Measuring the effect of site-specific soil input recommendations on choice Christopher G. Magombaa with Aurélie P. Haroub bMcGill University; aSokoine University of Agriculture Nyambi Amuri, Malgosia Madajewicz, Hope Michelson, Cheryl Palm, Johnson Semoka, Kevin Tschirhart Evidence to Action Conference May 24-25, 2017 Nairobi, Kenya Poor soil quality and the associated low crop productivity is linked to the pervasive rates of poverty and malnutrition ensnaring much of Africa (Minten and Barrett 2008, Sanchez and Swaminathan 2005). Low agricultural productivity in much of sub-Saharan Africa. For example, Tanzania: 1.3 T/ha v. US: 5.9 T/ha Cereal yields – kg/ha of harvested land (includes wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat and mixed grains SSA farmers: 9 kg/ha Latin America: 73 kg/ha Asia: 100-135 kg/Ha REASONS FOR LOW INPUT USE: Weak infrastructure – limited availability, quality of fertilizer is poor Lack of information - heterogeneous soil attributes make different levels of fertilizer profitably, recommended quantity and type of fertilizer are not adapted regionally according to area’s soil type More efficient with complementary practices Credit constrained Risk - rain-fed agriculture, price volatility

Motivation Reasons for low input use Weak infrastructure (Gregory and Bumb 2006, Jayne et al. 2003) More efficient with complementary practices (Snapp et al. 2010) Risk (Dercon and Christiaensen 2011, Morris et al. 2007) Quality concerns (Michelson et al.) Costs - credit constrained (Duflo et al. 2011) Lack of information (Foltz et al. 2011, Marenya and Barrett 2009, Duflo et al. 2008) Weak infrastructure – limited availability, quality of fertilizer is poor Lack of information - heterogeneous soil attributes make different levels of fertilizer profitably, recommended quantity and type of fertilizer are not adapted regionally according to area’s soil type More efficient with complementary practices Credit constrained Risk - rain-fed agriculture, price volatility

Motivation How do plot-specific fertilizer recommendations affect farmers’ agricultural input decisions and yields? How do these input decisions differ when farmers’ decisions are constrained by access to cash/credit. 1 - Relative to information received via traditional extension services

Outline Background Experimental design Data Preliminary results Preliminary conclusions Future work

Outline Background Experimental design Data Preliminary results Preliminary conclusions Future work

SoilDoc A portable on-farm soil testing kit Measure biological, chemical & physical conditions Recommendations provided in near real time Current results correlate highly with lab methods Lab-in-a-box developed by Dr. Ray Weil, University of Maryland Soil fertility depletion due to nutrient mining and erosion Information on soil properties is crucial for managing and maintaining soil health (ability of the soil to function properly for intended use) Limited access to soil testing service among small scale farmers – hinders quality recommendations, farmers decision on how to manage soil, and limit increased productivity After years of research – developped Sig interest – in Zambia, Tanzania and Nigeria Soil pH, Electrical conductivity, NO3-N, Exchangeable K+, Available P, Available S, and Active C

Background - literature Fabregas et al. (ongoing) Xavier Giné & Carolina Corral et al. (ongoing) Tjernstorm et al. (ongoing) Kremer et al:Fabregas et al. (2014) study how much farmers are willing to pay for results of nearby experimental fertilizer and yield plots. They first show the strong correlation between soil qualities between plots geographically close to each other. Using different elicitation techniques, they subsequently examine how much farmers are willing to pay for soil information. They find that farmers are willing to pay for such information, with a mean willingness to pay ranging between $0.30 - $2.60, depending on the method and level of information presented to the farmer.

Outline Background Experimental design Data Preliminary results Preliminary conclusions Future work

Experiment The treatment has two components: 3 treatment groups Plot-specific soil management recommendations based on SoilDoc for the 2014 – 15 growing season. A voucher worth $40 that can be redeemed for agricultural inputs. 3 treatment groups Treatment group 1: each farmer receives soil management recommendations. Treatment group 2: each farmer receives soil management recommendations and a voucher. Treatment group 3: each farmer receives a voucher. Is designed to assess whether soil information alone can improve input use and yields. … and whether relaxing the credit constraint increases the impact of information.

