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Can We Use Electrical Resistivity Tomography to Measure Root Zone Dynamics in Fields with Multiple Crops?
Available online at www.sciencedirect.com
Procedia Environmental Sciences 19 (2013) 403 – 410
Four Decades of Progress in Monitoring and Modeling of Processes in the Soil-PlantAtmosphere System: Applications and Challenges
Can we use electrical resistivity tomography to measure root zone dynamics in fields with multiple crops? S. Garréa,b*, I. Coteura, C. Wongleecharoenc, K. Hussainc, W. Omsunrarne, T. Kongkaewe, T. Hilgerc, J. Dielsa, J. Vanderborghta,d a
b
KULeuven, Earth and Environmental Sciences, Celestijnenlaan 200E, 3000 Leuven, Belgium Université de Liège, Gembloux Agro-Bio Tech, Passage des Déportés 2, 5030 Gembloux, Belgium c Universität Hohenheim, Faculty of agricultural sciences, 70593 Stuttgart, t Germany d e Kasetsart University, Department of Soil Science, Bangkok, Thailand
Abstract Contour hedgerow intercropping systems have been proposed to reduce run-off and control soil erosion on steep agricultural land. However, competition for water and nutrients between crops and associated hedgerows may reduce the overall performance of these systems. ERT measurements conducted in Thailand showed that the soils of our experimental plots were very heterogeneous both along the slope as with depth. This observation highlighted some constraints of the ERT method for soil moisture monitoring in the field. Nevertheless, the data indeed revealed contrasting water depletion patterns under monocropping and intercropping systems, which could also be related to plant parameters. © 2013 The Authors. Authors. Published Publishedby byElsevier ElsevierB.V B.V. ofof thethe conference Selection and/or and/or peer-review peer-reviewunder underresponsibility responsibilityofofthe theScientific ScientificCommittee Committee conference. Keywords: ERT, soil-root interactions, intercropping
1. Introduction The combination of steep slopes, highly erosive rainfall and shallow soils in many areas of the humid tropics makes on-site soil erosion and offf site sedimentation major concerns [1]. Contour hedgerow intercropping systems have been promoted in such areas, since they are highly effective in reducing
* Corresponding author. Tel.: +32(0)81622162. E-mail address: [email protected]
1878-0296 © 2013 The Authors. Published by Elsevier B.V Selection and/or peer-review under responsibility of the Scientific Committee of the Conference doi:10.1016/j.proenv.2013.06.046
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S. Garré et al. / Procedia Environmental Sciences 19 (2013) 403 – 410
erosion [2-4]. This agroforestry system involves planting hedgerows of perennial shrubs along the contour lines of a slope [4, 5]. Crops are produced in the alleys between the hedgerows. The shrubs are to be pruned regularly to prevent shading and if leguminous shrubs are used, the foliage can provide highprotein forage or nutrient-rich mulch. Next to the main advantage of reducing runoff and erosion, leguminous hedgerows also have a favorable effect on the soil fertility [6-9]. In addition to that, intercropping is a risk reduction strategy for farmers because of product diversification and less chance of yield loss by pests and diseases [1]. Some authors even mention the potential complementarity of the root systems of the different crops, resulting in a more efficient water and nutrient use [10]. Nevertheless, many authors also report negative effects of the hedgerow on the main crop [11-16]. These negative effects are mostly attributed to competition for nutrients and/or water and very often only based on comparison of growth and yield performance between monocropped and intercropped plots or fields. To get a more detailed understanding of the competition for water, 2- or 3-D monitoring of the water fluxes in the soil-plant-atmosphere system is necessary. Given the potentially high spatial variability of soil moisture in contour hedgerow intercropping systems on steep slopes, a measurement technique with a high spatial resolution, such as electrical resistivity tomography must be used. The objective of this research was to quantify patterns and strategies of water uptake in space and time of various crops in a contour hedgerow intercropping system with electrical resistivity tomography (ERT). 2. Material and methods 2.1. Field site The field site for this study was located in the Queen Sirikit Research Station, Ratchaburi Province, located in temperature and relative humidity zone 2 (16-38°C, 41-100% RH) according to Khedari, Sangprajak [17]. The soil of the field site is heterogeneous and ranges between an endoleptic Alisol (hyperdistric, endoskelletic) and hyperskelletic Leptosol (eutric, siltinovic) [18]. Four experimental plots of 4 m by 13 m (slope 18-20%) were used for this study (see also Fig. 1): Ba: Field plot with bare soil. T1: Field plot with only Maize (Zea mays L. cv. Pacific 999) (=monocropping) with application of conventional tillage techniques and fertilizer use. T4: Field plot with Maize rows intercropped with high value chilies (Capsicum annuum L. cv. Super Hot), application of minimum tillage and use of fertilizer. Jackbean (Canavalia ensiformis (L.) DC) is sown during the dry season as relay and catch crop substituting maize after harvest. In the plot, an additional perennial hedge of Leucaena leucocephala (Lam.) de Wit is planted between the rows. T6: As T4 but without fertilizer application. 2.2. Experimental design methodology The general approach followed in this research project to identify the optimal electrical resistivity tomography (ERT) survey design to study water fluxes in two different agricultural systems, is given in the following. First, a hydrological model was created approaching the soil, relief and climate conditions at a field site near Suan Phung, Ratchaburi Province, Thailand. Two cases were simulated: a field plot with only maize and one with contour hedgerow intercropping with Leucaena leucocephala L. The model was run for 130 d starting from maize sowing. Secondly, a pedo-physical relationship was used to convert
