| Literature DB >> 33344151 |
Willy Pradel1, Marcel Gatto2, Guy Hareau1, S K Pandey3, Vinay Bhardway4.
Abstract
Adoption of improved varieties is an important strategy to adapt to the negative implication associated with climate change and variability. However, incomplete data on varietal release and adoption is often the reality in many countries hindering informed decision-making on breeding and varietal dissemination strategies to effectively adapt to climate change. In taking the example of potatoes in India, we analyze the extent to which the potato sector is resilient to climate change. We do so by comparing state-level climate change projections with adoption of high resistant and tolerant potato varieties to major abiotic and biotic stresses. Release and adoption data was collected in 2016 in six expert elicitation workshops conducted with 130 experts from the potato value chain in Bihar, Gujarat, Karnataka, Punjab, Uttar Pradesh, and West Bengal. We found that from the total of 81 releases, 45 improved varieties are adopted in India and that in each state high resistant and tolerant varieties are cultivated providing some degree of varietal resilience. Early maturity has been the most important and heat tolerance is the least important trait. Comparing climate projections with adoption rates of high resistant and tolerant varieties, we found that Gujarat is relatively most resilient. In other states we found some mismatches between climate projections and adopted specific varietal traits. Our results allow policy-makers and breeders to better prioritize investments into breeding for specific traits and dissemination strategies.Entities:
Keywords: Adoption study; Climate change; India; Potato varieties; Resilience
Year: 2019 PMID: 33344151 PMCID: PMC7729832 DOI: 10.1016/j.crm.2019.01.001
Source DB: PubMed Journal: Clim Risk Manag ISSN: 2212-0963
Potato: area, production and yield in major producing states, 2015–2016 cropping season.
| State | Area ('000 has) | Production ('000 t) | Yield (t/ha) |
|---|---|---|---|
| Uttar Pradesh | 607.32 | 13,851.76 | 22.81 |
| West Bengal | 427.00 | 8,427.00 | 19.74 |
| Bihar | 319.13 | 6,345.52 | 19.88 |
| Madhya Pradesh | 141.05 | 3,161.00 | 22.41 |
| Gujarat | 112.40 | 3,549.38 | 31.58 |
| Assam | 104.83 | 1,037.26 | 9.89 |
| Punjab | 92.99 | 2,389.48 | 25.70 |
| Karnataka | 48.08 | 651.45 | 13.55 |
| Other States | 280.77 | 4,356.72 | 15.52 |
| India (total) | 2,133.58 | 43,769.56 | 20.51 |
Source:Directorate of Economics and Statistics (2017).
List of varieties released in India indicating their main climate change related attributes and preferences to Indian farmers.
| Variety name | Yield potential (t/ha) | Crop maturity1 | Heat tolerance | Drought tolerance | Late blight resistance |
|---|---|---|---|---|---|
| KUFRI PUKHRAJ | 35–40 | EARLY | SENSITIVE | HIGH | MEDIUM |
| KUFRI JYOTI | 25–30 | MEDIUM | SENSITIVE | MEDIUM | MEDIUM |
| KUFRI BAHAR | 30–35 | MEDIUM | SENSITIVE | MEDIUM | SENSITIVE |
| BHURA ALOO | 24 | LATE | LOW | SENSITIVE | SENSITIVE |
| KUFRI CHIPSONA | 30–35 | MEDIUM | SENSITIVE | MEDIUM | HIGH |
| KUFRI SINDHURI | 30–35 | LATE | HIGH | MEDIUM | SENSITIVE |
| KUFRI CHANDRAMUKHI | 20–25 | EARLY | SENSITIVE | HIGH | SENSITIVE |
| KUFRI KHYATI | 25–30 | EARLY | SENSITIVE | HIGH | HIGH |
| LADY ROSETTA | 30 | EARLY | HIGH | HIGH | SENSITIVE |
| KUFRI MEGHA | 25–30 | MEDIUM | SENSITIVE | MEDIUM | HIGH |
| KUFRI KANCHAN | 25–30 | MEDIUM | SENSITIVE | MEDIUM | MEDIUM |
| KUFRI ARUN | 30–35 | MEDIUM | SENSITIVE | HIGH | HIGH |
| KUFRI SURYA | 25–30 | EARLY | HIGH | MEDIUM | SENSITIVE |
Notes: 1Early: 70–90 days, Medium: 90–100 days, Medium late: 100–110 days, Late: > 110 days; a selection of further reading on varietal traits and varieties include Kumar et al. (2014) on yield potential, late blight resistance and crop maturity; Kumar and Minhas, 2013, Sharma et al., 2014 on drought tolerance and late blight resistance; Kumar and Sinha (2009) on Bhura Aloo; Sadawarti et al., (2018) on Kufri Khyati; Patel et al. (2005) and Minhas et al. (2006) on Kufri Surya.
