In situ determination of slurry nutrient content by electrical conductivity

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1 Bioresource Technology 98 (7) In situ determination of slurry nutrient content by electrical conductivity G. Provolo *, L. Martínez-Suller Istituto di Ingegneria Agraria, Università degli Studi di Milano, Via Celoria Milan, Italy Available online August Abstract Land application of animal slurries has both agronomic and environmental implications. It can be supported by the quantification of available nutrients in the field. A prototype device for indirect measurement of the nutrient content of slurry based on electrical conductivity (EC) was calibrated on manure samples collected from farms with different livestock typologies. The resulting correlations between EC and nutrient contents of slurries from laboratory analyses have shown good agreement (r from.7 to.9) with total and ammoniacal nitrogen and, with some exception, Potassium, but failed to demonstrate any significant relationship with total phosphorous. The mean errors obtained using the device in field conditions for nitrogen content were always lower or equal to %, while the standard deviations were % for pig and calf slurries, and % for dairy cow slurry. The results obtained suggest that the equipment, provided the regression line used to convert EC readings to nutrient contents is related to the livestock typology under observation, can provide good support to practical slurry spreading, even though it does not reach an accuracy comparable to laboratory methods and does not give reliable information on phosphorus. Ó Elsevier Ltd. All rights reserved. Keywords: Manure management; Slurry; Nutrient; Conductivity; Quick analysis method. Introduction Animal manures contain economically viable amounts of plant nutrients (Araji et al., ; Scotford et al., 998a,b) that promote the use of those manures in agriculture as fertilizers. The use of manures in agriculture must be suited to soil characteristics and fit the requirements of crops in order to avoid applying excess quantities of nutrients that can lead to pollution problems (Provolo, ). Models are commonly used to estimate nutrient content of slurries based on standard coefficients of production. These are derived from several experiments and express an average value of nutrient content of manure as a function of production practices (Carton et al., 997). These methods have serious limitations in that they give an annual average value of nutrient content that can * Corresponding author. Tel.: +9 8; fax: address: giorgio.provolo@unimi.it (G. Provolo). URL: (G. Provolo). differ from the real value due to differences in the management (feeding, removal system, etc.) and the variation occurring during the year (feeding, climatic conditions, etc.). Although slurries can be analyzed chemically using laboratory methods, these analyses require a long time to complete and are not usually economical. Moreover, slurry composition can differ widely over the duration of spreading operations according to the slurry tank level, mixing system used and time from the end of mixing and the loading of the application machinery (Ndegwa et al., ). Different systems have been proposed to measure chemical and physical characteristics of slurries and to determine their nutrient content. The least complicated of these approaches are based on measuring the density of slurry and using its good correlation with some nutrients in manures. These approaches have showed positive results in the estimation of nitrogen and phosphorus, both in bovine and pig slurry (Piccinini and Bortone, 99; Zhu et al.,, ). 9-8/$ - see front matter Ó Elsevier Ltd. All rights reserved. doi:./j.biortech..7.8