S. Garré et al. / Procedia Environmental Sciences 19 (2013) 403 – 410
the water content distribution of a few characteristic timeframes to a resistivity distribution. We used four pedo-physical relationships to assess the effect of using a deviating relationship on the assessment of the optimal survey design. Third, virtual ERT measurements were conducted by forward modeling using different measurement configurations and the simulated 2-D distribution of resistivities. We added noise to the simulated measurements equal to 1% of the resistivity value to approach real measurements, which are always prone to background and measurement noise. After that, the data were inverted using a regularization strength such that data were fitted within the noise level, and the resolution and sensitivity matrix were analyzed. Finally, we compared the original resistivity distributions with the inverted ones to obtain the model recovery. Measures for spatial variability as well as the resolution matrix, sensitivity matrix (based on the recovered resistivity maps) and model recovery (based on recovered water contents) were then used to judge the performance of the measurement arrays. The experimental design study is described in detail in Garré et al. (2012) [19].
Fig. 1. Overview of the cropping pattern Ba, T1 and T4: electrode locations, TDR probe locations (white = 0-0.25 m, grey = 0.200.45m), crops and planting distances.
2.3. Data acquisition The test site was equipped with a weather station. Soil moisture was continuously logged with TDR probes distributed over the plots in all replications at 0-0.25 m and 0.20-0.45m depth. The TDR probes were made of two rods (0.25 m long) with an inter-rod distance of 0.025 m. They were controlled by a TDR100 unit (Campbell Scientific Lt., UK). Soil moisture was obtained from the apparent dielectric [20]. From July 20th to July 29th 2011, we measured the soil temperature at 0.20, 0.30, 0.40, 0.50 and 0.60 m depth using ECH2O-TE sensors (Decagon devices, Inc., USA) in order to determine the damping depth of the soil, from which its thermal diffusivity was derived. Three additional temperature sensors were installed in the topsoil (z = -0.05cm) and collected data during this period: under maize, under chili and under Leucaena cover. The thermal diffusivity was then used together with superficial soil temperature to derive the soil temperature at all relevant depths and under the different crop covers [see 21 for more details]. The ERT measurements of this study took place during the growing season of the crops. Additionally, plant height, plant canopy, with a digital camera, and leaf area index (LAI), with the Sunscan Canopy Analyzer (Delta-T Devices, Ltd, UK) and a beam fraction sensor, were regularly measured. At the end of the growing season, the root distribution of maize and, if present, Leuceana, in treatments T1, T4 and T6 were observed in a trench at one side of the field plots touching the first plant in the row. We used the
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root counting method on a grid with square cells of 0.0025 m2 [22-24]. We also determined carbon isotopic ratios in the third youngest leaf from the top of the maize plant at 85 days after planting for 8 out of 16 plants of each plant row. For details about this procedure, see [25] ERT measurements were conducted using a ten-channel Syscal Pro resistivity meter (IRIS instruments, France). The electrodes were installed permanently in a line along the slope and inserted at three depths: 36 electrodes at 5 cm depth, nine at 25 cm and nine at 50 cm. The horizontal distance between electrodes at 5, 25 and 30 cm depth were 33, 132, and 132 cm, respectively. Following the results of the experimental design study, we carried out a combination m of dipole-dipole and Wenner measurements in each plot consisting of 1694 unique quadrupoles with a measurement time of approximately 1h per plot once per day. Temperature probes, TDR probes, and electrodes were inserted at 5, 25, 50 cm depth in a vertical wall of a calibration trench in an area of bare soil below field plot T4R3. Three soil horizons were identified in the calibration pit: an Ap (0-25 cm), a Bt (26-80 cm) and Cr (>80 cm). However, it must be noted that the transition between Ap and Bt was not clear and the depth also varied along the profile pit. Soil temperature, water content and resistance were logged twice a day. The temperature data were used to correct the conductivity measurements in the pit. The combination of soil moisture and conductivity data was used to adjust pedo-physical relationships for each soil horizon, which was then later applied to the ERT data. For more information on the field experiment, see Garré et al. (in press) [21]. 3. Results 3.1. Summary of the virtual experiment The aim of the virtual experiment was to optimize the experimental design of the ERT measurements or mono- and intercropping systems at the field site in Ban Bo Wi. The following is a very brief description of the results of the study published by Garré et al. (2012) [19]. First of all, the different electrode arrays produced very similar 1-D soil moisture profiles. Differences arise when analyzing the spatial patterns. Generally, arrays without deeper electrodes performed worse than those with additional electrodes at -0.25 m and -0.50m, but the gain was not always big. This was also clear from semiovariograms of soil moisture (see Fig. 2).
