Chapter 4
Program AEZCCS03

Program AEZCCS03 processes the records of the land inventory and computes for each location (cell) the production potential by single crop as well as crop combinations according to the following rules :

Application of these rules yields a productivity factor relating average yield to the maximum attainable yield of a particular crop. In each location every admissible crop combination is also evaluated in terms of implied soil loss from erosion (so far water erosion only is considered).

(1) Crop growth cycle suitability

(2) Thermal zone rating

(3) Soil unit rating

(4) Slope gradient cultivation factor

(5a) Coarse material rating

(5b) Texture rule

(6) Phase rule

(7) Fallow land requirements rule

(8) Pattern distribution

4.1 Program Logic and Overview

After reading the control information (subroutine CTRLIN), the required input files are opened and the data buffers, where required, are cleared (subroutine CLRALL). Then, several ‘in-core’ tables related to productivity assessment are read from random access files (subroutine TBGETO). These include crop cycle requirements, pattern distribution parameters, pattern LGP codes, and the master index containing the record addresses of the respective primary crop yield table entries.

At this stage the program enters its major processing loop. The next record from the land inventory file is read (subroutine READIN) and the data buffers specific to the processing of the current cell are cleared (subroutine CLRLNV). Cell specific information is fetched from random access (subroutine TBGET1) and primary production for each crop is calculated under the specific agro-climatic conditions.

FIGURE 4.1
Information flow in program AEZCCS03

Crop productivity assessment takes into account water stress situations (function INTZNF), soil related productivity discount factors (subroutine SLYSET), inter-cropping increment multipliers (function RICR), and rest period requirement factors (function FALLOW).

If at least one feasible crop, i.e. a crop that is sufficiently productive in the given environment, is identified in the current cell, then relevant cell information is saved (subroutine PUTBIN) and data processing continues.

As a next step, the crop combination independent multipliers of the USLE (universal soil loss equation) are calculated: the rain erositivity factor (function RUSLE), the rain erositivity distribution during the growing period (subroutine REDIST), the soil erodibility multiplier (function KUSLE), the slope length factor (function LSUSLE), the protection factor (function PUSLF), and a general management factor table (function MUSTAB) relevant under the given agro-ecological conditions and the chosen input level.

A major task in the land productivity assessment is the construction of sequential crop combinations (subroutine COMBI). Amongst all possible combinations admissible cropping patterns are filtered out (function LCOMBI), evaluated (subroutine CMBVAL) and, subject to certain criteria (function VALUE), saved for later processing (subroutine PUTCMB).

The crop combinations are also assessed in terms of degradation hazards. Crop combination specific multipliers in the USLE are calculated, a combined crop cover and management sub-factor, by matching the sequential cropping pattern to the component LGPs implied by the current pattern and LGP codes (function CUSLE). Estimated annual soil loss is then translated into estimated productivity loss (function PYLOSS).

At this point, the program has completed data processing for the current cell and returns to reading the next land inventory record. The described computations continue until EOF is reached, i.e. the last cell has been processed.

The structure and information flow in program AEZCCS03 is shown in Figure 4.1 and Figure 4.2, the latter containing aspects related to computations dealing with sequential crop combinations.

FIGURE 4.2
Information flow in crop combination sub-component of program AEZCCS03

4.2 Input/Output File Sizes and Connections

^a) ‘V’ indicates a variable, district or country specific number of records.

^b) FS = formatted sequential, UR = unformatted random access, US = unformatted sequent.

^c) R = input, W = output.

^d) Since yield information is saved only for crops which exceed the productivity threshold level, the actual number of records and size of file RND02 is significantly less than the indicated maximum file size (about 2Mb in the Kenya Case Study).

^e) ‘$$$’ in file names BIN.$$$ and LINV.$$$ has to be replaced by the respective three digit district codes. The land inventory generated from the basic resource maps used in the Kenya Case Study comprises of 91608 records describing the climatic and land resources of 41 districts in Kenya. The number of records per district ranges from 178 (Mombasa) to 6358 (Machakos), with a median value of 2068 (Nyeri).

4.3 Subprogram Description

4.4 Program Control Data, File CTRL03

Each AEZCCS program is controlled by a short input data file which is usually read from standard input. Table 4.1 shows an example used to run program AEZCCS03 for land productivity assessment of Nyeri district in Kenya at intermediate level of input.

TABLE 4.1
Control input data for program AEZCCS03

It should be noted that the fourth line of the control data file specifies the file name of a temporary working file which is usually of fairly moderate size - typically less than 20 K bytes. This file is re-written for every land inventory cell that is processed and its actual size depends on the number and kind of sequential crop combinations that have to be evaluated in the productivity assessment. It is highly advisable to use a medium that can be accessed quickly for storing this file to speed up program execution. On personal computer systems a RAM disk should be used whenever possible.

where

4.5 Computerized Land Inventory Data, File LINV.$$$

The computerized land inventory contains information on land units which are unique in terms of the indicators which comprise each record. In the Kenya Case Study the land inventory records have a length of 41 characters with the following contents:

A description of the respective coding schemes used in the Kenya Case Study can be found in Appendix A.

4.6 Additional Crop Factors, File COMMDAT

Program AEZCCS03 requires the input of additional crop factors which are read from a text file, named according to the contents of line five of the control file (variable FNTAB). The format of the relevant part in this file, records beginning with a 2-character card label CF, will be described in the next section dealing with the LP Matrix Generator program AEZCCS04.

4.7 Crop Productivity Assessment Results, File BIN.$$$

As mentioned earlier, the crop productivity assessment results are save to an unformatted sequential file with variable length records, named as specified in variable FNBIN, i.e. line two of the control input file.

The very first record of the file contains information on input level and maximum number of members in sequential crop combinations. Then, each cell begins with a cell record, containing land inventory information and the number of feasible crops in this cell. When the cell can be used by at least one crop activity, additional cell information regarding soil loss and conservation is saved. For each admissible crop combination the following information is stored: total number of members, crop number of members, maximum attainable yield, minimum, average and maximum yield factors relative to maximum yield.

The size of the crop production assessment file can vary substantially, depending on the number and kind of agro-ecological cells, the number and type of crops in the assessment, and the setting of the control variables NCCBS, ALLCMB, CYCFRC, NOMONO, YUMIT and YUMCB.

The crop productivity assessment file forms the basis for all further analysis, be it single crop suitability tabulation or district planning models. As an example, Figures 4.3 to 4.5 show, at aggregate level, productivity assessment results for crops, pastures and fuelwood species, respectively, as derived from district-wise crop assessment files of Kenya.

FIGURE 4.3
AEZ productivity assessment, crops

FIGURE 4.4
AEZ productivity assessment, pastures

FIGURE 4.5
AEZ productivity assessment, fuelwood species