Population of interest The population from which we select villages and farmers for the experiment consist of: Villages in the Morogoro district in Tanzania in which farmers grow maize. Farmers who planned to grow maize in the 2016 rainy season on at least one plot on which they grew maize in the 2014 rainy season. Farmers who in 2014 had not participated in the Tanzanian government voucher program during the preceding 3 years.

Selection of treatment and control 20 randomly selected villages receive the 3 treatments. In each treated village, we randomly select 10 farmers for each of the 3 treatment groups and 10 farmers for the control group. Control group consists of 2 groups: Farmers in treated villages. Farmers in 30 randomly selected villages in which no one receives treatment: 10 randomly selected farmers in each village.

Sample 200 households receive each of 3 treatments 200 control households are in treated villages and 300 in non-treated villages Total sample: 600 treated households + 500 control households = 1100 households

Intervention Part I: soil management recommendations Soil scientists collected soil samples from all households’ plot. Which plot? Main maize plot which was cultivated during the 2013-2014 rainy season and on which maize will be cultivated in the 2014-2015 rainy season. A farmer’s main plot is defined as the plot a farmer considers to be most important to their household’s livelihood (in terms of food security and income generation). In January 2016, households received their assigned treatments from extension agents: either soil management recommendations or a voucher or both. Control households received information for the 2015 – 16 growing season.

Intervention II: vouchers

Outline Background Experimental design Data Preliminary results Preliminary conclusions Future work

Project summary - time line 8/2014: Baseline survey 8/2015: Follow-up baseline survey 8/2016: Endline survey 10/2014 – 11/2014: Soil sampling 11/2014 – 7/2015: Soil analysis 11/2015 – 1/2016: Intervention

Data Baseline survey: demographics, household and dwelling characteristics, all agricultural inputs and yields, knowledge about soil and farming practices, credit, savings and assets, plot map and details Soil sampling analysis: Soil pH, Electrical conductivity, NO3-N, Exchangeable K+, Available P, Available S, and Active C Follow-up baseline: yields/harvest, quantities of maize sold, stored and consumed, expectations about returns from fertilizers Endline survey: demographics, household and dwelling characteristics, all agricultural inputs and yields, knowledge about soil and farming practices, credit, savings and assets, plot map and details.

Attrition Baseline Soil Analysis Follow up Endline Control Voucher   Baseline Soil Analysis Follow up Endline Control 190 181 147 179 Voucher 198 188 155 187 Recommendations 191 138 177 Recommendations + voucher 203 157 Control village 268 252 244 251 Total farmers 1,050 1,007 841 984

Outline Background Experimental design Data Preliminary results Preliminary conclusions Future work

Outline Background Experimental design Data Preliminary results Preliminary conclusions Future work

Fertilizer recommendations   Control Voucher Recommend. Recommend. & voucher Control villages Total N only limited 6 12 5 35 NP limited 2 1 NK limited NS limited 101 117 111 113 196 638 NPK limited NPS limited 51 50 43 48 22 214 NKS limited 10 9 19 56 NPKS limited 11 18 179 187 188 251 1,001

Recommended cost per 0.5 acre Nutrient limitations Treatment Cost breakdown Total cost N 25kg Urea + 2x5kg Urea 35,000 + 2x11,000 57,000 NS 25kg SA + 25kg Urea 30,000 + 35,000 65,000 NP 25kg DAP + 25kg Urea 45,000 + 35,000 80,000 NPS 40kg Minjingu Mazao + 25kg Urea 50,000 + 35,000 85,000 NK 25kg MOP + 25kg Urea + 2x5kg Urea 42,000 + 35,000 + 2x11,000 99,000 NKS 25kg SOP + 25kg Urea + 2x5kg Urea 50,000 + 35,000 + 2x11,000 107,000 NPK 25kg DAP + 25kg MOP + 25kg Urea 45,000 + 42,000 + 35,000 122,000 NPKS 50kg Yara Mila Tobacco + 25kg Urea 110,000 + 35,000 145,000

Fertilizer purchases 2016 Voucher Recommend. Recommend. & voucher   Voucher Recommend. Recommend. & voucher Control Control village Urea & SA (D) 7 99 Urea & DAP (D) 1 Only urea (D) 54 88 n 198 191 203 190 268

