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RESULTS
PS123 and simulated yield
PS123 model gave best result with 213 Julian planting date but model performance was poor with other planting date that was also used in the site. Therefore, the planting date best in the study site with this sets of climatic and crop data could be 213 Julian day. Further, model is well performed with different degree of salinity and maize yield is severely affected with salinity level beyond 8 dS/m in most soil units which is supported by several studies in different parts of the world. The result shows clay loam texture soil gives higher and less affected with soil salinity. It could be the effect of higher CEC and water holding capacity of the soil that delays in stress.
The model was tried to simulate the yield of cassava and rice with default crop parameters by changing total heat requirement and planting time. It simulates the cassava yield that is low but it did not performed well with rice. Since crop parameters vary from location to location that may lead to this result. Therefore, true site specific crop parameters could simulate reliable results.
Some selected measured soil salinity and fertility variables (EC, pH, CEC and OM) were used to estimate their variation in geopedologic units at “relief type” and at “landscape” levels, and also their spatial variation and dependency to estimate the area affected by salinity. Soil salinity and soil fertility varied significantly within and between geopedologic units at “relief type” and “landscape” levels (see table 2). Moving average gave best interpolation result in salinity and fertility modelling. PS123 simulates crop yield that correlates significantly with farmers yield (see table 1. ). This was not true for the case of CropSyst. Both models simulate crop yield with different degree of salinity (sensitivity analysis). Now a days, the development of GIS and remote sensing based models are used in various fields of studies. The GIS and remote sensing tools and techniques are commonly applied in natural resources management. These were also applied in order to compare the results obtained from the deterministic models. Result of a case study of applying image transformation by means of band rotation to enhance soil spectral reflectance and combining.
Table 1 : Farmers’ maize management (interview)
X
Coor		 Y
Coor		 Village Name Planting Date Harvest
ing (days)		 Yield (kg/rai) Basal Manure (N) (kg/rai) Basal N (kg/rai) N within 30D kg/rai N after 30D kg/rai N after 45D kg/rai N after 60D kg/rai No of plough Time of plough Imple
ment		 Residue amount Mixing %
805841 1659445   26 Jul 105 1100 0.5     16.8 4.5            
805838 1659510   20 Jul 120 700
-
800		       8.52 6            
810782 1661525 Nong Hoi June/
Jul		 95 800   1.5   23   2.5 2 June/
Jul		 Disc plough 1000 70
810782 1661525 Nong Sa Kae July 90 1000   3.75   26.75     3 June Disc plough 3500 70
808382 1661525   June/
Jul		 95 700   2.1   23              
809347 1657224   July 90 800       7.5 7.5            
807495 1657703   20 Jul 115 700
-
800		   2.72   13.5              
802068 1678891 Sa Cho rake 12 Jul 120 1000   4   11.5              
802068 1678891   July 120 1000   4   11.5              
802068 1678891   Jul 120 1000   2.72   4.95 2.72   2 before rain Tractor    
Mgt (2)
815470 1671678   Aug 120 500 3.75                    
816900 1672178   Aug 120 1000   6.28       9.68          
816900 1672178   Aug 120 500           8          
812947 1666227 Nong Ka Done Aug 120 500           2.21 2 prev month & plant      
Mgt (3)
801717 1664903 Nong Kradon 1st Sept 70 286 3.36   9.66       2 1 week & before plant Machine    
Nong Luang Sept 120 500 0.125     7.5              
815970 1672178   Oct 120 1000 1.8         8         Leave in the field
Table 2 : Total dry matter of maize with different degree of salinity
Soil unit Soil texture GW depts (cm) ECw
(dS/m)		 0 dS/m 1 dS/m 2 dS/m 3 dS/m 4 dS/m 6 dS/m 7 dS/m 8 dS/m 9 dS/m 10 dS/m 12 dS/m 14 dS/m 16 dS/m 18 dS/m 20 dS/m