2 G. Provolo, L. Martínez-Suller / Bioresource Technology 98 (7) Due to the emphasis on nitrogen emissions linked to environmental issues, including air pollution, most of the equipment for providing quick estimates of slurry nutrient content aims to determine this element. The measuring principles rely on rapid chemical reactions, as in the Agrosmeter and Quantofix devices (Tunney and Bertrand, 989); on detection of specific ions, as in selective ion methods (Price et al., ); or on the use of reagents to induce colour changes, as with colorimetric methods (Chescheir et al., 98; Van Kessel and Reeves, ). Other studies have recently used near infrared technology for nitrogen and potassium determination, with promising results (Millmier et al., ). No device has yet been widely adopted, due in some cases to prohibitively high purchase or maintenance cost, in addition to the complexity of operations and the delay in getting results. Another problem limiting most existing methods is the difficulty of obtaining a representative sample due to the heterogeneity of slurries. An alternative approach is based on electrical conductivity (EC) measurements, in which the electrical properties of manure related to its ionic concentration are measured. EC has been shown to be correlated to the content of the soluble nutrients (nitrogen and potassium) that are present in liquid manure. On the contrary EC has not shown good correlation with elements such as phosphorus, which are mainly linked to the solid fraction of manure and do not affect significantly the electrical properties of manures (Scotford et al., 998b; Stevens et al., 99). The objective of this work was to calibrate and assess a prototype sensor, developed on the EC principle, against the nutrient content of manures collected from farms with different livestock typologies (finisher pigs, farrowing sows, integrated farrow-to-finish pig, dairy cows and calves). The assessment aimed to evaluate the accuracy and the effectiveness of the device in practical conditions.. Methods.. The equipment The device (Fig. ) is capable of measuring the EC of the manure and displaying an estimation of the quantity of total Kjeldahl nitrogen (TKN), total ammoniacal nitrogen (TAN), total phosphorous (as P O ) and total potassium (as K O), expressed in Kg m contained in slurry. The device is composed of () a switchboard, which contains the electrical components and electronically stores correlation curves relating EC to nutrient content, and () a probe formed by two electrodes. When the probe is immersed in slurry, the electrical resistance between the electrodes decreases (i.e., electrical conductivity increases), in large part due to the ionic character of the slurry. The switchboard is able to sense this EC and translate it immediately into nutrient content (TKN, TAN, P O and K O) using the pre-programmed correlation curves. One of the advantages of this device is its ability to provide Fig.. The prototype device with handheld (left) and tanker (right) probe. an immediate estimation of the slurry composition without a need for another test, dilution or reagent. The probe can be used manually (to take measurements at different points, as in a slurry store, in an underfloor pit, etc.) or directly installed in a slurry tanker (to know the amount of fertilizing elements that are being distributed in every load) with the advantage, in this second case, of taking into account the agitation effect produced during the loading and unloading processes. In order to accommodate the variability in slurry compositions produced by different animal categories, and thus the correlation between EC and nutrient contents, the device requires a different calibration curve for each type of slurry... Correlation between EC and nutrient contents A total of slurry samples from different livestock typologies (8 from pig farms and 9 from cattle farms) on Italian farms were collected to establish correlations between the EC and the main nutrient elements TKN, TAN, P O and K O in animal slurry. All samples were homogeneously withdrawn from slurry storages during the agitation process. To provide reference nutrient contents, samples were analyzed in the laboratory for TKN, TAN, P O,K O and some physical chemical properties using standard methods (APHA, 998). EC and ph were determined potentiometrically in the raw slurries. Dry matter (DM) content was determined gravimetrically by oven-drying approximately ml of raw slurry at C. TKN was determined by the Kjeldahl method, while TAN was determined directly by steam distillation. Phosphorous was determined colorimetrically after reduction with ascorbic acid (APHA, 998; Zhu et al., ). Total potassium was determined, in the above digestion, by atomic absorption spectrophotometry. Data from laboratory analyses were analysed in order to obtain descriptive statistics using a spreadsheet (Microsof Excel Ò ). Linear regression analysis related to the nutrients