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Fig. 2. (adapted from Garré et al. 2012) [19] Semivariograms of the virtual and inverted water content at day 60 (a) for the monocropping (MC); (b) and the intercropping (IC) case. DipDip: dipole-dipole, PolDip: pole-dipole, WenDipDip: combination of Wenner and dipole-dipole array. All: including electrodes at -0.25 m and -0.50 m, OS: only surface electrodes.
S. Garré et al. / Procedia Environmental Sciences 19 (2013) 403 – 410
Standard deviations of water contents at a particular depth and semivariograms showed that the extent of the spatial variability is generally underestimated and smoothened by the ERT inversion, but the spatial structures remain present in the retrieved WC distributions. As mentioned by amongst others Vanderborght et al. (2005) [26], the main reason for this underestimation is probably the smoothnessconstrained inversion, causing strong contrasts to fade out after inversion. The array combining Wenner and dipole-dipole quadrupoles was best able to retrieve spatial variability. The standard deviations and consequently the sill of the semivariogram were underestimated more strongly by the Wenner and the pole-dipole array than by the others. Looking at the resolution radius, the Wenner and pole-dipole array showed a stronger increase of resolution radius with depth than the others, explaining the underestimation of the variability. These results show that it is important to estimate the spatially varying resolution for a measurement array. In general, the study showed that ERT should be able to retrieve spatial structure in soil moisture distributions under field conditions in fields with multiple crops and thus observe the effect of cropping systems on soil moisture. 3.2. Field experiment Fig. 3 shows the ERT-derived soil moisture distribution on August 6th, 2011, which is used in this figure as a reference timeframe, t0. Two distinct zones emerge, which is probably the distinction between soil material and bedrock. The water content in this second zone (dark blue hue) should not be interpreted, since we could not measure a pedo-physical relationship in the bedrock. The volumetric water content in the soil at t0 ranges between 20 and 30%. Soil moisture changes from the reference timeframe t0 are also shown in Fig. 3.
Fig. 3. Absolute soil moisture distribution at t0 = August 6th and soil moisture difference between t0 and August 27 th for each treatment (adapted from Garré et al. 2013, in press).
Between the reference timeframe t0 and August 27th only one small rain event occurred. The soil moisture difference shows us the effect of this long drying phase on soil moisture changes in the four cropping patterns (with red colors showing water depletion relative to t0). Clear differences between the cropping systems emerge. The bare soil has only minor soil moisture depletion and very surficial, whereas the soil dries out much deeper in the maize monocropped plot. The intercropping plots have more heterogeneous patterns as compared to the monocropped plot. Fig. 4 shows the average water depletion [z = (0,-0.8) m] under each crop type during the growing season. Purple hues indicate water depletion, whereas blue hues indicate soil moisture increase. The monocropped field used more water than the intercropped fields and was more homogeneous along the slope. In general, maize was the largest water consumer, followed by Leucaena. Water depletion under maize rows close to the Leucaena hedge was mostly larger than further away. In addition, dry matter,
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C in maize grains point out that that the maize plants near to the hedges were 13 C [for data, see 25]. This, in combination with root profile observations (not shown), points to the fact that Leucaena roots spread laterally and that they probably compete for water and nutrients with maize in the maize root zone. The chili used markedly less water than the other crops. There is also a depletion of water in the bedrock under the Leucaena and some maize plants. Inspection of the bedrock in the root profile pit revealed that some roots populate the small cracks in the weathering bedrock, which could indicate that at least some roots allow the plant to extract water from the bedrock. Finally, the unfertilized intercropping plot T6 has much less water depletion than the fertilized intercropping plot.
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Fig. 4. Average water depletion per row as measured with electrical resistivity tomography in 3 field plots (T1, T4 and T6) between z = 0 m and z = -0.8 m. Crop type is indicated for each row at the left side of the graph.
As shown in the virtual experiment, experimental semivariograms of the soil moisture distributions demonstrate whether and which systematic spatial structures are present in the water content distribution due to the agricultural treatment. Because spatial variation in absolute moisture data is influenced by bedrock depth, we focus instead on the soil moisture differences relative to the August 6 base dataset. The experimental semivariograms for each treatment (see Fig.10 Garré et al. (2013)) [21] show clear differences between the four cropping patterns, which is due to the crop choice and alignment. The semivariance of the bare soil plot is markedly lower than the others. The highest semivariance is found in the fertilized intercropped plot T4R3. In general, we successfully monitored soil moisture distributions and changes in the field under different cropping patterns with electrical resistivity tomography (ERT). In addition, the distribution of electrical conductivities showed the existence of two main structural entities in the profiles and revealed the heterogeneity of the soil depth across the research plots. The soil moisture status of the different treatments and the change thereof was measured and whereas the average soil moisture change of the treatments was similar, clear differences in depletion patterns were recognized making use of the spatial information provide by the ERT. This knowledge was crucial to understand the validity of the applied calibration relationship to convert measured bulk electrical conductivity to soil water content. It became clear that at places with shallow soil, we did not have a calibration relationship for the weathered bedrock and that in these areas, absolute values had to be used with caution. However, even though quantitative information about this area lacked, changes could be registered, which would have been impossible with conventional soil moisture sensors.
