1. AAMP Training Materials Module 1.4: Options for Raising Productivity Among Resource-Poor Farmers Steven Haggblade (MSU) blade@msu.edu

2. Outline Objectives Profile of cotton farm households in Zambia Sources of CF productivity gains Linear programming (LP) optimization methods LP Exercises & Discussion References

3. Asset-poor farm households How can a farmer with low assets increase productivity? This man has a farm, his own labor, and a hand hoe – the most basic technology.

4. Asset-poor farm households The average farm household has land, family labor, and several hand hoes Can their technology be improved to boost farm productivity?

5. Asset-poor farm households The families in the previous slides have little money, do not use herbicides, and are unlikely to have access to animal traction What the families do have is labor So, if families can increase labor productivity, they can increase farm productivity too The following discussion focuses on cotton farmers in Zambia, examining the productivity differences between available technology packages and feasible alternatives for households of different asset holdings.

6. Objectives Evaluate the impact of Conservation Farming (CF) on asset-poor households compared to conventional tillage Analyze the impact of different productive technologies –With and without oxen –With and without cash –With and without herbicides

7. Outline Objectives Profile of cotton farm households in Zambia Sources of CF productivity gains Linear programming (LP) optimization methods LP Exercises & Discussion References

8. Zambian Cotton Farmers Agro-Ecological Zone 12a2b3Total Total farm households number 73,313513,218105,543575,0711,267,145 percent 6%41%8%45%100% Cotton growing households number 5,008126,0960127131,230 percent 4%96%0% 100% Source: Supplemental Post-Harvest Survey of 2002/03.

9. Farm size distribution in AEZ 2a All farming households in AEZ2a CategoryFarm SizeHouseholdsArea/HH A11.5 ha or less46%0.8 A21.51 to 2.5 ha26%1.8 B2.51 to 5 ha21%2.8 C5 to 20ha7%6.6 Total100%1.9 Cotton farming households in AEZ2a CategoryFarm SizeHouseholdsArea/HH A11.5 ha or less29%1.1 A21.51 to 2.5 ha31%1.9 B2.51 to 5 ha30%3.0 C5 to 20ha10%6.5 Total 100%2.4 Source: Supplemental Post-Harvest Survey of 2002/03.

10. Asset holdings LandLaborNonfarm Y Farm SizehaFTECattle $/year Non-cotton farming households A1.1.5 ha or less0.91.62.6$209 Total2.11.93.5$259 Cotton farming households A1.1.5 ha or less1.11.80.7$35 Total2.72.00.7$84

11. Outline Objectives Profile of cotton farm households in Zambia Sources of CF productivity gains Linear programming (LP) optimization methods LP Exercises & Discussion References

12. Sources of CF Productivity Gains Minimum tillage Dry season land preparation Early planting Crop residue retention & water harvesting Precision layout and input application

13. Sources of CF Productivity Gains Minimum tillage requires 75% lower energy

14. Conventional Hand Hoe Using a hand hoe requires a lot of energy. Every centimeter of land must be turned manually.

15. Hand Hoe Conservation Farming In Conservation Farming, only about 15% of the surface area is disturbed in preparing planting basins. Moving less dirt requires less energy and labor.

16. Conventional Ox Plowing Conventional plowing inverts all of the soil in the field. For this, the soil must be relatively soft and moist. In clay soils, the trowel-like action of the plow builds up an impermeable plow-pans after years of repeated plowing.

17. Conservation Farming with Ripper The CF Ripper can be used before the rains come. It is a minimum tillage method that breaks the hardpan, leaves the rest of the topsoil unturned, and needs less energy.

18. Sources of CF Productivity Gains Minimum tillage requires 75% lower energy Dry season land prep  overcome peak season labour bottlenecks  increased area cultivable with fixed household labour

19. Dry Season Land Preparation Source: Haggblade and Tembo (2003).

20. Sources of CF Productivity Gains Minimum tillage requires 75% lower energy Dry season land prep  overcome peak season labour bottlenecks  increased area cultivable with fixed household labour Early planting  1-2% yield increase per day

21. Gains from Early Planting CountryCrop Gains from early planting (kg/week) Zambiacassava, dried319 Mozambiquecotton100 Zambiacotton70 Zambiamaize189 Zimbabwemaize200 Sources: Arlussa (1997), Birgess (2009), Haggblade and Tembo (2003), Barratt et al. (2006), Nyagumbo (2007).