3 G. Provolo, L. Martínez-Suller / Bioresource Technology 98 (7) 7 (TKN, TAN, P O and K O) and the EC was conducted using SPSS Ò software. The results obtained were used to define the calibration curves for the equipment... Field test The device was tested in practical conditions, both manually, using a handheld probe and automatically, with the probe installed in a slurry tanker. For each test, the device was set with the calibration curves determined as described above and selected according to the livestock typology of the farm. The device was tested on different livestock farms ( calf rearing farms and six integrated farrow-to-finish pig farms) in the area of Brescia. In each farm, approximately l of slurry were collected in a bucket from the slurry storage tank and mixed manually. The handheld probe was then inserted into the slurry and the resulting estimates of TKN, TAN, P O and K O were recorded. A l sub-sample of slurry was then withdrawn, placed in a refrigerated bag and sent to the laboratory for analysis the same day. The samples were analysed in the laboratory within h of collection. In addition, a more limited number of tests were performed with the probe of the device installed in the rear door of a conventional m vacuum tanker, fitted with a splash plate. The equipment was programmed to record EC values every s during the operation of the tanker (loading, transport, spreading). The transition from one event to another was marked in order to identify the sequence of data according to the operation. These tests were carried out on three different farms (dairy cows, calf and pigs). The test carried out on the dairy farm followed normal spreading operations. The slurry was taken from two pits ( m each) that were mixed thoroughly before starting the operations. No further mixing was performed. The measures with the device were recorded at every load while a slurry sample from the storage pit was collected for laboratory analysis at the beginning of the operation (and every 9 min thereafter if the operations were not completed). Altogether, completed loads were monitored with the device, and seven samples were taken for laboratory analysis. The slurries at the other two farms were taken from the pits under slatted floors using the same slurry tanker as used on the dairy farm. As no mixing devices were available for these pits, the reference samples of slurry were obtained from the vacuum tanker during the unloading operation (one sample each load). The nutrient contents predicted by the equipment were obtained as the average of values recorded for min after the end of the loading operation. The nutrient contents estimated by the device were compared with the laboratory analyses of the slurry samples. For every nutrient the individual errors, calculated as differences between element concentrations predicted by the Table Mean (x), standard deviation ð^sþ and maximum and minimum values of the studied slurries Animal typology Pigs Cattle Finisher Farrowing sows Integrated farrow-to-finish Dairy cows Calves Samples 7 Property x ^s max min x ^s max min x ^s max min x ^s max min x ^s max min ph 7. ± ± ± ± ± Electrical conductivity. ± ± ± ± ± (ms cm ) Dry matter.99 ± ±..7.. ±..9.. ± ±... (g DM kg ) Total nitrogen.8 ± ±....9 ±.... ± ± (kg N m ) Total ammoniacal. ± ± ± ± ±...7 nitrogen (kg N m ) Percentage TAN 7% 88% % 77% 97% % 8% 9% % % % % 87% 9% 7% (%) of TKN Phosphorus (kg).9 ± ± ± ±.... ±..7.7 P O (m ) Potassium (kg).9 ±....8 ± ± ± ±.7.. KO (m )

4 8 G. Provolo, L. Martínez-Suller / Bioresource Technology 98 (7) device and reference concentrations determined by the laboratory analyses, were calculated. Mean error and standard deviation (absolute value and ratio to the average laboratory analysis value) were also calculated. TKN (kg.m - ) Finisher pigs Farrowing sows Farrow to finish pigs EC (ms.cm - ) TKN (kg.m - ) Dairy cows Calves EC (ms.cm - ) TAN (kg.m - ) TAN (kg.m - ) Finisher pigs Farrowing sows Farrow to finish pigs EC (ms.cm - ) Dairy cows Calves EC (ms.cm - ) Finisher pigs Farrowing sows Farrow to finish pigs P O (kg.m - ) P O (kg.m - ) Dairy cows EC (ms.cm - ) Calves EC (ms.cm - ) 8 7 K O (kg.m - ) K O (kg.m - ) Finisher pigs Farrowing sows Farrow to finish pigs Dairy cows Calves EC (ms.cm - ) EC (ms.cm - ) Fig.. Linear regressions between EC and nutrients for pig (left) and cattle (right) slurries.

