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Farm-level options for accelerating the transition towards climate smart agriculture

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The difference between clever and smart people is mainly that clever people can get in and out of problems which smart people would not have gotten into in the first place. In the same light, faced with multifaceted challenges related to climate change, smartness would entail adapting our agricultural systems to avoid experiencing the negative impacts of climate change. In other words, climate smart agriculture (CSA) involves changing our agricultural systems to simultaneously address climate change challenges such as low food production, accelerated land degradation and increasing atmospheric concentrations of greenhouse gases. To achieve these objectives, agricultural systems should (1) sustainably increase productivity; (2) adapt and build resilience to climate change; and (3) reduce and/or avoid the emission of greenhouse gases. As will be discussed in this presentation, there is definitely no single agricultural technology or practice that can be universally applied to achieve these objectives. Nonetheless, site-specific assessments should be pursued to identify suitable agricultural practices, technologies, polices, financing and institutional arrangements that enhance smartness within a given situation. It will be noted that CSA is not necessarily based on new practices, technologies, polices and institutions. However, it involves holistically and simultaneously addressing challenges related to climate change by using a combination of familiar practices, technologies, polices and institutions in strategic but unfamiliar ways; that are not counterproductive. Moreover, the presentation aims to start a conversation on part of the work that has been done, is being done and can be done, through CIAT, to accelerate the transition towards smarter agriculture systems to ensure that, similar to smart people, we can avoid problems that complicate ours and the lives of generations to come.

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Farm-level options for accelerating the transition towards climate smart agriculture

