure 6 shows, the model proposes increasing rye and corn production.
Production of other crops, especially, sunflowers and rape, may be reduced and allocated differently.
5. Conclusions and discussions
These studies analyze agricultural development trends in Ukraine in the last two decades. Major attention is given to characterize implications of agriculture production intensification on food and environmental security and rural livelihood. Agribusiness activities in Ukraine have the following main characteristics:
1. Short-term planning goals: maximization of immediate profits, cash crops export;
low quality agripractices, etc.;
2. Monopolism and lobbying caused by fast growth of large agriholdings ; 3. Rapid deterioration of soils, environmental degradation;
4. Absence of social responsibility;
5. Non-market (shadow) system of crediting and financial support;
6. Unfair access to resources, markets, etc.
It is becoming increasingly difficult to regulate agricultural intensification because of the absence of proper institutional regulations by government. Among the important regulatory constraints the following should be selected: firstly, satisfaction of local demand; introducing the institute of social responsibility; increasing the level of rural employment; enforcement of the control for the use of natural resources (especially important in context of new land law); state interventions review. The studies use the model being developed jointly by IIASA and Institute of Economic and Forecasting, National Academy of Ukraine, for planning agricultural production in accordance with sustainable goals. With the new model introduction (especially at the district level) the farmer’s activity is expected to be oriented on satisfying the country demand at local level and as result will provide:
- Sustainable rural development
- Food security with minimal pressure on ecology
- Avoidance of significant production costs (fertilizers, lease rates, transport, etc.)
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Appendix A. Fertilizer application in Ukraine, 2009, kg/t crop yield.
Wheat Corn Sunflower seed Rape seed N P2O5 K2O N P2O5 K2O N P2O5 K2O N P2O5 K2O Optimal 32 14.7 36 26 11.6 30 90 25.2 20.4 90 25.2 20.4 Average 15.0 2.7 2.4 11.6 2.3 2.2 12.7 4.3 3.4 36.6 8.4 7.7 Crime 14.7 5.3 0.6 7.0 0.6 0.4 9.0 3.8 0.0 45.9 15.2 2.1 Vinnitsa 15.0 2.4 2.3 10.9 1.1 1.0 15.5 3.9 4.3 33.6 4.8 5.6 Volhynia 19.5 3.4 3.7 13.0 2.6 5.2 3.3 0.0 0.0 52.1 10.1 11.5 Dnepropetrovsk 14.4 2.1 1.0 8.9 2.1 1.3 8.3 3.8 2.1 37.0 7.8 2.9 Donetsk 13.0 3.0 0.9 5.4 1.5 0.8 6.2 3.9 1.7 32.0 10.1 1.7 Zhitomir 14.5 2.9 3.3 15.8 2.7 3.1 10.5 5.7 3.8 33.6 8.0 11.1 Transcarpathia 17.1 6.2 4.3 14.7 3.3 