5 G. Provolo, L. Martínez-Suller / Bioresource Technology 98 (7) 9. Results and discussion.. Correlation between EC and nutrient content The compositions of the samples of slurries, dependent upon livestock typology, are summarised in Table. Data on the DM amount and the TAN content as a percentage of TKN illustrate the variability in slurry composition among different animal typologies. While TAN in pig slurries was nearly 77% of TKN, this ratio was % in slurries from dairy cows and almost 9% in slurries from calves. The mean DM for dairy cow slurries was high (. kg DM m ), lower for the pig slurries (..99 kg DM m ) and significantly lower in slurries from calves (. kg DM m ). In addition, the large standard deviation in DM around the mean indicates how highly variable the slurries were within the same livestock typology. DM variations affect the amounts of nutrient elements that are contained in the slurries, but there are many other sources of variability that are reflected in the results. Comparative studies have shown significant variations in the characteristics (including nutrient content) of animal manure according to the animal category, but also to the feeding and management system used (Moral et al., ; Scotford et al., 998b). Because of the variability in slurry composition among different livestock typologies, slurries from each typology were studied individually. Linear determination coefficients (r ) for regressions between EC and the four nutrient elements varied as a function of livestock typology and also as a function of the parameter being correlated with EC as evidenced by the results obtained for TKN and TAN shown in Fig.. Previous experience has shown good correlation of EC with the nutrient content of slurries, and especially with TAN (Moral et al., ; Scotford et al., 998b). Stevens et al. (99), showed the ability of the EC to act as a feasible indicator for ionic species in slurries, such as ammonium or potassium. Table reports the correlations between EC and nutrient content for the different slurries studied. Coefficients of determination (r ) obtained for pig slurries were high in the correlation of EC and nitrogen (both TKN and TAN) with the regressions being highly significant. The highest values of r were observed in integrated farrowto-finish slurry types, with values of.899 and.9 for TKN and TAN, respectively. This result might be explained by the similar management and feeding strategy of these livestock typologies. EC and K O correlations for pig slurries were found to be significant as earlier reported by Stevens et al. (99), but not as high as those obtained for nitrogen. The results for farrowing sow and integrated farrow-to-finish pig slurries confirmed the trend, but in slurries from finisher pigs the correlation was not significant (r =.). As observed by Scotford et al. (998b) the content of K O and the degree to which it is correlated with EC is considerably variable between geographical locations. This observation may be due to the differences in feeding and management strategies. In agreement with other studies (Piccinini and Bortone, 99; Scotford et al., 998a,b), the correlation between EC and P O was not significant for any of the livestock typologies studied because EC is not a good indicator for organic-dependent parameters, such as phosphorous (Stevens et al., 99). In fact, P O is the parameter which shows the highest error in determination regardless of the method used to measure it (Zhu et al., ). Considering the wide range in DM content among the different slurries, it was not surprising that the agreement between EC and phosphorus content was poor. In relation to the correlation analysis carried out for the cattle slurry, it is clear that calf slurries have a trend similar to the pig slurries in that they share a good correlation between EC and the nitrogen parameters, fairly good correlation with K O, and no significant agreement with P O. This might be the effect of the feeding strategies adopted for those farms, which are based on liquid feed as is used by pig farms. Dairy cow slurries showed significant correlations between EC and nitrogen content, as well as for K Oand P O. The unexpected result for the latter parameter might be related to the high variation of dry matter content of these slurries. Table Regressions, coefficients of determination and standard error between EC and nutrient contents in slurries according to livestock typology Livestock Nutrient (kg m ) Equation r Std error Finisher pigs TKN. EC ***.9 TAN. EC +..7 ***. P O.8 EC *.9 K O. EC +.7. *. Farrowing sows TKN. EC ***. TAN. EC ***.8 P O.87 EC +.7. ns.78 K O. EC.87.7 ***.78 Integrated farrow-to-finish pigs TKN.79 EC..899 ***.8 TAN. EC.7.9 ***.98 P O.8 EC.7.8 ns.8 K O. EC.8.8 ***. Dairy cows TKN. EC.7.89 ***.7 TAN.9 EC.8.77 ***.89 P O.8 EC.8. ***. K O.88 EC.978. ***.789 Calves TKN.79 EC ***. TAN.77 EC ***.8 P O. EC +.. ns. K O.98 EC ***. ns: not significant; EC expressed as m S cm. * P.. *** P..

