Grey Water Drip Irrigation Intervention: Minas Gerais & Sao Paulo Brazil

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23rd Sep 2019 Environmental Sciences Reference this

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Grey Water Drip Irrigation Intervention: Minas Gerais & Sao Paulo Brazil


  1. Environmental Assessment

This study is set to utilize treated greywater for drip irrigation purposes in randomly selected coffee estates growing C. Arabica trees in southeastern Brazil’s microregions of Minas Gerais and Sao Paulo. It seeks to test the hypothesis that a treated  greywater drip irrigation intervention for coffee farmers in water stressed areas of SE Brazil will prove an efficient and productive climate change mitigation strategy.  This will be assessed with measures of crop yield in 60 kg bags/hectare and relative measures of overall water needs per year in experimental farms versus control group farms (i.e. rainfall mm/day, temperature 0C, evapotranspiration in mm/day, and relative humidity in g/m3). The study will be conducted from June 2019 until June of 2022. 

 The State of Minas Gerais is located between the parallels 140 13’ 57” S and 220 55’ 22” S of latitude and meridian 390 51’ 23” W and 510 02’ 45” W of longitude (Assis et al., 2014; PGC 2019).  The State of Sao Paulo is located between parallels 220 8’ 14.28” S and 220 39’ 54” S of latitude and meridian 450 23’ 20.4” W and 510 00’ 34” W of longitude (PGC 2019). According to the Koppen Climate Classification, Minas Gerais is climate subtypes Aw, BSw, Cwa, and Cwb (i.e. semiarid to temperate with dry winter & rainy summer) while Sao Paulo is Cfa, humid & sub-tropical (Weatherbase 2019).  Average total rainfall in Minas Gerais fell from 93.3 mm per month in March 2014 to 78.03 mm per month in March 2018 (IBGE, 2019). Sao Paulo’s average total rainfall in March 2014 was 93.04 mm per month falling to 50.86 mm per month in March of 2018.  Temperatures in both regions hold steady averages between 15-250 C however maximum temperatures continue to climb in both regions during drier months (Weatherbase 2019).

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 These two states are the largest coffee producing regions of Brazil and are experiencing significant shifts in hydrologic cycle due to increasing temperatures and shifts in rainfall patterns (Gorgich & Formigo 2016; Jain, 2012; Salemi et al., 2012). Farms from this region are ample sites for field testing efficiency in greywater drip irrigation as water scarcity is prevalent due to decreased water reserves, increasing average temperatures, and shifts in rainfall patterns (Scott 2015).  Studies have shown that drip irrigation increases coffee yield by at least 66% versus non-irrigated crops and is extremely efficient at utilizing water supply an especially important factor water stressed regions. According to recent studies, agricultural water use in Brazil hones almost 70% of water consumption from rivers, lakes, and aquifers yet only 15-50% of this water reaches its intended recipient (Salemi et al. 2012).  Drip irrigation, when working appropriately, increases efficiency by up to 100% compared to conventional irrigation methods (Gorgich & Formigo 2016; Jain, 2012; Ripening, 2010). 

Each of the coffee farms to be selected for the study are from a 12,000 member sample pool from the world’s largest coffee cooperative, Cooxupe (Cooxupe 2010).  This group has roughly equal numbers of coffee growers from both Sao Paulo and Minas Gerais making it a reasonably good fit for equally representing coffee farms from both regions. Each individual grower in the coop has equal chance of being selected from this pool and a total of 1,000 farms will be randomly selected from the membership list for participation in the intervention.  Control group farms of equal number will also be selected from the same pool given the known consistency in statistics of each of the coop’s growers (Cooxupe 2010).  All relevant counterfactual data will come from standardized surface meteorological sites and reference data for evapotranspiration given agro-climate type of the region (IBGE, 2019) Each farm in the intervention group will be composed of a similar number of coffee plants relative to total hectares with equal plant spacing density, number of total rows, and average plant heights at baseline of intervention (Cooxupe 2010).