3.3 17.0 9.7 11.4 41.4 11.5 15.2 Zaporozhe 12.9 1.3 0.7 10.7 2.9 1.7 5.6 3.3 1.6 38.7 5.2 3.2 Ivano-Frankovsk 17.8 4.3 4.5 12.8 4.5 4.0 12.1 8.7 9.4 33.5 7.0 16.0 Kiev 12.2 2.1 2.2 10.3 1.8 1.8 11.0 3.0 3.3 37.4 5.3 6.1 Kirovograd 12.4 1.8 1.4 7.5 1.8 1.6 5.8 2.6 1.9 40.3 7.3 6.0 Lugansk 19.8 1.3 0.8 19.1 1.6 1.4 11.7 2.2 1.0 37.8 11.2 3.1 Lvov 22.4 5.0 6.5 12.1 3.1 3.2 0.0 0.0 0.0 31.1 8.8 10.6 Nikolayev 12.3 1.6 0.9 4.8 1.9 0.9 5.8 2.8 1.7 37.9 8.2 5.0 Odessa 13.6 1.8 1.2 9.2 3.1 1.0 8.9 3.5 2.2 33.8 6.3 2.9 Poltava 12.5 2.5 1.4 10.7 1.0 0.8 8.9 3.2 2.5 31.3 9.8 4.1 Rovno 19.4 2.1 3.9 16.8 1.8 4.0 16.4 5.5 4.6 36.9 6.7 13.4 Sumy 15.4 3.6 3.8 11.1 2.6 4.1 9.3 5.4 4.5 38.8 8.4 8.6 Ternopol 12.5 3.0 3.1 9.8 1.9 2.6 14.6 2.9 3.9 35.8 11.2 12.2 Kharkov 15.1 2.5 2.3 13.7 5.5 5.1 8.1 4.1 3.7 41.8 17.9 18.6 Kherson 15.1 1.1 0.4 11.4 2.5 1.6 17.9 9.3 3.2 27.5 3.0 1.1 Khmelnitsky 15.1 2.9 2.7 13.8 3.5 3.0 18.7 7.2 7.6 39.1 12.4 11.4 Cherkassy 13.5 1.9 1.7 8.0 1.6 1.7 9.9 5.1 5.1 46.1 7.0 6.9 Chernovtsy 10.3 1.9 3.2 17.4 1.3 1.1 17.1 2.8 2.8 18.7 3.5 4.2 Chernigov 15.8 2.8 2.6 14.5 1.6 1.8 16.1 7.9 8.4 32.0 10.0 11.1 Source: State committee of statistic of Ukraine: introduction of mineral and organic fertilizers by agrarian enterprises under the crops 2004-2009; FAO (1995).
Appendix B. Comparison of actual and critical (revenue=costs) crop yield in regions according to share of sowed area, 2009.
Crop Legend Wheat
Corn
Sunflower seed
Rape seed
Indexes: sowed area
Appendix C. Actual vs critical yields: main crops (lease rates higher for 2 times)
Wheat Corn SF Rape seed
Actual Critical Actual Critical Actual Critical Actual Critical Crime 2.3 2.8 9.0 2.6 0.9 1.4 1.1 1.3 Vinnitsa 4.2 4.8 6.1 7.2 2.0 1.8 2.0 1.9 Volhynia 2.9 2.7 6.8 8.3 0.9 2.0 2.3 2.0 Dnepropetrovsk 3.2 3.8 3.6 4.1 1.6 1.4 1.5 1.5 Donetsk 2.9 3.7 2.9 5.5 1.7 1.6 2.3 1.9 Zhitomir 3.2 3.4 6.1 7.3 1.6 1.6 2.0 1.8 Transcarpathia 2.9 3.0 4.0 6.7 1.0 0.6 1.4 0.3 Zaporozhe 3.0 3.5 2.6 4.3 1.5 1.4 1.7 1.7 Ivano-Frankovsk 3.1 3.0 5.4 7.1 1.7 2.2 2.0 1.7 Kiev 4.0 4.4 6.5 7.8 2.5 2.1 2.2 2.0 Kirovograd 3.2 3.7 5.2 6.1 1.9 1.7 1.7 1.4 Lugansk 2.6 3.2 2.0 4.1 1.3 1.2 1.0 1.6 Lvov 3.3 3.3 7.0 6.5 1.1 12.2 3.3 1.9 Nikolayev 3.0 3.3 4.6 3.8 1.6 1.4 1.3 1.5 Odessa 2.8 3.2 2.5 4.0 1.1 1.2 1.5 1.3 Poltava 3.8 4.6 6.0 6.2 2.3 2.2 2.0 2.3 Rovno 3.4 3.7 4.8 11.7 1.5 2.6 2.5 2.4
Sumy 3.4 4.6 5.4 9.8 1.8 2.2 1.8 1.9
Ternopol 3.9 3.8 6.0 7.1 1.9 2.2 2.2 1.8 Kharkov 3.2 4.4 3.4 5.5 1.9 1.7 1.5 2.5 Kherson 2.5 2.9 6.0 7.0 0.5 0.6 1.2 1.2 Khmelnitsky 3.8 4.5 6.0 9.4 1.6 2.1 1.8 1.7 Cherkassy 4.8 5.5 7.5 6.3 2.4 2.2 2.1 2.1 Chernovtsy 3.7 2.9 4.8 9.7 1.6 1.7 2.6 2.5 Chernigov 3.4 3.8 5.4 6.5 1.8 1.5 2.0 2.1
- profitable production.