  1. 1. Ngonidzashe Chirinda Farm-level options for accelerating the transition towards climate smart agriculture n.chirinda@cigar.org
  2. 2. Joint work of a large working net (network of colleagues & partners) • Soils • Forages • Cassava • Rice • DAPA • FLAR • CCAFS • Livestock and Fish • UNAL-Medellìn • UC Davis • Fedearroz • Fedegan • MADR and MADS • CCAC • IRRI 2
  3. 3. Climate smart agriculture (CSA) pillars • Sustainably increasing agriculture productivity and income • Adapting and building resilience to climate change • Reducing and/or removing greenhouse gas emissions were possible • Ensuring that current and future farmers (and non-farmers) always have food on their table and money in their pockets under all climatic conditions (FAO, 2013) 3
  4. 4. My agenda today • Discussion practical farm- and field-level options for attaining climate smartness • Provide evidence on management & technological options that could promote climate smartness • Make the case that by harnessing CIAT’s collective capacities and improving them we can speed up the pace towards climate smartness 4
  5. 5. Livestock production 5
  6. 6. The big picture • ~60% of global agricultural land is grazing land • 1.5 Tg N2O emissions from animal production systems 41% of which are from dung & urine deposited on pastures • Enteric fermentation: 30% global anthropogenic CH4 emissions • Challenge: productivity, C sequestration & GHG emissions Sources: Oenema et al., 2005 www. globalmethane.org
  7. 7. Rincón, 2013 (Corpoica) Animal live weight gain (kg/ha/year) Native savanna Grass/legume pasture with fertilizer Improved pasture planted with maize Pasture after 3 years of maize-soybean rotation Degraded pasture Increasing animal live-weight gain (kg/ha/year) in acid soil savannas of Colombia
  8. 8. As we are increasing cattle productivity we are also removing GHG emissions - soil C accumulation (Loaiza et al, draft manuscript) 0.6-2.6 t carbon ha-1 y-1
  9. 9. DP_Control IP_Control DP_Urine IP_Urine Accumulatedflux(KgN2Oha -1 ) 0 2 4 6 8 10 12 DP_Control IP_Control DP_Urine IP_Urine 0 2 4 6 8 10 12 Nicaragua – Estelí 23 days after urine application Colombia – Patía 20 days after urine application *** Symbols indicate differences between treatments and regions (prueba t: † p <0,10, * p <0,05, *** p <0,01) *** Degraded pasture (DP) Improved pasture (IP) Degraded pasture Improved pasture Pasture improvement reduces soil N2O emissions from urine patches IP: Andropogon gayanus: 9 years-old DP: Paspalum notatum: 25 years-old IP : Brachiaria hybrid cv. Mulato II: 3 years-old DP: Dichanthium aristatum: 3 years-old (Chirinda et al., manuscript in prep)
  10. 10. Nitrate production rate in soilNitrate production rate in soil Mulato: low BNI -- Bh CIAT 679: high BNI MULATO (No BNI) 679 (high BNI) NN22O fluxesO fluxes Byrnes et al., manuscript submitted BNI: An innovative biological approach of reducing N2O emissions from cattle urine patches
  11. 11. Pessimist scenario Grass 70%:30% legume 10,5% reduction of Methane/animal/day Optimistic scenario Grass 70%:30% legume 32,5% reduction of Methane/animal/day Current scenario 100% Tropicales grass (Toledo) Literature: Abdolla et al 2012; Molina et al 2015; Molina et al 2016; Rivera et al 2015; Gaviria et al., work in progress
  12. 12. Cassava leaves reduce enteric methane emissions 12 Alvarez et al. manuscript in prep
  13. 13. Rice production 13
  14. 14. Big picture • Rice is the staple food for the largest number of people (~50% of the world population) • Global mean water footprint = 2497 L/kg: >50% (1670 L/kg) is related to rice production • Globally, rice fields are responsible for 20-60 Tg (3-10 %) of global CH4 emissions • Challenge: reducing the water and C footprint without reducing productivity 14 (GRiSP, 2013)
  15. 15. …where are the rice emissions coming from? 15
  16. 16. Smart water management reduce rice CH4 emissions 16 48% reduction in CH4 emissions (IPCC) Water management practices that save water & reduces GHG emissions while maintaining yields.
  17. 17. 30% reduction in water use  Better root development  Reduced arsenic uptake  Higher yields  Better nutrient availability  Reduced lodging  Reduced damage due to fungal diseases  Higher resistance to certain pests  Better soil conditions for machine operation  Reduction in incidence of mosquito-borne diseases (link to health) Source: Bjoern Ole Sander Scientist –IRRI Other benefits of AWD?
  18. 18. Colombia Paddy Rice Consortium Bogota, August 2015 Location: FEDEARROZ HQ
  19. 19. 1. AWD suitability maps (where?) 2. Field measurements of GHG emissions (reductions?) 3. Evaluation of gender roles & water distribution (adoption issues) 4. Evaluation of varietal differences and effects on CH4 emission (other options) Rice consortium activities FEDEARROZ Experimental Station “Las Lagunas” (Saldaña), 2015 Photo credit: Cristina Katto
  20. 20. Analysis based on the methodology developed by Nelson et al., 2015 (IRRI) AWD suitability maps
  21. 21. Colombia AWD - Suitability Maps 1st Semester 2nd Semester Barrios et al. Manuscript in preparation
  22. 22. Irrigation treatment Conventional AWD CumulativeFluxofCH4(mgCH4m-2 ) 0 200 400 600 800 1000 Cumulative flux of CH4 65% Arenas et al, unpublished
  23. 23. Perception of water control for irrigation • Water is charged per ha • No money saving if less water is used • Low capacity to measure water use 64.4% of HH pay for water Perception on how producers pay for water? Socio-economic study AWD: Management and use of water Expected benefits for AWD (La Hue and Katto, unpublished)
  24. 24. Important aspects for AWD implementation (Garcia and Twyman, unpublished)
  25. 25. Varietal differences and effects on CH4 emission . Variety % Aerenchyma g CH4/m2 NIPPONBARE 42,85 10.4 TAICHUNG NATIVE 38,16 7.4 IR-59469 33,78 6.6 TEQUING 26,06 6.1 ORIZICA LLANOS 25,805 7.6 (Chaparro, Zuñiga, Alvarez, Rebolledo, unpublished)
  26. 26. Capacity building
  27. 27. Analytical infrastructure: quality, speed, price Before Now 1 GC 3 GC’s Manual injection Automated 56 samples per day 480 samples per day >6 months to get results <1 month to get results Price USD 9-15 Price USD 5
  28. 28. Methodological advancements: expansion CH4 ppm (Gasmet) 0 20 40 60 80 100 120 140 160 180 200 220 CH4ppm(GasCromatography) -100 0 100 200 300 400 Gasmet-Portable FTIR Multi-Gas Analyzer
  29. 29. Intellectual capacity building: creativity
  30. 30. 30 Innovation in measurements, modelling and policies (PhD students, research institutions and policy makers) Martinez et al
  31. 31. Take home message • Farm-level options that can ensure that farmers have money in their pocket and food on their table under all climatic conditions (Productivity, Adaptation and Resilience) • By not destroying the environment, we can ensure that future farmers have money in their pockets and food on their table too (Mitigation) • By harnessing our collective efforts and continuously building our capacity we can achieve smartness faster 31

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