In Spain, the potato occupies third place in annual consumption per head, after fresh vegetables and milk (Manual de Estadística Agraria 1986).

Transcription

1 Scientific registration nº :1491 Symposium nº :12 Presentacion : poster Soil evaluation in the cultivation of the potato in Granada, Spain Evaluation de l'aptitude des sols pour la culture de pomme de terre en Grenade, Espagne AGUILAR 1, IMENEZ M 2, MARTÍNEZ A 3, OLLERO 4 1. Dptº. Edafología, Universidad de Granada 2. Laboratorio Agrario Regional, Atarfe 3. Centro de Investigación y Desarrollo Agrario, Granada 4. Dpto Estadística, Universidad de Granada INTRODUCTION In Spain, the potato occupies third place in annual consumption per head, after fresh vegetables and milk (Manual de Estadística Agraria 1986). The highest producing countries are China, Poland, India and the United States. Spain has the largest surface area under cultivation in the European Union, although its production is lower than that of Germany, the Netherlands, France and the United Kingdom (Anuario de la Producción. FAO 1987). In Spain, Granada has the second largest potato-growing area under irrigation (5910 Ha) after León, but is the Province with the highest yield per hectare. Several factors influence the cultivation of the potato - its optimum temperature is between 15 and 18 degrees centigrade, it is suited to relatively cool night temperatures, although prolonged periods at below 10 degrees may reduce its yield (Caesar 1981). Kern (1979) indicates that yield depends on the interaction between soil, water and fertilisation. Water determines the effect and efficiency of mineral fertilisation. The optimum level of fertilisation with Nitrogen, Phosphorus and Potassium for the potato is closely linked to the physical composition and structure of the soil profile and the amount of rainfall. The pedological parameter with the greatest influence on yield is texture. In the present study we will compare the results obtained from three soil evaluation methods applied to potato cultivation in some areas in the Province of Granada dedicated to this crop. The evaluation methods used were: Riquier, Bramao and Cornet (1970), Buol, Couto and Sánchez (1975) and, the most specific to the potato, Manrique 1

2 and Vehara (1984). We seek to stablish the method that best suits, and this way we will be able to develope more surface dedicated to this culture. MATERIAL AND METHODS The crops subject to assessment were chosen from all potato farming regions in the Province of Granada. In these regions, 25 different profiles were taken, whose general characteristics are shown in Table 1. Their morphological description was carried out following the FAO guidelines for profile description (1977) and the analytical methodology was effected in accordance with the Soil Conservation Service (1972) and ISRIC (1987). EXPERIMENTAL RESULTS The objectives of "Fertility Capability Classification" (FCC) are basically the interpretation of soil profiles according to fertility, the management practices designed to modify it, and the prediction of the soil's limitations for agronomic uses. The main analytical results for the upper horizons, going down to 40 cms deep, are shown in Table 2. The results obtained by applying the FCC can be observed in Table 3. In view of the above results set out in Table 3, we can see that soils with a loamy texture (type L) predominate, with an average infiltration speed and good water retention capacity. Profiles 1 and 12 (type LR) are remarkable although we have to take into account some limitations for deeprooting crops as well as a lack of water due to the existence of a hard layer; In spite of having the same formula in this system, production is very different. Plot 1 produced 5940 Kg/Ha, whereas the plot 12 has a production of 24,570 Kg/Ha. This cannot, by any means, be explained by the different variety of potato used. Soils 2, 4, 10, 13, 19, 21, and 22 are also remarkable for their clayish texture which means that they have a low water infiltration speed and are therefore difficult to cultivate. When we compare the different production of each soil, we can see that it varies from 10, Kg/Ha for profile 22 to 35,000 Kg/Ha for profile 2. Again, this cannot be explained by the different varieties of potato involved. As a modifier, the basic carbonate character of all the soils studied, except profile 6, is remarkable. This basic reaction limits the use of phosphate rock-based fertilisers which are insoluble in water and should be avoided, and produces deficiencies in oligoelements, especially iron and zinc. Other modifiers of note are the salinity present in soils 5, 6, 11, 13, 15, 16, 18, 21, 22, 24 and 25 and the low potassium retention aptitude in profiles 11, 15, 20, 23, 24 and 25, as well as the low cation exchange capacity in profile 6. The "FCC" system is rather too basic for our study; it provides us with primary information which is quite similar for the majority of the soils studied, i.e. their basic character and their greater or lesser capacity for water retention. In most cases, only a few modifying factors exist which give rise to a clear differentiation between the plots of 2