Appendix D. Variability of actual yields: example of wheat yields at regional level.
26
Appendix E. Distribution of land under crops according to the model (aggregated from the districts), ha.
Wheat Rye Corn Barley Sunflower seeds Soybeen Rape seeds
ha % ha % ha % ha % ha % ha % ha %
Crime 84.7 21.9 59.6 15.4 105.4 27.3 100.1 25.9 0 0.0 36.6 9.5 0 0.0 Vinnitsa 261.5 38.3 20.7 3.0 143.9 21.1 135.1 19.8 106.3 15.6 13.4 2.0 2.1 0.3 Volhynia 25.7 26.6 13.2 13.6 11.6 12.0 15.4 15.9 30.9 31.9 0 0.0 0 0.0 Dnepropetrovsk 310.2 32.4 19 2.0 127.2 13.3 185.7 19.4 242.2 25.3 47.9 5.0 26.2 2.7 Donetsk 181.1 37.1 19.5 4.0 64.1 13.1 27.9 5.7 48.7 10.0 141.9 29.0 5.5 1.1 Zhitomir 54.6 26.0 22.2 10.6 21.5 10.2 13.2 6.3 98.5 46.9 0 0.0 0 0.0 Transcarpathia 4.2 36.8 1.7 14.8 0 0.0 0 0.0 0 0.0 3.2 27.6 2.4 20.7 Zaporozhe 302 36.7 37.8 4.6 102.9 12.5 185 22.5 167.5 20.4 24.7 3.0 2.2 0.3 Ivano-Frankovsk 15.7 27.0 4.9 8.4 5.1 8.7 4.3 7.4 28.1 48.4 0 0.0 0 0.0 Kiev 272.6 40.8 20.3 3.0 120.5 18.0 97.3 14.6 155.7 23.3 0 0.0 1.7 0.3 Kirovograd 240.4 30.6 217.6 27.7 74.6 9.5 173 22.0 65.4 8.3 14.8 1.9 0.7 0.1 Lugansk 155.9 37.3 13 3.1 73.2 17.5 117.3 28.0 0 0.0 54 12.9 4.9 1.2 Lvov 55 42.9 12.7 9.9 0.9 0.1 25.91 20.2 32.1 25.0 0 0.0 2.4 1.9 Nikolayev 204.4 32.8 79.4 12.8 83.8 13.5 103.9 16.7 127.2 20.4 22.6 3.6 0.9 0.2 Odessa 277.7 33.7 28.9 3.5 164.8 20.0 188.4 22.8 128.6 15.6 36.7 4.5 0 0.0 Poltava 236.1 30.7 21.8 2.8 235.6 30.6 141.7 18.4 119.1 15.5 14.2 1.8 1.2 0.2 Rovno 43.4 29.1 7.4 5.0 0 0.0 47.6 31.9 31.1 20.8 0 0.0 19.8 13.3 Sumy 103.5 25.9 17.6 4.4 76.3 19.1 96.2 24.1 95 23.8 8.9 2.2 1.9 0.5 Ternopol 103.7 30.2 14.5 4.2 34.6 10.1 58.1 17.0 102.5 29.9 28.9 8.4 0.5 0.2 Kharkov 276 34.4 30.6 3.8 83.3 10.4 148.9 18.5 246.2 30.7 17.2 2.1 1.1 0.1 Kherson 200.3 33.7 19.7 3.3 98.3 16.5 112.2 18.9 147.2 24.8 14.9 2.5 1.7 0.3 Khmelnitsky 157 41.4 10.9 2.9 35.3 9.3 131.5 34.7 43.9 11.6 0 0.0 0.7 0.2 Cherkassy 147.5 23.9 9.1 1.5 235.5 38.2 64.5 10.5 131.5 21.3 27.3 4.4 0.6 0.1 Chernovtsy 0.4 0.5 4.3 5.5 18.4 23.6 25.8 33.0 27.7 35.4 0 0.0 1.7 2.2 Chernigov 120.9 28.7 10.9 2.6 44.3 10.5 106 25.2 125.3 29.8 12.1 2.9 1.4 0.3
Appendix F. Spatial distribution of robust crop portfolios suggested by the model.