Spatial QUEFTS
==============

Sebastian Palmas and Robert Hijmans

Introduction
------------

Here we show how to spatially model QUEFTS. We will run an example using
data from Tanzania.

Chapter requirements
--------------------

We use the *R* packages ``Rquefts``, ``terra`` and ``reagro``. See
`these instructions </installation.html>`__ about installing these
packages.

Soil data
---------

We use soil raster data from `SoilGrids <https://soilgrids.org/>`__. We
changed the pH values from the original data source by dividing them by
10 to get the correct numbers.

.. code:: r


   library(agrodata)
   SOC <- reagro_data("TZA_ORC")
   Kex <- reagro_data("TZA_EXK")
   pH <- reagro_data("TZA_PH")

Need to fix this, there is P data in soilgrids:

“There is no P olsen raster in soilgrids, in the code below we create a
constant P Olsen layer by using the soilC layer as a template and
assigning all cells a value of 15.”

From these chemical soil properties, we can use QUEFTS to compute the
soil nutrient supply.

.. code:: r

   library(Rquefts)
   supply <- rast(pH, nlyr=3)
   values(supply) <- nutSupply(pH, SOC, Kex, Polsen=15)
   supply
   ## class       : SpatRaster 
   ## dimensions  : 1191, 1210, 3  (nrow, ncol, nlyr)
   ## resolution  : 1000, 1000  (x, y)
   ## extent      : 1037375, 2247375, -1841875, -650875  (xmin, xmax, ymin, ymax)
   ## coord. ref. : +proj=laea +lat_0=5 +lon_0=20 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs  
   ## data source : memory 
   ## names       :    lyr.1,    lyr.2,    lyr.3 
   ## min values  : 8.283516, 0.000000, 0.000000 
   ## max values  : 742.1297,  52.7725,  89.2500

We have some negative values (let’s remove them. Let’s see what happens
when we use real P values)

.. code:: r

   supply <- clamp(supply, 1, Inf)
   names(supply) <- c("Ns", "Ps", "Ks")
   supply
   ## class       : SpatRaster 
   ## dimensions  : 1191, 1210, 3  (nrow, ncol, nlyr)
   ## resolution  : 1000, 1000  (x, y)
   ## extent      : 1037375, 2247375, -1841875, -650875  (xmin, xmax, ymin, ymax)
   ## coord. ref. : +proj=laea +lat_0=5 +lon_0=20 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs  
   ## data source : memory 
   ## names       :       Ns,       Ps,       Ks 
   ## min values  : 8.283516, 1.000000, 1.000000 
   ## max values  : 742.1297,  52.7725,  89.2500
   plot(supply)

|image0|

Water limited yield
-------------------

For attainable yield, we use a raster of water-limited yield estimated
by the GYGA project.

.. code:: r

   Ya <- reagro_data("TZA_YW")

Running QUEFTS
--------------

.. code:: r

   library(Rquefts)
   maize <- quefts_crop("Maize")
   fertilizer <- list(N=64, P=20, K=0)
   q <- quefts(crop=maize, fert=fertilizer)

In this example, 64 kg/ha of N and 20 kg/ha of P comes from using 100
kg/ha of Urea and 100 kg/ha of DAP.

.. code:: r

   yield <- rast(Ya)
   values(yield) <- run(q, supply, Ya)
   plot(yield)

|image1|

.. |image0| image:: figures/clamp-1.png
.. |image1| image:: figures/Running-1.png