22. Sources of CF Productivity Gains Minimum tillage requires 75% lower energy Dry season land prep  overcome peak season labour bottlenecks  increased area cultivable with fixed household labour Early planting  1-2% yield increase per day Crop residue  Soil Organic Matter (SOM) buildup  improved moisture retention  higher yields

23. Dry Season Land Preparation Source: Marenya and Barrett (2009) MVPFERTILIZERMVPFERTILIZER

24. Water Harvesting, CF Basins Water harvesting boosts the amount of water concen- trated around the crop roots. Useful in semi-arid zones.

25. Water Harvesting, Rip Lines Water harvesting boosts the amount of water concen- trated around the crop roots. Useful in semi-arid zones.

26. Sources of Maize Yield Gains Under CF Yield kg/ha Conventional plowing1,350 Conservation farming basins3,000 Sources of difference - Higher input use500 - Early planting400 - Water harvesting, SOM750 TOTAL Difference1,650 Source: Haggblade and Tembo (2003).

27. Outline Objectives Profile of cotton farm households in Zambia Sources of CF productivity gains Linear programming (LP) optimization methods LP Exercises & Discussion References

28. Method: Linear Programming (LP) Model Maximize Crop Income = Revenue (∑Pi*Qi)- cost (∑ Pn*Qn) Subject to household asset constraints –Seasonal labour availability –Animal traction (ANTRAC) –Cash –La nd

29. What Crops to Include? Crops grown in AEZ2aAll FarmsCotton Farms maize98%99% cotton25%100% groundnuts48%56% sweet potatoes16%7% sunflower11%15% beans7%4% cassava7%3% sorghum6%2% soya beans5%3% cowpeas5%2% tobacco3% millet3%1% other crops3%2%

30. Alternate Technologies Conventional Conservation Farming Hand hoe a)low input b)high input c)+ herbicides Animal traction rental d)plowripper

31. Seasonal Labour Constraints SeasonTimingHH Labour Peak (early rains)Nov15-Dec 1543 Mid SeasonDec15-Mar151 HarvestApr-July173 Dry SeasonAug-Nov14151 TOTAL 518

32. Outline Objectives Profile of cotton farm households in Zambia Sources of CF productivity gains Linear programming (LP) optimization methods LP Exercises & Discussion References

33. Exercise 1 – Baseline Scenario (Setup) Examine [LP – baseline] sheet in Excel file –Rows 01 – 25 are raw data (do not alter these). –Rows 25 – 45 will change during the exercises. –Rows 50 – 99 are where results are pasted for comparison Only yellow cells should be changed Green cells display results

34. Exercise 1 – Baseline Scenario 0a. Actual Base Case Before using Excel’s LP model to find the profit maximizing land allocation, first determine farmers’ actual land allocation Change land allocation choice variables (yellow: line 30) Input base values (set cells E30:G30 = line E25:G25) Copy results to section 0.a. (row 53:65) –Copy the entire block of values in yellow and green –When pasting, use “paste values”

35. Exercise 1 – Baseline Scenario 0b. Low Technology, Profit Maximization Open LP optimization: Go to Data tab / Solver* Set objective (H31) By changing variable cells (E30:G30) Subject to these constraints –Available technologies (E30:G30 >= 0) –Land holdings (H30 <= D30) –Seasonal labor (H39:42 <= D39:42) * If you don’t find the Solver add-in on the Data Tab, you may need to install it: Options/Add-Ins/Analysis Toolpak/Solver.

36. Exercise 1 – Baseline Scenario 0c. Low Technology, “Safety First” Set objective (H31) By changing variable cells (E30:G30) Subject to these constraints –Available technologies (E30:G30 >= 0) –Land holdings (H30 <= D30) –Seasonal labor (H39:42 <= D39:42) –Safety first (H37:38 >= D37:38) * If you don’t find the Solver add-in on the Data Tab, you may need to install it: Options/Add-Ins/Analysis Toolpak/Solver.

