ESTIMATING YIELDS AND YIELD GAPS: Experiences from East Africa

Transcription

1 ESTIMATING YIELDS AND YIELD GAPS: Experiences from East Africa Godfrey Taulya, Lydia Wairegi, Piet van Asten

2 PRESENTATION OUTLINE 1. General approach 2. Yield estimation in banana/plantain systems 3. On-farm monitoring study vs. one-time farm visit survey 4. Non-destructive bunch weight estimation 5. Yield-determining factors and yield gaps

3 GENERAL APPROACH 1. What is happening in farmer fields? 2. Study single factors in controlled environments 3. Field validation and testing for interactions 4. Modeling to understand interactions and extrapolate 5. Proposing technologies for on-farm testing

4 Understanding existing spatial variability at regional scale 0 L. Edward Uganda Kenya L. Kivu Rwanda L. Victoria Burundi Tanzania km 5 S DRCongo L. Tanganyika 30 E 35 E

5 Understanding existing spatial variability diagnostics at farm scale Michael Okumu, Severine Delstanche

6 YIELDS IN BANANA/PLANTAIN SYSTEMS Integrative index for impacts of: constraints cultural practices Yield bunch weight Harvests are year-round Temporal variations in fresh bunch weight

7 YIELD DATA COLLECTION BY FARMERS One-time farm visits during surveys patchy data o Mature bunches may be weighed o Immature bunches: visual guestimates + extrapolation Farm monitoring studies comprehensive data o Challenge: Synchronizing farm visit with harvest operations Yield data collection by farmers in monitoring studies o Extra demands on the farmer (time and labour) o Literacy/numeracy skills; accuracy and consistency in data o Restricting studies to literate farmers can bias datasets Greater resource endowment, higher standard of management are correlated with higher literacy levels

8 ESTIMATION OF FRESH BUNCH WEIGHT General allometric function for estimation of fresh bunch weight (FBW, kg) at harvest was established through linear regression: Where: H is number of hands per bunch F is number of fingers on second-last hand V is the pseudostem volume at 1-m above ground (cm 3 ) k is linear regression intercept while a, b and c are coefficients Where: G 0 is girth at base of pseudostem (cm) G 1 is girth at 100 cm above ground level (cm)

9 APPLICATION OF ALLOMETRIC FUNCTION Across regions in Uganda Across cultivars in Uganda

10 LIMITING FACTORS AND YIELD GAPS Each farm was visited once in 4 to 6 weeks Input data for the allometric function were collected on flowered plants Agronomic management/crop environment, pest damage data were also collected Boundary line analysis identify the limiting factor/s and to quantify the yield gap due to each factor

11 LIMITING FACTORS AND YIELD GAPS

12 LIMITING FACTORS AND YIELD GAPS

13 Evaluation of yield-determining factors during plant growth, and Non-destructive estimation of bunch weights based on allometry permit: Identification of limiting factors Quantification of yield-gap CONCLUSIONS