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4. Conclusions Electrical resistivity tomography allowed us to investigate spatial and temporal patterns of soil moisture dynamics resulting from different cropping systems. By comparing the water depletion under distinct crop types, we showed that the depth of depletion of the chili culture was much less than that of maize and Leucaena. Also local differences in magnitude of depletion were visible, notably a difference between fertilized and unfertilized treatments. Measurement of increased depletion under the maize rows close to the hedges pointed towards competition for water between the two plants, which was supported by the observation of the presences of roots of both plants in this region. Similar conclusions could be 13 . Finally, experimental semivariograms revealed patterns in the variability of soil moisture and soil moisture changes. This technique showed that variability of the measured soil moisture was largely influenced by the soil heterogeneity. This kind of spatial analysis extracts very valuable information from ERT data, which is unavailable when using point measurements of soil moisture. Combination with other plant productivity measures reveals information about nutrient and water use of the crops. Acknowledgements We would like to thank the KULeuven to fund this OT project and the colleagues of the University of Hohenheim and Kasetsart University for their support during field experiments and discussions. We wish to express our profound gratitude to prof. Thanuchai Kongkaew, who passed away on February 16 th, 2012 at the end of the experimental phase of the project. Thanks to his perseverance, we were able to conduct this study at the research site in Ratchaburi. References [1] Craswell ET, et al., Agroforestry in the management of sloping lands in Asia and the Pacific. Agroforestry Systems 1997;38:121-137. [2] Lal R. Agroforestry systems and soil surface management of a tropical alfisol. Agroforestry Systems 1989;8:97-111. [3] Morgan RPC. Soil Erosion and Conservation. Wiley-Blackwell; 2004. [4] Sun H, Tang Y, Xie J. Contour hedgerow intercropping in the mountains of China: a review. Agroforestry Systems 2008;73:6576. [5] Tang Y. Manual on contour hedgerow intercropping technology, ICIMOD, Editor 2000: Katmandu, Nepal. p. 31. [6] Högberg P., Kvarnström M. Nitrogen fixation by the woody legumeLeucaena leucocephala in Tanzania. Plant and Soil 1982;66:21-28. [7] Imo M., Timmer VR. Vector competition analysis of a Leucaena-maize alley cropping system in western Kenya. Forest Ecology and Management 2000;126:255-268. [8] Hairiah K, Van Noordwijk M, Cadisch G. Quantification of biological N2 fixation of hedgerow trees in Northern Lampung. NJAS - Wageningen Journal of Life Sciences 2000b;48:47-59. [9] Danso SKA, Bowen GD, Sanginga N. Biological nitrogen fixation in trees in agro-ecosystems. Plant and Soil 1992;141:177196. [10] Narain P, et al. Water balance and water use efficiency of different land uses in western Himalayan valley region. Agricultural Water Management 1998;37:225-240. [11] Aerts R, Boot R, van der Aart P. The relation between above- and belowground biomass allocation patterns and competitive ability. Oecologia 1991;87:551-559. [12] Agus F, Cassel DK, Garrity DP. Soil-water and soil physical properties under contour hedgerow systems on sloping oxisols. Soil and Tillage Research 1997;40:185-199. [13] Dercon G, et al. Spatial variability in crop response under contour hedgerow systems in the Andes region of Ecuador. Soil and Tillage Research 2006;86:15-26.