37. LP Results – Baseline Scenario ScenarioActualY maxSafety First Purchased inputslow Simulation #0a0b0c M1 Maize (Ha)0.500.000.69 GR1 Groundnut (Ha)0.100.000.15 COT1 Cotton (Ha)0.401.150.18 Total Hectares1.001.151.02 Crop income190283173 Cash input costs000 Household labor input peak4143 total109129111 Returns to household labor peak4.646.554.01 total1.742.191.56

38. Discussion questions: Baseline Scenario a)Why don’t farmers maximize income? b)Why do they adopt the safety-first rule?

39. Exercise 2 – Conventional Tillage 1a. Cash constraint, Safety First –Household can use only low-input technologies & must adopt Safety First risk aversion 1b. No cash constraint, Safety First –Can use all conventional technologies, but must adopt Safety First 1c. No cash constraint, plow rental possible –Includes all conventional technologies plus ANTRAC >= 0 1d. No cash constraint, household owns cattle –All conventional technologies are available in this scenario

40. Exercise 2 – Conventional Tillage 1a. Cash Constrained, Hand Hoe, Safety First Open [LP Conventional tillage] worksheet Open Solver and input the following Set objective (X31) By changing variable cells (E30:W30) Subject to the constraints –Available technologies (E30, O30, Q30 >= 0; all others == 0) –Land holdings (X30 <= D30) –Seasonal labor (X39-42 <= D39-42) –Safety first (X37-38 >= D37-38)

41. Exercise 2 – Conventional Tillage 1b. No Cash Constraint, Hand Hoe, Safety First Open [LP Conventional tillage] worksheet Open Solver and input the following Set objective (X31) By changing variable cells (E30:W30) Subject to constraints –Available tech: E30, F30, O30, P30, Q30 >= 0 (all else == 0) –Land holdings (same as before) –Seasonal labor (same as before) –Safety first (same as before)

42. Exercise 2 – Conventional Tillage 1c. Cash Available, ANTRAC Rental OK, Safety First Open [LP Conventional tillage] worksheet Open Solver and input the following Set objective (X31) By changing variable cells (E30:W30) Subject to constraints –Available tech: E30, F30, L30, M30, O30, P30, Q30, V30 >= 0 (all else == 0) –Land holdings (same as before) –Seasonal labor (same as before) –Safety first (same as before)

43. Exercise 2 – Conventional Tillage 1d. Cash Available, ANTRAC Ownership OK, Safety First Open [LP Conventional tillage] worksheet Open Solver and input the following Set objective (X31) By changing variable cells (E30:W30) Subject to constraints –Available tech: E30, F30, L30, M30, N30, O30, P30, Q30, V30, W30 >= 0 (all else == 0) –Land holdings (same as before) –Seasonal labor (same as before) –Safety first (same as before)

44. LP Results – Conventional Tillage Simulation # 1a1b1c1d Tillagehoe ox rentalown oxen Purchased inputslowhigh Safety first yes Cropped areaM1M2M7M8 Maize0.690.280.350.26 GR1GR2 Groundnut0.150.09 COT1 COT4COT5 Cotton0.180.631.411.53 Total Hectares1.021.001.851.88 Crop income ($)173203266507 Cash input costs ($)08120974 Household labor input peak43 total111123173184 Returns to household labor ($/day) peak4.014.696.1711.75 total 1.561.641.542.75

45. Discussion questions: Conventional Tillage a)1b. Why do farmers switch to M2? b)1b. Why do cash costs increase? c)1c. What major changes result when animal traction becomes available? d)1.c. Why do cash costs increase? e)1.d. What is the most important consequence when farmers own draft oxen?