Source:Gatto et al., 2016a, Gatto et al., 2018.
Most adopted potato varieties in six Indian states in 2015.
| Variety | Area (has) | Percentage of total area1 | |
|---|---|---|---|
| Kufri Pukhraj | 521,375 | 33% | |
| Kufri Jyoti | 325,665 | 21% | |
| Kufri Bahar | 272,642 | 17% | |
| Kufri Pukhraj | 121,464 | 39% | |
| Bhura Aloo | 70,539 | 22% | |
| Kufri Sindhuri | 31,082 | 10% | |
| Kufri Pukhraj | 95,630 | 85% | |
| Kufri Khyati | 10,706 | 10% | |
| Kennebec | 2192 | 2% | |
| Kufri Jyoti | 38,993 | 94% | |
| FL-1533 | 1170 | 3% | |
| Kufri Pukhraj | 57,411 | 64% | |
| Kufri Jyoti | 14,625 | 16% | |
| Lady Rosetta | 3600 | 4% | |
| Kufri Bahar | 271,328 | 45% | |
| Kufri Pukhraj | 167,176 | 28% | |
| Kufri Chipsona 1 | 59,526 | 10% | |
| Kufri Jyoti | 228,539 | 56% | |
| Kufri Pukhraj | 79,321 | 19% | |
| Kufri Chandramukhi | 30,919 | 8% | |
Note: 1for 6 states which represents 75% of total potato area in India.
Source:Gattoo et al., 2016b, Gatto et al., 2018.
Expected effects of climate change on temperature, rainfall, and potato yield with no adaptive measures by state.
| State | Temperature | Rainfall | Effect on potato yield |
|---|---|---|---|
| Increasing trend | Mixed trend | Negative | |
| Increasing trend | Increasing trends | Negative | |
| Increasing trend | Mixed trends | Negative | |
| Decreasing trend | Decreasing trend | Mixed | |
| No change | Mixed trend1 | Mixed | |
| Increasing trend | Mixed trend | Negative |
Notes: 1Positive in western parts of Uttar Pradesh, negative in eastern parts of Uttar Pradesh; a detailed table showing the specific effects and their predicted magnitudes and associated literature can be found in Appendix 1.
Fig. 1Map of India and Varietal Resilience Indicators for studied states. Source: Own calculations based on Gatto et al. (2015), Gatto et al. (2018).
| Minimum temperature (°C) | Maximum temperature (°C) | Rainfall (mm) | Potato Yield (t/ha) | |||||
|---|---|---|---|---|---|---|---|---|
| Current | Change (%) | Current | Change (%) | Current | Change (%) | Current | Change (%) | |
| Bihar | 10.52, 20.03 | +32.0%1, No change4 | 25.31, 31.02 | +8.7%1, Increasing trend3 | 1225 6, 11165 | −55.2%6, +12%7, Increasing trend3 | 25.81, 40.88 | −18%1, −11.5%9 |
| Gujarat | 22.010, 21.02 | +5.5%9, Increasing trend3 | 33.49, 33.82 | +2.1%9, Increasing trend3 | 8734, 7009 | +44.6%5, Increasing trend3 | 37.57 | −12.7%7, −55.1%8 |
| Karnataka | 19.02 | Increasing trend3 | 29.52 | No change3 | 8524 | Northern: +77.6%5 | 6.111 | −45.7%8 |
| Punjab | 16.02 | Decreasing trend3 | 30.02 | Decreasing trend3 | 7474 | +52.9%5, Decreasing trend3 | 51.27 | −5.3%7, +3.7%8, −6.5%12 |
| Uttar Pradesh | 18.82 | No change3 | 32.32 | No change3 | 8774, 94613 | Western: +1.5%5 | 45.37 | −13.4%7, +9.1%8 |
| West Bengal | 20.114, 22.52 | +6.5%12, Increasing trend3 | 28.912, 31.02 | +4.5%12, No change3 | 220312, 175015 | +95.2%5, +13.7%12, Decreasing trend12 | 39.27, 17.412 | −12.0%7, −16.1%8, −25.3%12 |