3 land. It is, moreover, a qualitative method. It does not quantify the effects, and thus its purpose is to give a broad idea, but is not enough to evaluate soils for potato farming. We have used two quantitative indices; one of a general nature - Riquier, Bramao and Cornet (1970) and the other more specific to the potato, Manrique and Vehara (1984). The results obtained with Riquier, Bramao and Cornet's method are set out in Table 4. In view of the data showed at Table 4, and given that the crop is irrigated, on welldrained flat land, we may assume that humidity, drainage, effective depth, erosion and mineral reserves do not influence our study, and to a lesser extent, neither do salinity, toxicity and cation exchange capacity. On the other hand, the factors which do influence productivity are texture and structure, organic material content and carbonate content. When comparing the productivity index obtained by using this method with the productivity actually achieved, we are faced with the following facts: The system proposed by Riquier, Bramao and Cornet establishes 5 classes according to their productivity index as set out below: Class 1 Productivity index > 65 Class 2 " " between 34 and 65 Class 3 " " between 19 and 34 Class 4 " " between 7 and 19 Class 5 " " < 7 Only plot 1 can be considered as within Riquier's class 5, with a productivity index of 7.05, which corresponds to an extremely low production of Kg/Ha. There is no farm in the province of Granada which falls within Class 4. Only three farms, corresponding to profiles 19, 21 and 22 fall within Class 3, with productivity indices ranging from 19 to 34, and in these, the highest Riquier indices apparently correspond to the soils with lowest productivity. With regard to plot 22, it could be argued that this is due to it having a different variety (Espunta) which is true, but no for plots 29 and 21 which have the same variety (aerla). The majority of the farms studied fall within Riquier Class 2, and on comparing Riquier's indices with actual productivity, we see that there is no correlation between them. The same occurs with Riquier's class. We can therefore conclude that there is no correlation between Riquier's productivity index and actual potato production. The results obtained with Manrique and Vehara's methodology are set out in Table 5. The problem with applying this methodology lies in the assignation of the value of each index within a particular class. Only four levels are distinguished, 25, 50, 75 and, and so it is difficult to establish intermediate values. This means that the global indices obtained for the soils are fairly similar, soils 1, 6, 11, 13, 15, 21, 22 and 24 all being included in the poor categories, and soils 2, 3, 4, 5, 7, 8, 9, 10, 12, 14, 16, 17, 18, 19, 20, 23 and 25 in the normal or average categories. Given that the evaluation methods used did not correspond with our production facts, we proceeded to carry out a regression analysis in order to establish the best fit considering the soil characteristics. 3

4 By considering the soil parameters exclusively, we established various regression models that are significant and are shown in Table 6. In Table 6, we see how models 4 and 5 are those which best explain the variability in the data, with a fit of 62 and 66.5% respectively. According to these models, the soil parameters which have a significant influence on potato production are, in order of importance, the following: texture, the carbon/nitrogen relationship, nitrogen, ph, calcium carbonate content, organic carbon, sodium, cation exchange capacity and potassium, thus, loamy textures have the most positive influence on production. The organic carbon, carbonate, sodium and potassium content is equally positive, whilst this is not the case for the ph, the carbon/nitrogen relationship, cation exchange capacity and nitrogen content. Moreover, if we consider each variety independently, we can establish other regression models which fit the production data better. Thus, by considering the aerla and Red Pontiac varieties individually, for which we have sufficient data available, and grouping the other varieties together, we obtained the regression models which are shown in Table 7. By examining Table 7, we can see that some parameters cause a rise in production, such as texture, carbonates and potassium, whilst some cause a fall, such as the carbon/nitrogen relationship and ph. Others go in both directions, according to the particular variety, among which are organic carbon content, nitrogen content and cation exchange capacity. The relative contribution of each factor is determined by the regression equation coefficients, which would be, in mean values: 8.8txt C/N CO3Ca- 0,91 ph Å 11N Å 1.02 C.O. + 1Na + 0.9K Å 0.05 CIC, and thus the real contribution of each factor to production would be, in order of importance: txt > ph > C/N > CaCO3 > C.O. > N > CIC > K > Na. These results coincide with those of Kern (1979) and Borin (1987), i.e. that texture determines the best conditions for the crop and loamy texture, in particular, suits the characteristics of potato farming best. As a final conclusion, the study leads us to regard the quantitative methods studied with certain reservations, as well as to remark the influence of the crop variety, which may cause the most considerable differences, to the extent that it maynot-be possible to compare results between different varieties of potato. 4

