Learning Conservation Agriculture the Innovation Systems way - PowerPoint PPT Presentation

Grain-SA Smallholder Farmer Innovation Programme Erna Kruger, Ngcobo P, Dlamini M and Smith H Learning Conservation Agriculture the Innovation Systems way

CA-Farmer Innovation Programme Key objectives and activities Stakeholder interaction, partnerships, horizontal Farmers days, Awareness raising and and vertical scaling symposiums, cross Access to Information visits, conferences, popular articles Learning groups; practical demonstrations, Farmer-centred workshops, field Incentives and Education assessments Innovation Market Based and Training Mechanisms System Subsidies, Village Farmer experimentation; Saving and Loan On-farm, intercropping, crop Associations, farmer farmer-led rotation, cover crops, centres, group based Research livestock integration. access to equipment and infrastructure

Trial summaries over 5 seasons; Bergville,SKZN and EC CA Farmer led Trial summaries Midlands Bergville EC, SKZN Season 2017 2013 2014 2015 2016 2017 2013 2014 2015 2016 2017 No of villages 6 3 9 11 17 18 4 10 8 8 13 No of trial participants 42 28 83 73 212 259 23 16 43 54 93 Area planted (trials) - ha 1,36 2,8 7,2 5,9 13,5 17,4 0,36 0,3 0,37 1,18 3,58 Average yield maize (t/ha) 2,04 3,74 3,63 4,12 5,03 5,7 0,95 0,7 1,37 2,52 2,17 Min and max yield maize (t/ha) 0,4-7,1 2-4,3 1-6,7 0,6-7,4 0,3-11,7 0,5-12,2 0,3-1,7 0,3-1,8 0,5-4,4 1,1-5,2 0,2-6,7 Average yield beans (t/ha) 0,62 1,24 0,26 0,79 1,05 1,22 1,26 0,34 0,69 1,28 0,35

The CA system and effect on soil fertility and soil health Intercropping with • For CA plots the pH is higher on average and acid legumes (beans and saturation lower than on cowpeas) and use control plots of cover crops • The required P has reduced increase soil fertility on CA plots • And % Org C and % N and soil health increased significantly FASTER than compared to control plots monocropping • Savings of around R400/ha made on inorganic N in three seasons • C:N ratios in the soil decrease over time for the CA plots Increased % • Soil health scores are higher Organic C and % for CA plots than control N under CA plots

Soil health; methods • Visual and quantitative indicators • Visual Soil Assessments: soil cover, soil structure, run-off, crusting, earthworms, root size, soil porosity, soil texture • Measurements: infiltration, run-off plots, weather stations • Soil health analysis

VSAs May-18 • Colour and texture – Visual soil Indicators Stulwane Eqeleni Ezibomvini more an indicator of K Dladla(T) K Dladla(C) D Hlongwane(T) D Hlongwane(C) T Dlamini (T) T Dlamini (C) M Dladla(T) M Dladla(C) C Buthelezi(T) C Buthelezi(C) P Sthebe(T) P Sthebe(C) ThZikode (T) ThZikode (C) T Zikode (T) T Zikode (C) T Mabaso (T) T Mabaso (C) N Zikode (T) N Zikode (C) S Hlatshwayo (T) S Hlatshwayo (C) C Hlongwane (T) C Hlongwane (C) P Hlongwane (T) P Hlongwane (C) soil type – so doesn’t change too much with tillage options • Soil depth- no NAME OF PARTICIPANT SOIL TEXTURE (X3) 6 6 6 6 6 6 6 3 6 6 3 6 3 6 3 6 3 3 3 3 3 3 6 6 6 3 distinction between CA SOIL STRUCTURE( AGGR) (x3) 6 6 6 6 6 3 6 3 6 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 and Conv – how to SOIL POROSITY (x3) 6 3 3 3 6 3 6 0 3 3 3 3 6 6 3 3 3 6 3 3 3 0 3 3 6 3 measure? SOIL COLOUR (x2) 2 2 2 2 2 2 4 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 2 NO. OF SOIL MOTTLES AND • Soil cover- ? Which COLOUR (x1) 1 1 1 1 1 1 2 2 2 1 1 1 1 1 1 1 1 0 0 0 1 2 1 1 1 0 version? EARTHWORM COUNTS (x2) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 SOIL COVER (RESIDUE) (x2) 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 • Infiltration – how to SOIL DEPTH( CM) (x2) 4 4 4 4 4 4 2 4 4 4 4 4 4 4 4 4 4 2 2 2 2 4 2 2 2 2 measure RUN-OFF (x2) 4 4 0 2 2 4 0 0 2 2 2 2 2 2 2 4 2 2 2 0 2 2 0 2 2 2 INFILTRATION (x2) 4 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 TOTALS 33 30 4 26 31 25 28 18 27 23 20 23 23 26 20 2520 20 17 15 18 18 19 21 27 17

Soil cover • Should be >30% • 15-30% still considered CA • <15% a problem BUT little cover in our system – we want to assess increase in cover – • Cannot assess this before spraying – complicates things too much • During season: IMPORTANT THT WEEDING DOES NOT REMOVE COVER • Option 3: A range of different percentages with more categories to be more specific: • 0 =0-15% cover • 1=16-30% cover • 2=31-45% cover • 3=46-60% cover • 4= 61-90% cover • 5= > 90% cover

Soil cover • When in the season should we look at soil cover? • How to relate that to canopy cover?