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[14] Hairiah K, Van Noordwijk M, Cadisch G. Crop yield, C and N balance of three types of cropping systems on an Ultisol in Northern Lampung. NJAS - Wageningen Journal of Life Sciences 2000a;48:3-17. [15] Pansak W, Dercon G, Hilger T, Kongkaew T, Cadisch G. 13C isotopic discrimination: A starting point for new insights in competition for nitrogen and water under contour hedgerow systems in tropical mountainous regions. Plant and Soil 2007; 298:175-189. [16] Pansak W, et al. Changes in the relationship between soil erosion and N loss pathways after establishing soil conservation systems in uplands of Northeast Thailand. Agriculture, Ecosystems & Environment 2008; 128:167-176. [17] Khedari J, Sangprajak A, Hirunlabh J. Thailand climatic zones. Renewable Energy 2002;25:267-280. [18] FAO/ISRIC/ISSS, World reference base for soil resources World Soil Resources Reports. Vol. 84. 1998, Rome: International Society of Soil Science. [19] Garré S, et al. Evaluating experimental design of ERT for soil moisture monitoring in contour hedgerow intercropping systems Vadose Zone Journal 2012;11:4. [20] Topp GC, Davis JL, Annan AP. Electromagnetic determination of soil water content: Measurements in coaxial transmission lines. Water Resour. Res. 1980;16:574-582. [21] Garré, S., et al., Non-invasive monitoring of soil water dynamics in mixed cropping systems: A case-study in Ratchaburi province, Thailand. Vadose Zone Journal 2013, in press. [22] Bohm W. Methods of Studying Root Systems. Springer-Verlag; 1979. [23] Tardieu F. Analysis of the spatial variability of maize root density. Plant and Soil 1988;107:267-272. [24] Tardieu F., Manichon H. Caractérisation en tant que capteur d'eau de l'enracinement du maïs en parcelle cultivée. Une méthode d'étude de la répartition verticale et horizontale des racines. Agronomie 1986;6:345-354. [25] Hussain K, et al. Carbon isotopic discrimination and ERT imaging: New insights for understanding competition in polyculture systems. in preparation 2013. [26] Vanderborght J, Kemna A, Hardelauf H, Vereecken H. Potential of electrical resistivity tomography to infer aquifer transport characteristics from tracer studies: A synthetic case study. Water Resour. Res. 2005; 41: W06013.
Procedia Environmental Sciences 19 (2013) 403 – 410
Four Decades of Progress in Monitoring and Modeling of Processes in the Soil-PlantAtmosphere System: Applications and Challenges
Can we use electrical resistivity tomography to measure root zone dynamics in fields with multiple crops? S. Garréa,b*, I. Coteura, C. Wongleecharoenc, K. Hussainc, W. Omsunrarne, T. Kongkaewe, T. Hilgerc, J. Dielsa, J. Vanderborghta,d a
b
KULeuven, Earth and Environmental Sciences, Celestijnenlaan 200E, 3000 Leuven, Belgium Université de Liège, Gembloux Agro-Bio Tech, Passage des Déportés 2, 5030 Gembloux, Belgium c Universität Hohenheim, Faculty of agricultural sciences, 70593 Stuttgart, t Germany d e Kasetsart University, Department of Soil Science, Bangkok, Thailand
Abstract Contour hedgerow intercropping systems have been proposed to reduce run-off and control soil erosion on steep agricultural land. However, competition for water and nutrients between crops and associated hedgerows may reduce the overall performance of these systems. ERT measurements conducted in Thailand showed that the soils of our experimental plots were very heterogeneous both along the slope as with depth. This observation highlighted some constraints of the ERT method for soil moisture monitoring in the field. Nevertheless, the data indeed revealed contrasting water depletion patterns under monocropping and intercropping systems, which could also be related to plant parameters. © 2013 The Authors. Authors. Published Publishedby byElsevier ElsevierB.V B.V. ofof thethe conference Selection and/or and/or peer-review peer-reviewunder underresponsibility responsibilityofofthe theScientific ScientificCommittee Committee conference. Keywords: ERT, soil-root interactions, intercropping
1. Introduction The combination of steep slopes, highly erosive rainfall and shallow soils in many areas of the humid tropics makes on-site soil erosion and offf site sedimentation major concerns [1]. Contour hedgerow intercropping systems have been promoted in such areas, since they are highly effective in reducing
* Corresponding author. Tel.: +32(0)81622162. E-mail address: [email protected]
1878-0296 © 2013 The Authors. Published by Elsevier B.V Selection and/or peer-review under responsibility of the Scientific Committee of the Conference doi:10.1016/j.proenv.2013.06.046
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S. Garré et al. / Procedia Environmental Sciences 19 (2013) 403 – 410
erosion [2-4]. This agroforestry system involves planting hedgerows of perennial shrubs along the contour lines of a slope [4, 5]. Crops are produced in the alleys between the hedgerows. The shrubs are to be pruned regularly to prevent shading and if leguminous shrubs are used, the foliage can provide highprotein forage or nutrient-rich mulch. Next to the main advantage of reducing runoff and erosion, leguminous hedgerows also have a favorable effect on the soil fertility [6-9]. In addition to that, intercropping is a risk reduction strategy for farmers because of product diversification and less chance of yield loss by pests and diseases [1]. Some authors even mention the potential complementarity of the root systems of the different crops, resulting in a more efficient water and nutrient use [10]. Nevertheless, many authors also report negative effects of the hedgerow on the main crop [11-16]. These negative effects are mostly attributed to competition for nutrients and/or water and very often only based on comparison of growth and yield performance between monocropped and intercropped plots or fields. To get a more detailed understanding of the competition for water, 2- or 3-D monitoring of the water fluxes in the soil-plant-atmosphere system is necessary. Given the potentially high spatial variability of soil moisture in contour hedgerow intercropping systems on steep slopes, a measurement technique with a high spatial resolution, such as electrical resistivity tomography must be used. The objective of this research was to quantify patterns and strategies of water uptake in space and time of various crops in a contour hedgerow intercropping system with electrical resistivity tomography (ERT). 