46. Exercise 3 – Conservation Farming 2a. Cash constraint, Safety First –Household can use only low-input Conservation Farming technologies & must adopt Safety First strategy 2b. No cash constraint, Safety First –Can use all CF technologies, but must adopt Safety First 2c. No cash constraint, CF ripper rental possible –Includes all CF technologies plus animal traction, Safety First 2d. No cash constraint, herbicides available –All CF technologies are available, plus herbicides, Safety First

47. Exercise 3 – Conservation Farming 2a. No Cash + Low Input + Safety First Open [LP conservation farming] worksheet Open Solver and input the following Set objective (X31) By changing variable cells (E30:W30) Subject to the constraints –Available technologies (E30, G30, O30, Q30, R30 >= 0; all others == 0) –Land holdings (X30 <= D30) –Seasonal labor (X39-42 <= D39-42) –Safety first (X37-38 >= D37-38)

48. Exercise 3 – Conservation Farming 2b. Cash Available + High-Input CF + Safety First Open [LP conservation farming] worksheet Open Solver and input the following Set objective (X31) By changing variable cells (E30:W30) Subject to the constraints –Available technologies (E30, F30, G30, H30, O30, P30, Q30, R30 >= 0; all others == 0) –Land holdings (X30 <= D30) –Seasonal labor (X39-42 <= D39-42) –Safety first (X37-38 >= D37-38)

49. Exercise 3 – Conservation Farming 2c. Cash + High-Input + Ripper + Safety First Open [LP conservation farming] worksheet Open Solver and input the following Set objective (X31) By changing variable cells (E30:W30) Subject to the constraints –Available technologies (E30, F30, G30, H30, K30, L30, M30, O30, P30, Q30, R30, U30, V30>= 0; all others == 0) –Land holdings (X30 <= D30) –Seasonal labor (X39-42 <= D39-42) –Safety first (X37-38 >= D37-38)

50. Exercise 3 – Conservation Farming 2d. Cash + High-Input + Ripper + Herbicide + Safety First Open [LP conservation farming] worksheet Open Solver and input the following Set objective (X31) By changing variable cells (E30:W30) Subject to the constraints –Available tech (E30, F30, G30, H30, I30, J30, K30, L30, M30, O30, P30, Q30, R30, S30, T30, U30, V30>= 0; all others == 0) –Land holdings (X30 <= D30) –Seasonal labor (X39-42 <= D39-42) –Safety first (X37-38 >= D37-38)

51. LP Results – Conservation Farming Simulation # 2a2b2c2d Tillagebasins ripper rentalbasins Purchased inputslowhigh Herbicidesno yes Safety first yes Cropped areaM3M4 M3 Maize0.480.21 0.48 GR1GR2 Groundnut0.150.09 COT2 COT2h-liteCOT2 Cotton0.801.17 2.110.18 Total Hectares1.431.47 2.87 Crop income ($)421495 883 Cash input costs ($)061 84 Household labor input peak43 total204225 350 Returns to household labor ($/day) peak9.7411.46 20.43 total 2.062.20 2.52

52. Discussion questions: Conservation Farming a)2a vs. 2b. What changes when high-input CF becomes feasible. b)2c. Why is ripper rental less profitable than hand-hoe CF? c)2d. What major changes result from herbicide availability? d)2d. What kinds of households are most likely to adopt herbicides?

53. Optimization Summary Results HectaresCrop IncomeInput costLabor inputs (days) Returns to Labor ($/day) Cultivated($US) peaktotal peaktotal Cash-constrained households Hand hoe conventional(1a)1.02$173$043111$4.01$1.56 CF basins(2a)1.43$421$043204$9.74$2.06 Cash available for input purchase Hand hoe conventional(1b)1.00$203$8143123$4.69$1.64 CF basins(2b)1.47$495$6143225$11.46$2.20 CF basins + herbicides(2d)1.88$883$8443350$20.43$2.52 Animal traction Plow rental(1c)1.85$266$20943173$6.17$1.54 Plow with own oxen(1d)1.88$507$7443184$11.75$2.75 CF ripper rental(2c)1.47$495$6143225 $11.46$2.20

54. Outline Objectives Profile of cotton farm households in Zambia Sources of CF productivity gains Linear programming (LP) optimization methods LP Exercises & Discussion References

55. Ferguson, Thomas. 2010. Linear Programming: A Concise Introduction. Annopolis: U.S. Naval Academy.. http://www.usna.edu/Users/weapsys/avramov/Compress ed%20sensing%20tutorial/LP.pdf Waner, Stefan. 2010. Linear Programming. http://people.hofstra.edu/Stefan_Waner/RealWorld/Sum mary4.html Wikipedia. 2011. Linear Programming. http://en.wikipedia.org/wiki/Linear_programming