Distribution of fertilizer purchases

Cash Voucher Recommend. Recommend. + voucher Control Control village Cash balance (D) 61 183 Cash only (D) 121 3 Cash balance (TZ Sh) 31,687 N/A 21,807 Cash only (TZ Sh) 68,000

Effect of interventions Interested in quantifying the effect of giving farmers plot-specific fert recommendations on farmers’ use of fertilizer and other inputs and on their yields The effect of the intervention (T) on maize yield & fert use for main maize plot

Effects on main maize plots (as defined in 2014)   Fertilizer used on maize plots, kg/acres - 2014 main maize plot Fertilizer used on other crops, kg/acres - 2014 main maize plot Maize yields, kg/acres - 2014 main maize plot Interaction: recommendations & 2016 -11.503 0.00 16.765 (12.064) (0.00) (62.122) Interaction: recommendations + vouchers & 2016 10.91*** 353.991 90.341 (1.700) (346.398) (59.488) Interaction: vouchers & 2016 4.549*** 0.161 27.603 (1.561) (0.159) (57.289) Interaction: control in trt villages & 2016 0.397 -60.652 (0.421) (62.597) Trt: recommendations (D) 13.063 -43.497 (12.893) (56.054) Trt: recommendations + vouchers (D) -0.01 -17.398 (0.029) (50.647) Trt: vouchers (D) 0.427 -22.217 (0.313) (47.666) Control in Trt Villages (D) -0.017 -5.31 (0.025) (57.533) Year 2016 (D) 0.291* -48.752 (0.171) (43.705) Constant 0.03 306.678*** (0.021) (30.155) R2 0.01 N 2,022

Effects on main maize plots (as defined in 2016)   Fertilizer used on maize plots, kg/acres - 2016 main maize plot Fertilizer used on other crops, kg/acres - 2016 main maize plot Maize yields, kg/acres - 2016 main maize plot Interaction: recommendations & 2016 -11.924 -0.53 (12.069) (0) (63.983) Interaction: recommendations + vouchers & 2016 11.563*** 353.246 83.384 (1.763) (346.438) (60.087) Interaction: vouchers & 2016 4.488*** 0.161 12.753 (1.546) (0.159) (57.478) Interaction: control in trt villages & 2016 -0.024 -57.07 (0.552) (65.833) Trt: recommendations (D) 13.063 -43.497 (12.893) (56.054) Trt: recommendations + vouchers (D) -0.01 -17.398 (0.029) (50.647) Trt: vouchers (D) 0.427 -22.217 (0.313) (47.666) Control in Trt Villages (D) -0.017 -5.31 (0.025) (57.533) Year 2016 (D) 0.713* -18.728 (0.396) (45.729) Constant 0.03 306.678*** (0.021) (30.155) R2 0.01 N 2,022

Effects on entire farm   Fertilizer used on maize plots, kg/acres - entire farm Fertilizer used on other crops, kg/acres - entire farm Maize yields, kg/acres - entire farm Interaction: recommendations & 2016 -12.654 -0.1 13.853 (12.49) (0.1) (68.294) Interaction: recommendations + vouchers & 2016 10.23*** 353.678 128.801* (1.485) (346.411) (65.271) Interaction: vouchers & 2016 4.477*** 0.061 42.409 (1.467) (0.188) (59.662) Interaction: control in trt villages & 2016 -0.024 -42.506 (0.552) (76.324) Trt: recommendations (D) 13.063 -50.733 (12.893) (65.388) Trt: recommendations + vouchers (D) -0.01 -50.945 (0.029) (59.803) Trt: vouchers (D) 0.427 -47.161 (0.313) (59.162) Control in Trt Villages (D) -0.017 -6.002 (0.025) (75.077) Year 2016 (D) 0.713* 0.1 -90.17* (0.396) (48.465) Constant 0.03 375.181*** (0.021) (39.212) R2 0.01 N 2,022

Monthly maize sales and purchases

Use of maize n Mean Std. dev. Min Max Kg consumed 922 556 624 16 6,860   n Mean Std. dev. Min Max Kg consumed 922 556 624 16 6,860 Kg sold 295 1009 1844 19 18,200 Kg stored 132 391 552 4 3,120 Kg harvested 926 931 1550

Expectations of yield returns

Expectations of fertilizer returns by good, bad, average season

Some Conclusions Learning effect Financial constraints are real Information flow

Asanteni Sana