2. Material and methods 2.1. Field site The field site for this study was located in the Queen Sirikit Research Station, Ratchaburi Province, located in temperature and relative humidity zone 2 (16-38°C, 41-100% RH) according to Khedari, Sangprajak [17]. The soil of the field site is heterogeneous and ranges between an endoleptic Alisol (hyperdistric, endoskelletic) and hyperskelletic Leptosol (eutric, siltinovic) [18]. Four experimental plots of 4 m by 13 m (slope 18-20%) were used for this study (see also Fig. 1): Ba: Field plot with bare soil. T1: Field plot with only Maize (Zea mays L. cv. Pacific 999) (=monocropping) with application of conventional tillage techniques and fertilizer use. T4: Field plot with Maize rows intercropped with high value chilies (Capsicum annuum L. cv. Super Hot), application of minimum tillage and use of fertilizer. Jackbean (Canavalia ensiformis (L.) DC) is sown during the dry season as relay and catch crop substituting maize after harvest. In the plot, an additional perennial hedge of Leucaena leucocephala (Lam.) de Wit is planted between the rows. T6: As T4 but without fertilizer application. 2.2. Experimental design methodology The general approach followed in this research project to identify the optimal electrical resistivity tomography (ERT) survey design to study water fluxes in two different agricultural systems, is given in the following. First, a hydrological model was created approaching the soil, relief and climate conditions at a field site near Suan Phung, Ratchaburi Province, Thailand. Two cases were simulated: a field plot with only maize and one with contour hedgerow intercropping with Leucaena leucocephala L. The model was run for 130 d starting from maize sowing. Secondly, a pedo-physical relationship was used to convert
S. Garré et al. / Procedia Environmental Sciences 19 (2013) 403 – 410
the water content distribution of a few characteristic timeframes to a resistivity distribution. We used four pedo-physical relationships to assess the effect of using a deviating relationship on the assessment of the optimal survey design. Third, virtual ERT measurements were conducted by forward modeling using different measurement configurations and the simulated 2-D distribution of resistivities. We added noise to the simulated measurements equal to 1% of the resistivity value to approach real measurements, which are always prone to background and measurement noise. After that, the data were inverted using a regularization strength such that data were fitted within the noise level, and the resolution and sensitivity matrix were analyzed. Finally, we compared the original resistivity distributions with the inverted ones to obtain the model recovery. Measures for spatial variability as well as the resolution matrix, sensitivity matrix (based on the recovered resistivity maps) and model recovery (based on recovered water contents) were then used to judge the performance of the measurement arrays. The experimental design study is described in detail in Garré et al. (2012) [19].
Fig. 1. Overview of the cropping pattern Ba, T1 and T4: electrode locations, TDR probe locations (white = 0-0.25 m, grey = 0.200.45m), crops and planting distances.
2.3. Data acquisition The test site was equipped with a weather station. Soil moisture was continuously logged with TDR probes distributed over the plots in all replications at 0-0.25 m and 0.20-0.45m depth. The TDR probes were made of two rods (0.25 m long) with an inter-rod distance of 0.025 m. They were controlled by a TDR100 unit (Campbell Scientific Lt., UK). Soil moisture was obtained from the apparent dielectric [20]. From July 20th to July 29th 2011, we measured the soil temperature at 0.20, 0.30, 0.40, 0.50 and 0.60 m depth using ECH2O-TE sensors (Decagon devices, Inc., USA) in order to determine the damping depth of the soil, from which its thermal diffusivity was derived. Three additional temperature sensors were installed in the topsoil (z = -0.05cm) and collected data during this period: under maize, under chili and under Leucaena cover. The thermal diffusivity was then used together with superficial soil temperature to derive the soil temperature at all relevant depths and under the different crop covers [see 21 for more details]. The ERT measurements of this study took place during the growing season of the crops. Additionally, plant height, plant canopy, with a digital camera, and leaf area index (LAI), with the Sunscan Canopy Analyzer (Delta-T Devices, Ltd, UK) and a beam fraction sensor, were regularly measured. At the end of the growing season, the root distribution of maize and, if present, Leuceana, in treatments T1, T4 and T6 were observed in a trench at one side of the field plots touching the first plant in the row. We used the
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root counting method on a grid with square cells of 0.0025 m2 [22-24]. We also determined carbon isotopic ratios in the third youngest leaf from the top of the maize plant at 85 days after planting for 8 out of 16 plants of each plant row. For details about this procedure, see [25] ERT measurements were conducted using a ten-channel Syscal Pro resistivity meter (IRIS instruments, France). The electrodes were installed permanently in a line along the slope and inserted at three depths: 36 electrodes at 5 cm depth, nine at 25 cm and nine at 50 cm. The horizontal distance between electrodes at 5, 25 and 30 cm depth were 33, 132, and 132 cm, respectively. Following the results of the experimental design study, we carried out a combination m of dipole-dipole and Wenner measurements in each plot consisting of 1694 unique quadrupoles with a measurement time of approximately 1h per plot once per day. Temperature probes, TDR probes, and electrodes were inserted at 5, 25, 50 cm depth in a vertical wall of a calibration trench in an area of bare soil below field plot T4R3. Three soil horizons were identified in the calibration pit: an Ap (0-25 cm), a Bt (26-80 cm) and Cr (>80 cm). However, it must be noted that the transition between Ap and Bt was not clear and the depth also varied along the profile pit. Soil temperature, water content and resistance were logged twice a day. The temperature data were used to correct the conductivity measurements in the pit. The combination of soil moisture and conductivity data was used to adjust pedo-physical relationships for each soil horizon, which was then later applied to the ERT data. For more information on the field experiment, see Garré et al. (in press) [21]. 3. Results 3.1. Summary of the virtual experiment The aim of the virtual experiment was to optimize the experimental design of the ERT measurements or mono- and intercropping systems at the field site in Ban Bo Wi. The following is a very brief description of the results of the study published by Garré et al. (2012) [19]. First of all, the different electrode arrays produced very similar 1-D soil moisture profiles. Differences arise when analyzing the spatial patterns. Generally, arrays without deeper electrodes performed worse than those with additional electrodes at -0.25 m and -0.50m, but the gain was not always big. This was also clear from semiovariograms of soil moisture (see Fig. 2).