Initial measurement of soil composition and municipal grey water analysis will be completed for each site participating in the study. Soil thermometers, rain gauges, and psychrometers are to be installed at each farm to track daily readings for temperature, precipitation, and relative humidity. This will also gain metrics for calculating evapotranspiration as this is temperature and relative humidity dependent (Hanson 2015; Jain 2012) .  Municipal water sourcing for greywater supply is to be piped into each farming locale based on expert recommendation from local extension educators.  Drip irrigators and emitters with a flow rate of up to 4 L/hour will be installed with best practice recommendations for coffee trees including installation of filters, adding acidity agents to the irrigated water, and spacing nozzle header 50-85 cm apart (Assis et al. 2014).  Water efficiency measurements will be gained from theoretical baseline averages, actual project water use measured from meters on the drip irrigators, and return water flow from evapotranspiration (ETc) of the plants.  The ETc metric is a calculation of crop water demands utilizing a reference measure (EV0), crop coefficient (Kc), and ground cover coefficient  Kr, expressed in millimeters per unit time (Hanson 2015). This allows for calculating the water needs per farm with reference values of water demands for C. Arabica trees in respective microregions at 5-7 mm/day at peak times.  These periods are typically the drier winters months where rainfall patterns are lowest or the 10-17 week period following flowering of the coffee tree where ample water is critical to survival of the fruit. Each study site will utilize these average and equations below to assess expected irrigation needs (“Global Coffee Farming: Keeping the Fields Alive,” 2016; Llobet 2017; Peasley & Rolfe 2003).

  1. Baseline Water needs
    1. WBI: total baseline irrigation/season, m^3/ham^3/ha
    2. WBRF: baseline return flow/season,
    3. YBCY: baseline crop yield, 60 kg bags/ha
  2. Project Water Needs
    1. WEI: total experimental water use, m^3/ha
    2. WERF: total experimental return flow, m^3/ha
    3. YEY: total experimental crop yield, 60 kg bag/ha
  3. Greywater needs
    1. WGWB: WBI-WBRF (baseline)
    2. WGWE: WEI-WERF (experimental)
  4. Water Deficit (WD)*
    1. Precipitation (P)-Evapotranspiration (EP)=Water Deficit(WD), mm/day
    2. ETc=ETo *Kc *Kr

*Agro-climatic zone dependent

*EP=Evaporation (soil)  + transpiration (plant)

Methodologic assumption is that total experimental water use minus experimental return flow will be roughly equivalent to the amount of potable water saved from otherwise being diverted away from human use for agribusiness purposes. 

All farms receiving greywater drip irrigation will apply an actual schedule based on soil water potential value, essentially an underground pressure measurement in kilo Pascals (Assis et al. 2014) .  This will be indirectly measuring soil moisture content with tensiometers and an electronic tensiometer with a hypodermic needle (Assis et al. 2014).  These are to be installed at baseline of intervention in the soil along the root lines of trees positioned below drip line source at depths of 0.10, 0.25, 0.40, 0.60m respectively.  Extension agency experts will advise as necessary with placement of these apparatuses to ensure optimal placement. This ensures arrangement of the irrigation lateral lines and the depth of the tensiometers are in optimal arrangement for each coffee tree row’s root system.  Irrigation will apply whenever soil water tension reads around the 0.25 m depth near pre-determined pressure values (i.e. 20 or 60 kPa).  These depth and pressure ranges have been noted as optimal indicators for irrigation purposes in the C. Arabica tree as the part of the root system in a mature tree best for absorption of nutrients, including water, are typically positioned around this area in the soil (Llobet 2017; Peasley & Rolfe 2003).  Since all trees in the study are mature trees, it can be assumed when the root system seeks water and exerts force on the soil around it, the tensiometers will be triggered and engage the irrigation system at optimal times for the survival of the plant (Assis et al. 2014).  This ensures water is only dispersed from the irrigation system when the plant truly needs it to maintain critical moisture, reducing likelihood of unnecessary water utilization and waste. 