6 G. Provolo, L. Martínez-Suller / Bioresource Technology 98 (7).. Field test Fig. compares the results of laboratory analyses against predicted nutrient contents obtained from the device both in handheld mode and installed on a slurry tanker. The mean errors of slurry nutrient content determinations made using the device, as a function of the animal typology considered and using laboratory analyses as indicators, are shown in Table. The results obtained during the handheld probe tests show that for calf slurries the TKN measures have a quite high variability (%), showing a relevant effect of the farm for this animal typology. It should also be noted that the calf slurries analysed were very dilute, and therefore the nutrient contents were very low. Thus, even a relatively low absolute variation in the measurement of nutrient content resulted in a relatively high percentage of variation. In fact, the standard deviation for TAN predicted by the device was., which is not dissimilar to the accuracy of laboratory analyses (Bidlingmaier and Maile, 99). The results on nitrogen parameters related to pig slurries show a slightly different trend, with a lower standard deviation for TKN (7%), but a higher mean error ( %) and the opposite behaviour for TAN. A further component of variability, besides the one cited for calf slurries, is related to the feeding strategy used that is more variable in this typology of livestock. As expected, the error associated with determining P O was much higher for both slurry types than errors in determining other nutrients. In fact, this nutrient has shown very low and not statistically significant correlation coefficients. With regard to K O, the results obtained show errors that are limited (. kg m ±.) for farrow-to-finish pigs and confirm the results obtained in the regression analysis for this parameter. The outcomes of tests of the device installed on a slurry tanker confirm the results of the field tests using the handheld device, and of the regression analysis. The mean errors for nitrogen content (TKN and TAN) obtained in this test were always lower or equal to % and TKN device (kg.m - ) TAN device (kg.m - ) TKN lab analysis (kg.m - ) TAN lab analysis (kg.m - ) P O device (kg.m - ) K O device (kg.m - ) P O lab analysis (kg.m - ) K O lab analysis (kg.m - ) handheld probe - Calves slurry tanker probe - Farrow-to-finish pigs handheld probe - Farrow-to-finish pigs slurry tanker probe - Dairy cows slurry tanker probe - Calves Fig.. Nutrient content obtained with the prototype device in field test compared to laboratory analysis.

7 G. Provolo, L. Martínez-Suller / Bioresource Technology 98 (7) Table Mean errors and standard deviations of determinations (using the different probes) compared to laboratory analyses of slurry nutrient content Animal typology Calves Farrow-to-finish pigs Dairy cows Probe Handheld Installed on a slurry tanker Handheld Installed on a slurry tanker Installed on a slurry tanker Samples Property TKN TAN P O K O TKN TAN P O K O TKN TAN P O K O TKN TAN P O K O TKN TAN P O K O Mean error Absolute value (%) % % % % % % % % % % 78% 7% 8% % % % % % 8% % Standard deviation Absolute value (%) % % 7% % 9% % % % 7% % % % % % % % % % % % * Italics value = underestimation of nutrient content by the prototype device. the standard deviation was % for pig and calf slurries while for dairy cows is was %. The relatively high standard deviation in measures on slurries from dairy cows may be partially due to the methodology used for collecting the slurry samples, in which a single storage tank sample was taken as a standard to which samples from different loads of slurry were compared. Also the errors obtained on K O are below % with the same maximum value for the standard deviation. As in other tests, P O was poorly estimated. The mean errors show a slight underestimation of nutrient content by the prototype device in pig and calf slurries, confirming the previous results from using the device in handheld mode, while the use of the device on cattle slurries moderately overestimated the nutrient content, as compared to laboratory analyses. Inaccuracies arose for two reasons: firstly from equipment error in measuring the electrical conductivity of the slurries, and secondly from errors in estimating the different nutrient contents due to imperfect regression lines (i.e., calibration curves) used for the different nutrient elements. The mean errors obtained for the nutrient elements are a combination of error in measuring the conductivity and errors introduced when translating EC values to nutrient estimates using the calibration curves. Although significant linear regressions with high correlation coefficients