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Fig. 2. (adapted from Garré et al. 2012) [19] Semivariograms of the virtual and inverted water content at day 60 (a) for the monocropping (MC); (b) and the intercropping (IC) case. DipDip: dipole-dipole, PolDip: pole-dipole, WenDipDip: combination of Wenner and dipole-dipole array. All: including electrodes at -0.25 m and -0.50 m, OS: only surface electrodes.
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Standard deviations of water contents at a particular depth and semivariograms showed that the extent of the spatial variability is generally underestimated and smoothened by the ERT inversion, but the spatial structures remain present in the retrieved WC distributions. As mentioned by amongst others Vanderborght et al. (2005) [26], the main reason for this underestimation is probably the smoothnessconstrained inversion, causing strong contrasts to fade out after inversion. The array combining Wenner and dipole-dipole quadrupoles was best able to retrieve spatial variability. The standard deviations and consequently the sill of the semivariogram were underestimated more strongly by the Wenner and the pole-dipole array than by the others. Looking at the resolution radius, the Wenner and pole-dipole array showed a stronger increase of resolution radius with depth than the others, explaining the underestimation of the variability. These results show that it is important to estimate the spatially varying resolution for a measurement array. In general, the study showed that ERT should be able to retrieve spatial structure in soil moisture distributions under field conditions in fields with multiple crops and thus observe the effect of cropping systems on soil moisture. 3.2. Field experiment Fig. 3 shows the ERT-derived soil moisture distribution on August 6th, 2011, which is used in this figure as a reference timeframe, t0. Two distinct zones emerge, which is probably the distinction between soil material and bedrock. The water content in this second zone (dark blue hue) should not be interpreted, since we could not measure a pedo-physical relationship in the bedrock. The volumetric water content in the soil at t0 ranges between 20 and 30%. Soil moisture changes from the reference timeframe t0 are also shown in Fig. 3.
Fig. 3. Absolute soil moisture distribution at t0 = August 6th and soil moisture difference between t0 and August 27 th for each treatment (adapted from Garré et al. 2013, in press).
Between the reference timeframe t0 and August 27th only one small rain event occurred. The soil moisture difference shows us the effect of this long drying phase on soil moisture changes in the four cropping patterns (with red colors showing water depletion relative to t0). Clear differences between the cropping systems emerge. The bare soil has only minor soil moisture depletion and very surficial, whereas the soil dries out much deeper in the maize monocropped plot. The intercropping plots have more heterogeneous patterns as compared to the monocropped plot. Fig. 4 shows the average water depletion [z = (0,-0.8) m] under each crop type during the growing season. Purple hues indicate water depletion, whereas blue hues indicate soil moisture increase. The monocropped field used more water than the intercropped fields and was more homogeneous along the slope. In general, maize was the largest water consumer, followed by Leucaena. Water depletion under maize rows close to the Leucaena hedge was mostly larger than further away. In addition, dry matter,
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C in maize grains point out that that the maize plants near to the hedges were 13 C [for data, see 25]. This, in combination with root profile observations (not shown), points to the fact that Leucaena roots spread laterally and that they probably compete for water and nutrients with maize in the maize root zone. The chili used markedly less water than the other crops. There is also a depletion of water in the bedrock under the Leucaena and some maize plants. Inspection of the bedrock in the root profile pit revealed that some roots populate the small cracks in the weathering bedrock, which could indicate that at least some roots allow the plant to extract water from the bedrock. Finally, the unfertilized intercropping plot T6 has much less water depletion than the fertilized intercropping plot.