5 Table 1. General characteristics of the studied profils. Profil location soil type texture structure Variety yield nº 1 Deifontes 1 Eutric leptosol silty loam granular aerla Deifontes 2 Calcaric fluvisol silty clay loam subangular blocky Red Pontiac ' Deifontes 3 Calcaric fluvisol clayey loam subangular blocky aerla Red Pontiac ' Deifontes 4 Calcaric cambisol silty loam subangular blocky aerla Red Pontiac Dúrcal 1 Calcaric cambisol loamy subangular blocky Turia Dúrcal 2 Rhodic Nitisol Sandy loam subangular blocky aerla clayey prismatic 7 Dúrcal 3 Petric calcisol silty loam crumby Red Pontiac Dúrcal 4 Haplic calcisol silty loam subangular blocky Draga Baza 1 Calcaric regosol loamy subangular blocky Quenebec Baza 2 Gleyc-calcaric silty clay subangular blocky Quenebec regosol 11 Baza 3 Haplic calcisol clayey loam subangular blocky Quenebec Baza 4 Petric calcisol loamy subangular blocky Red Pontiac Santa Fé Vertic calcaric fluvisol silty clay subangular blocky aerla ' Guadix Calcaric fluvisol sandy loam subangular blocky aerla R. Pontiac Huétor Vega Calcaric fluvisol loamy subangular blocky aerla Cájar 1 Calcaric fluvisol loamy subangular blocky Red Pontiac Cájar 2 Calcaric fluvisol loamy subangular blocky Red Pontiac ' Cájar 3 Calcaric fluvisol loamy subangular blocky aerla R. Pontiac Pinos Puente Calcaric fluvisol silty clay subangular blocky aerla Chauchina Calcaric fluvisol loamy subangular blocky aerla Casanueva Calcaric fluvisol loamy subangular blocky aerla

6 22 Loja 1 Haplic calcisol clayey subangular blocky Espunta Loja 2 Chromic cambisol sandy loam subangular blocky Espunta Motril 1 Cumulic anthrosol sandy loam subangular blocky Claustra Motril 2 Calcaric fluvisol loamy subangular blocky Claustra

7 Table 2. Main Analytical data for the upper 40cms Profile org. c% PH (water) CaC0 3 % Salinity s/m N 2 % P Pmm K 2 0 s/m Na 2 0 s/m C.I.C s/m C/N ,37 3' ' ' '

8 TABLE 3. Fertility classification System Profile Nº Classification Profile Nº Classification 1 L R - b 14 S b n 2 C - b 15 LL bks 3 L b 16 L b s 4 C C b 17 L b 5 L b s 18 LL b s 6 L C s e h 19 C b 7 L b 20 LL b k 8 L b 21 C b s 9 L b 22 C b s 10 C C bv 23 S b k 11 Lb ks 24 S L b k s 12 L R b 25 L L b k s 13 C b s 8

9 TABLE 4. Results obtained by Riquier. Bramao and Cornet Sys Humidity Available Drainage Effective Depth Texture And Structure Degree Of Saturation Organic Material Exchange Capacity B Mineral Reserves B Salinity Toxicity Carbonates Erosion Temperature Mechanisation Class A S 1 Productivity Index 1 P 2 20 T 6a 80 N A 1 M 2c E 5 C 5 80 slight Maize Class B P 5 T 6b N A 1 M 2c S 1 E 5 C 5 80 Nil Maize Class A 46, P 6 T 6b N A 1 M 2C S 1 E 5 C 5 80 Nil Maize Class A 41, P 6 T 6b N 6 80 O 2 A 1 M 2C S 1 E 5 C 4 slight Maize Class A 47, P 6 T 7 N 6 80 O 3 A 1 M 2C S 1 E 5 C 4 Nil Maize Class A 58, D 3a P 5 T 6b N A 0 M 2b S 2 E 2 C 4 Nil Maize Class A 47, P 5 T 6B N A 1 M 2c S 1 E 5 C 4 Nil Maize Class A 52, P 6 T 6b N A 1 M 2c S 1 E 5 C 4 Nil Maize Class A 52, P 6 T 7 N A 1 M 2c S 1 E 5 C 5 80 Nil Maize Class A 51, Yield Kg/Ha Variety RP Turia RP Drag a Q 10 P 6 11 P 6 12 P 6 T 6b N 6 80 O 3 A 1 M 2c T 6b N A 1 M 2c T 7 N A 1 M 2c S 1 S 1 S 1 E 5 C 4 Nil Class A 52, E 5 C 4 Nil Maize Class A 47, E 5 C 5 80 Nil Maize Class A 51, Q Q RP 13 P 6 T 6a 80 N 6 80 O 2 A 1 M 2c S 2 E 5 C 4 Nil Maize Class A 37,