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This system reduces water loss from the stems and leaves of the coffee trees minimizing evaporative loss to the air and sun.  Hence, the ETc during irrigation is expected to favor conditions of optimal water efficiency for C. Arabica trees in study sites.  Minimizing preventable transpiration from the plant and evaporation from the soil during irrigation periods ensures best practice in only using the total volume of water absolutely necessary for plant survival, for optimal coffee yields, and from the municipal water facility (Jain, 2012; Llobet 2017; Peasley & Rolfe 2003; Sage 2014) .

In order to assess coffee yields, trees will be harvested during June and July when the least amount of coffee beans should be left green ideally less than 15% of total coffee bean volume harvested (Llobet 2017; Ripening 2010; Scott 2015).   At this stage, all farms in the study will be harvested to move into standard air drying processes until the beans are around 12% moisture.  From this state, mass of coffee yield will be converted to industry standard 60 kg bag per hectare (Assis et al. 2014).

Statistical analysis will be done using an independent two sample t-test  to compare the means between the control group and experimental group of water efficiency and coffee yield.  Large sample size and standard confidence interval of alpha values of 0.05 assumes normal distribution of expected data for this study.  Simple linear regression models would take into account comparison of group means on all variables: rainfall(B1), temperature(B2), evapotranspiration(B3), relative humidity(B4), irrigation water use(B5), and coffee yield (Cy).



  • Assis, G. A. de, Scalco, M. S., Guimarães, R. J., Colombo, A., Dominghetti, A. W., & Matos, N. M. S. de. (2014). Drip irrigation in coffee crop under different planting densities: Growth and yield in southeastern Brazil. Revista Brasileira de Engenharia Agrícola e Ambiental, 18(11), 1116–1123.
  • Cooxupe. (2010). Cooxupe. Retrieved from

Global Coffee Farming: Keeping the Fields Alive. (2016). Retrieved from

  • Gorgich, M., & Formigo, N. (2016). A Study on the Possibility of Using Greywater in Irrigation of Agricultural Products Supervisor. Retrieved from
  • Hanson, B. (2015). Crop Coefficients Irrigation Water Management : Art, Science, or Guess? Davis, CA. Retrieved from
  • Institue of Brazilian Geography and Statistics. (2019). Census of Agriculture.
  • Jain, N. (2012, March). Drip Irrigation for Coffee Plantations. Irrigazette: Saving Water Magazine-The Irrigation Industry. Retrieved from
  • Llobet, A. (Llota D. cafe). (2017). A Coffee Producer’s Guide to Soil Management & Farm Conditions. Perfect Daily Grind, All. Retrieved from
  • Peasley, D., & Rolfe, C. (2003). Developing irrigation strategies for coffee under sub-tropical conditions. Retrieved from
  • Poloar Geographic Coordinates. (2019). PGC Cooridnate Converter. Retrieved from
  • Ripening, C. (2010). Drip Irrigation for Coffee Plantations : feasible and profitable, (September). Retrieved from
  • Sage, E. (Coffee S. M. (2014). Basic Plant Biology: Keeping the Coffee Plant “Happy.”
  • Salemi, L. F., Groppo, J. D., Trevisan, R., Seghesi, G. B., Moraes, J. M. De, Fronsini, S., … Martinelli, L. A. (2012). Consequências hidrológicas da mudança de uso da terra de floresta para pastagem na região da tropical pluvial Atlântica Tel .: floresta Hydrological consequences of land-use change from forest to pasture in the Atlantic rain forest region, 11(12).
  • Scott, M. (NOAA/Climate. gov. (2015, June). Climate & Coffee. Retrieved from
  • Weatherbase. (2019). Sao Paulo Koppen Climate Classification.

Final presentation DUE WEEK 10:

 Week 11

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