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Fig. 4. Average water depletion per row as measured with electrical resistivity tomography in 3 field plots (T1, T4 and T6) between z = 0 m and z = -0.8 m. Crop type is indicated for each row at the left side of the graph.
As shown in the virtual experiment, experimental semivariograms of the soil moisture distributions demonstrate whether and which systematic spatial structures are present in the water content distribution due to the agricultural treatment. Because spatial variation in absolute moisture data is influenced by bedrock depth, we focus instead on the soil moisture differences relative to the August 6 base dataset. The experimental semivariograms for each treatment (see Fig.10 Garré et al. (2013)) [21] show clear differences between the four cropping patterns, which is due to the crop choice and alignment. The semivariance of the bare soil plot is markedly lower than the others. The highest semivariance is found in the fertilized intercropped plot T4R3. In general, we successfully monitored soil moisture distributions and changes in the field under different cropping patterns with electrical resistivity tomography (ERT). In addition, the distribution of electrical conductivities showed the existence of two main structural entities in the profiles and revealed the heterogeneity of the soil depth across the research plots. The soil moisture status of the different treatments and the change thereof was measured and whereas the average soil moisture change of the treatments was similar, clear differences in depletion patterns were recognized making use of the spatial information provide by the ERT. This knowledge was crucial to understand the validity of the applied calibration relationship to convert measured bulk electrical conductivity to soil water content. It became clear that at places with shallow soil, we did not have a calibration relationship for the weathered bedrock and that in these areas, absolute values had to be used with caution. However, even though quantitative information about this area lacked, changes could be registered, which would have been impossible with conventional soil moisture sensors.
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4. Conclusions Electrical resistivity tomography allowed us to investigate spatial and temporal patterns of soil moisture dynamics resulting from different cropping systems. By comparing the water depletion under distinct crop types, we showed that the depth of depletion of the chili culture was much less than that of maize and Leucaena. Also local differences in magnitude of depletion were visible, notably a difference between fertilized and unfertilized treatments. Measurement of increased depletion under the maize rows close to the hedges pointed towards competition for water between the two plants, which was supported by the observation of the presences of roots of both plants in this region. Similar conclusions could be 13 . Finally, experimental semivariograms revealed patterns in the variability of soil moisture and soil moisture changes. This technique showed that variability of the measured soil moisture was largely influenced by the soil heterogeneity. This kind of spatial analysis extracts very valuable information from ERT data, which is unavailable when using point measurements of soil moisture. Combination with other plant productivity measures reveals information about nutrient and water use of the crops. Acknowledgements We would like to thank the KULeuven to fund this OT project and the colleagues of the University of Hohenheim and Kasetsart University for their support during field experiments and discussions. We wish to express our profound gratitude to prof. Thanuchai Kongkaew, who passed away on February 16 th, 2012 at the end of the experimental phase of the project. Thanks to his perseverance, we were able to conduct this study at the research site in Ratchaburi. References [1] Craswell ET, et al., Agroforestry in the management of sloping lands in Asia and the Pacific. Agroforestry Systems 1997;38:121-137. [2] Lal R. Agroforestry systems and soil surface management of a tropical alfisol. Agroforestry Systems 1989;8:97-111. [3] Morgan RPC. Soil Erosion and Conservation. Wiley-Blackwell; 2004. [4] Sun H, Tang Y, Xie J. Contour hedgerow intercropping in the mountains of China: a review. Agroforestry Systems 2008;73:6576. [5] Tang Y. Manual on contour hedgerow intercropping technology, ICIMOD, Editor 2000: Katmandu, Nepal. p. 31. [6] Högberg P., Kvarnström M. Nitrogen fixation by the woody legumeLeucaena leucocephala in Tanzania. Plant and Soil 1982;66:21-28. [7] Imo M., Timmer VR. Vector competition analysis of a Leucaena-maize alley cropping system in western Kenya. Forest Ecology and Management 2000;126:255-268. [8] Hairiah K, Van Noordwijk M, Cadisch G. Quantification of biological N2 fixation of hedgerow trees in Northern Lampung. NJAS - Wageningen Journal of Life Sciences 2000b;48:47-59. [9] Danso SKA, Bowen GD, Sanginga N. Biological nitrogen fixation in trees in agro-ecosystems. Plant and Soil 1992;141:177196. [10] Narain P, et al. Water balance and water use efficiency of different land uses in western Himalayan valley region. Agricultural Water Management 1998;37:225-240. [11] Aerts R, Boot R, van der Aart P. The relation between above- and belowground biomass allocation patterns and competitive ability. Oecologia 1991;87:551-559. [12] Agus F, Cassel DK, Garrity DP. Soil-water and soil physical properties under contour hedgerow systems on sloping oxisols. Soil and Tillage Research 1997;40:185-199. [13] Dercon G, et al. Spatial variability in crop response under contour hedgerow systems in the Andes region of Ecuador. Soil and Tillage Research 2006;86:15-26.
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