10 14 P 6 15 P 6 16 P 6 17 P 6 18 P 6 19 P 6 20 P 6 T 6b N 5 O 3 T 6a 80 N 5 O 3 T 7 N 5 O 3 T 7 N 6 80 O 3 T 7 N T 5A 50 N 6 80 O 2 T 7 N 6 O 2 A o 85 M 2a S 1 E 4 C 1 Nil Maize Class A 68, A 1 M 2c S 2 E 4 C 3 Nil Maize Class A 64, A 1 M 2c S 1 E 5 C 3 Nil Maize Class A 81, A 1 M 3c S 1 E 4 C 3 Nil Maize Class A 72, A 1 M 2c S 1 E 5 C 3 Nil Maize Class A 64, A 1 M 3c S 1 E 4 C 4 Nil Maize Class A 29, A 1 M 2c S 1 E 5 C 4 Nil Maize Class A 52, RP RP 21 P 6 22 D 3a P 6 23 P 6 24 P 6 25 P 6 T 5a 50 N 6 80 O 2 T 5b 80 N 6 80 O 2 T 6b N 6 80 O 2 T 6b N 5 O 3 T 7 N 5 O 2 A 1 M 3c S E 5 C 4 Nil Maize Class A 24, A 1 M 3c S 2 E 5 C 4 Nil Maize Class A 34, A 1 M 2a S 1 E 5 C 4 Nil Maize Class A 42, A 1 M 2a S 3 80 E 4 C 1 Nil lemon Class A 58, grove 0 A 1 M 2c S 1 E 5 C 3 Nil lemon Class A 72, 36.5 grove 80 Esp Esp Clau str Clau str 10

11 TABLE 5. Results obtained by Manrique and Vehara System Water availability Oxigen availability Soil cultivation Temperature regimen Root development Soil acidity Salinity and alkalinity Nutrient availability Possibility erosion Global index Yield Kg/Ha , , , , , , , , , ,

12 TABLE 6. Models that explain the variability of the data Model Equation R 2 F ratio P value 1 Kg/m 2 = 6,557 txt -0,6779 ph 0,297 6,91 0,39% 2 Kg/m 2 = 6,267 txt +0,074 p -0,157 C/N - 6,379N 0,429 6,26 0,13% 3 Kg/m 2 = 6,322 txt + 0,088 P-0,206 C/N +0,0218 CO = 3-6,185 N - 0,778 ph 0,549 6,68 0,04% 4 Kg/m 2 = 8,803 txt -0,296 C/N -14,74 N - 1,089 ph +0,0276 CO = 3 +1,2296 Org.con +1,021 0,620 7,53 0,01% Na 5 Kg/m 2 = 8,47 txt -0,28 C/N +0,028 CO = 3-0,9 ph +0,955 Na -11 N +0,99 Org.con- 0,665 7, % 0,051 CIC +0,897 K T Kg/m 2 = 8,64 txt -0,28C/N + 0,028 CO = 3-0,91 ph -10,92N +1Na + 1,02 Org. con +0,94K -0,059 CIC +0,012Mg -0,007P -0,006 Salinity 0,605 4,59 0,28% TABLE 7. Models that explain the variability of the data grouped by varieties Model Equation R 2 F ratio P value aerla Variety 0, ,03% Kg/m 2 = 0,123P + 9,86txt - 0,168 C/N - 0,65pH - 10,29N + 0,917K + 0,0118 CO = 3 + 0,0158 CIC - 0,845 Red Pontiac Variety 0,98 66,4 1,49% Kg/m 2 = 0,0497 CO = 3-4,292pH -0,357 C/N -0,678Mg +16,38N +0,186 CIC +34,693 4 Other varieties Kg/m 2 = 11,605txt +2,168K -1,55 Org.con +12,32N -0,114CIC +1,865pH -22,207 0, ,05% 12