PE111 SL 510 0.95 8400 8036 7840 7489 7205 6742 - 12 - - - - - - -
PE112 LS 320 1.50 10461 10036 9828 9444 8499 12 - - - - - - - - -
PE113 SL 380 1.00 8864 8435 8272 7827 7401 6618 - 12 - - - - - - -
PE114 SL 300 0.80 9885 9149 8906 8747 8325 7088 6501 12 - - - - - - -
PE115 SL 334 3.34 9188 8617 8406 7991 7509 6666 - 12 - - - - - - -
PE211 SL 285 1.08 9851 9310 8877 8624 8600 7603 6643 12 - - - - - - -
PE211 CL 285 1.08 13047 12583 12455 12284 12197 11786 - 11518 - 11358 11227 11044 10847 10531 12
PE311 SL 273 0.48 10235 9715 9149 8811 8551 7919 7051 12 - - - - - - -
PE411 SL 140 6.85 14857 14640 14302 13806 13274 11055 7924 224 - - - - - - -
PE412 SL 176 3.12 13769 13300 13064 12493 11728 10521 9462 6185 12 - - - - - -
PE413 SL 122 3.32 15108 15108 15457 15447 15079 13292 - 5737 12 - - - - - -
PE511 SL 500 0.50 8419 8046 7829 7511 7211 6492 5856 12 - - - - - - -
VA111 SCL 112 10.00 10963 10965 10483 9960 9779 9232 9513 8854 7757 2069 - - - - -
VA211 LS 171 5.40 14472 14275 13874 12440 2036 12 - - - - - - - - -
VA311 SL 240 5.00 10959 10985 10557 10073 9375 7767 7442 12 - - - - - - -
Yield Response to Salinity Sensitivity with Cropsyst
The model did not give reliable result of simulated yield with collected data from the primary and secondary source. After that, sensitivity analysis with different degree of salinity was practiced in order to study the sensitiveness performance of the model. Yield is reduced with higher salinity. The reduction is high beyond EC value 4.
Table 3 : Total maize yield in response to different degree of salinity (sensitivity analysis)
EC (Salinity dS/m) Maize yield (kg/ha)
0 2471.690
1 2471.690
2 2461.390
3 2272.230
4 1232.678
5 600.161
6 275.599
7 106.993
8 48.788
9 28.307
10 8.991
11 8.788
12 6.397
13 5.874
14 5.348
15 4.84
16 4.578
17 4.246
18 4.064
19 3.905
20 3.666
PS123 AND SIMULATED CROP YIELD RESULT
The model successfully simulates the total dry mass (TDM) yield with different degree of salinity. The yield in different geopedologic units vary with soil salinity in relation to ground water depth and its salt content. Also, texture classes showed effect on simulated maize yield and had close relationship with soil fertility. Clay is one of the components of soil texture class which has significant relationship with the cation exchange capacity. Similarly, organic matter varied between geopedologic units. CEC and OM could be considered as indicators for soil fertility factors. This model took into account these factors while simulating the crop yield.
Since the crop yield is also affected by factors other than soil and ground water salinity, ground water depth and soil texture such as crop varieties, fertilizers, pests and diseases, and agronomic practices, the relationship presented refers to Pioneer high yielding variety, well-adapted to the local environment assuming optimum agronomic practices and adequate input supply are provided. Therefore, the simulated yield could vary with the real yield data but the presented yield relationship in relation to salinity is possible to plan, design and operate management system taking into account the effect of different degree of salinity on crop production.
Yield Modelling with different Degree Of Salinity Maize Yield estimate from Landuse/Cover Map
Figure shows the maize yield map resulted from relationship between salinity and total dry mass. Maize yield estimated higher in depression and its periphery. Yield collected from the farmers are closely related with estimated yield. Generally, yield from farmers field varied from 4 to 6 mt/ha which correlate well with yield map obtained from the simulation. Farmers express yield in average that could not be used to validate the results.
Figure 2 : (a) Maize yield map, (b) Potential maize yield class
Development of Methodologies for
Land Degradation Assessment Applied to
Land Use Planning in Thailand
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