were determined in many cases, the resulting calibration curves were, nevertheless, imperfect and contained some degree of variation. The latter are affected by the degree to which the slurry under test matches the characteristics of those used to define the calibration curves.. Conclusions The study of correlations between EC and various nutrient contents of slurries (TKN, TAN, P O and K O) from various typologies confirmed good agreement between EC, TKN and TAN, reasonable agreement between EC and K O, and no significant agreement between EC and P O. Regression lines describing the linear correlation of EC with the various nutrients differ according to the specific typology of slurry, as characterised by species and category of animal as well as the management and feeding system used. Climate may have an influence as well. For this reason the results obtained are probably applicable only for slurries produced in similar conditions to those reported in this study. Using nutrient contents of slurries determined by laboratory tests as benchmarks, the field test of the prototype device showed that the equipment is able to give reliable estimates of TKN, TAN and K O in slurries, provided the regression equation used to convert EC readings to nutrient contents is related to the animal category under observation. The mean errors and the standard deviations obtained suggest that the equipment can provide good support to practical slurry spreading, even though it does not

8 G. Provolo, L. Martínez-Suller / Bioresource Technology 98 (7) reach an accuracy that is comparable to laboratory methods and does not give reliable information on phosphorus. References APHA, 998. In: Lenore, S.C., Greenberg, A.E., Eaton, A.D. (Eds.), Standard methods for the examination of water and wastewater, twentieth ed. American Public Health Association, Washington, DC. Araji, A.A., Abdo, Z.O., Joyce, P.,. Efficient use of animal manure on cropland: economic analysis. Bioresour. Technol. 79, Bidlingmaier, W., Maile, A., 99. Results of a parallel interlaboratory test of the analysis of compost. Compost. Sci. Util. (), 8 7. Carton, O., Hattermann, U., Lorenz, F., Steffens, G., 997. Balance approach: evaluation of a simple nutrient balance model for pig and cattle farms to estimate the nutrient value of slurry. SWAMP project Optimal use of animal slurries for input reduction and protection of the environment in sustainable agricultural systems. LUFA Oldenburg (German), March, 997. Chescheir III, G.M., Westerman, P.W., Safley Jr., L.M., 98. Rapid methods for determining nutrients in livestock manures. Trans. ASAE 8 (), Millmier, A., Lorimor, J., Hurburgh Jr., C., Fulhage, C., Hattey, J., Zhang, H.,. Near-infrared sensing of manure nutrients. Trans. ASAE (), Moral, R., Moreno-Caselles, J., Perez-Murcia, M.D., Perez-Espinosa, A., Rufete, B., Paredes, C.,. Characterization of the organic matter pool in manures. Bioresour. Technol. 9, 8. Ndegwa, P.M., Zhu, J., Luo, A.,. Stratification of solids, nitrogen and phosphorous in swine manure in deep pits under slatted floors. Bioresour. Technol. 8,. Piccinini, S., Bortone, G., 99. The fertilizer value of agricultural manure: simple rapid methods of assessment. J. Agric. Eng. Res. 9, 7 8. Price, R.R., Hummel, J.W., Birrel, S.J., Ahmad, I.S.,. Rapid nitrate analysis of soil cores using ISFETs. Trans. ASAE (),. Provolo, G.,. Manure management practices in Lombardy (Italy). Bioresour. Technol. 9,. Scotford, I.M., Cumby, T.R., Han, L., Richards, P.A., 998a. Development of a prototype nutrient sensing system for livestock slurries. J. Agric. Eng. Res. 9, 7 8. Scotford, I.M., Cumby, T.R., White, R.P., Carton, O.T., Lorenz, F., Hatterman, U., Provolo, G., 998b. Estimation of the nutrient value of agricultural slurries by measurement of physical and chemical properties. J. Agric. Eng. Res. 7, 9. Stevens, R.J., O Bric, C.J., Carton, O.T., 99. Estimating nutrient content of animal slurries using electrical conductivity. J. Agric. Sci., 8. Tunney, H., Bertrand, M., 989. Rapid field test for estimating dry matter and fertilizer value of animal slurry. In: Proceeding of the th International Congress on Agricultural Engineering, Land and Water Use. Dodd & Grace, Dublin, Ireland, pp. 7. Van Kessel, J.S., Reeves III, J.B.,. On-farm quick test for estimating nitrogen in dairy manure. J. Dairy Sci. 8, Zhu, J., Ndegwa, P.M., Zhang, Z.,. Manure sampling procedures and nutrient estimation by the hydrometer method for gestation pigs. Bioresour. Technol. 9,. Zhu, J., Zhang, Z., Ndegwa, P.M.,. Using a soil hydrometer to measure the nitrogen and phosphorus contents in pig slurries. Biosyst. Eng. 8, 8.

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