Soils O,1,2 CONTROL (CA yes or no) TRIAL Soil colour (light, ave, dark) (uniformity- specks) – (x3) Soil structure (aggregates) – (x4) Porosity (Clods, pores, organic matter)- (x5) Soil surface (run-off, texture, crusting) – (x3) Soil cover: 0-15%; 15-30%, >30% (x3)

Bulk density Period undue and Bulk k Densit sity y - VS VSA Control CA Control CT Surname Average CA (yrs) Village In the pit wall; using a pocket knife Name M+CP M+B SCC M - Knife easily pushed in, soil Ezibomvini 4 Phumelele Hlongwane 1,30 1,36 1,38 1,33 1,38 1,28 1,34 disintegrates; 1.4-1.6g/cm 3 (2) - Knife pushed in for about half the Eqeleni 5 Ntombakhe Zikode 1,35 1,49 1,37 1,32 1,38 length of the blade (1,6-1,8g/cm 3 (1) Thamela 1 Mkhuliseni Zwane 1,14 1,08 1,09 1,07 1,10 - Only knife tip can be pushed in Average bulk density 1,27 >2g/cm 3 (0) - So, is it worth doing the VSA version??? How difficult is the lab version - When should the sample be taken?

Rainfall data Averages for Ezibomvini, Eqeleni, Dec Jan Feb March April May Stulwane, Thamela and Ndunwana Monthly rainfall (mm) 185 72,25 169,2 114,7 17 5 Monthly rainfall – weather station 92,8 93,2 89,6 148,8 24,8 5,2 Monthly rainfall Ezibomvini 29,5 94 11,2 114,7 17 Mean (mm) per rainfall event 7,9 5,8 8,2 7,6 2,1 0,4 Max (mm) per rainfall event 60 30 30 20 1 3,5 • Generally the rain gauge data has under- estimated the rainfall for each month. • There are reasonably significant differences between the villages- but we don’t know whether it is real or due to haphazard recording

Run-off data Rainfall records Run-off plots litres Date Maize+Beans Maize only Maize+CP Summer CC Control Feb-18 169 35,61 18,53 37,05 35 57,59 Mar-18 114,7 7,5 1,52 8,9 7,7 23,32 Rainfall records Percentage rain converted to runoff Feb-18 169 21% 11% 22% 21% 34% Mar-18 114,7 7% 1% 8% 7% 20% • Run-off data for Phumelele only and only for two months… • % conversion should be per rainfall event but these were not correlated. • Nthombakhe’s run- off plots only recorded for 1 week at end Feb…

Infiltration Village Name and Surname Yrs under infiltration rate infiltration rate • Infiltration in CA trials CA (mm/hr) control (mm/hr) trial higher for 5 of 11 participants Stulwane Khulekani Dladla 5 587,4 531,4 • Unclear whether Dlezakhe Hlongwane 5 226,2 423,8 controls are also CA Thulani Dlamini 5 422,7 450,0 (and CA how – mono- Makhethi Dladla 5 226,6 587,4 cropped?) Pasazile Sithebe 5 544,4 478,3 Cuphile Buthelezi 5 429,2 637,7 • Difficult to say Ezibomvini Phumelele Hlongwane 4 455,5 282,5 anything Cabangile Hlongwane 3 183,0 133,9 • Continue? And if so Eqeleni Tholwephi Mabaso 5 218,8 250,8 how? – double ring, single ring??? Tombi Zikode 5 618,1 177,1 Smephi Hlatshwayo 5 434,8 218,8

Soil health(SH) scores CO 2 /10+WEOC/50 +WEON/10 =SH • WEOC – sugars from root exudates, score plus organic matter degradation • CO 2 – microbial activity/respiration • Developed by Rick Haney – to • WEON – Atmospheric N 2 sequestration accommodate for and include the from free living N fixers, plus SOM organic fractions of nutrients in soil sample analysis degradation • Recognising that soil health is a • C/N – Balance between WEOC and dynamic process of cycling of nutrients, WEON microbial activity and degradation of • MAC% - efficiency of cycling of WEOC organic matter (WEOC/CO 2 -C) • And the plant roots are active participants in the cycling providing Joining soil science and carbon sugars as root exudates to ecology into a new supply microbes with food science of soil health