A b C R C a B b c P a A R R A A K A S P N S 1 g o s h 1 Europ. J. Agronomy 80 (2016) 88–104 Contents lists available at ScienceDirect European Journal of Agronomy j ourna l ho me page: www.elsev ier .com/ locate /e ja nnual crop rotation of tropical pastures with no-till soil as affected y lime surface application arlos A.C. Crusciola,∗, Rubia R. Marquesa, Antonio C.A. Carmeis Filhoa, ogério P. Sorattoa, Claudio H.M. Costaa, Jayme Ferrari Netoa, Gustavo S.A. Castrob, ristiano M. Parizc, André M. de Castilhosc São Paulo State University (UNESP), College of Agricultural Sciences, Department of Crop Science, P.O. Box: 237, 18610-307 Botucatu, State of São Paulo, razil Brazilian Agricultural Research Corporation (EMBRAPA), 13070-115 Campinas, State of São Paulo, Brazil UNESP, School of Veterinary Medicine and Animal Science, Department of Animal Nutrition and Breeding, P.O. Box: 560, 18618-970 Botucatu, State of São aulo, Brazil r t i c l e i n f o rticle history: eceived 14 March 2016 eceived in revised form 6 July 2016 ccepted 12 July 2016 vailable online 21 July 2016 eywords: cidity oil management lant nutrition et profit ustainability of tropical agriculture a b s t r a c t Soil acidity and low natural fertility are the main limiting factors for grain production in tropical regions such as the Brazilian Cerrado. The application of lime to the surface of no-till soil can improve plant nutrition, dry matter production, crop yields and revenue. The present study, conducted at the Lageado Experimental Farm in Botucatu, State of São Paulo, Brazil, is part of an ongoing research project initi- ated in 2002 to evaluate the long-term effects of the surface application of lime on the soil’s chemical attributes, nutrition and kernel/grain yield of peanut (Arachis hypogaea), white oat (Avena sativa L.) and maize (Zea mays L.) intercropped with palisade grass (Urochloa brizantha cv. Marandu), as well as the forage dry matter yield of palisade grass in winter/spring, its crude protein concentration, estimated meat production, and revenue in a tropical region with a dry winter during four growing seasons. The experiment was designed in randomized blocks with four replications. The treatments consisted of four rates of lime application (0, 1000, 2000 and 4000 kg ha−1), performed in November 2004. The surface application of limestone to the studied tropical no-till soil was efficient in reducing soil acidity from the surface down to a depth of 0.60 m and resulted in greater availability of P and K at the soil surface. Ca and Mg availability in the soil also increased with the lime application rate, up to a depth of 0.60 m. Nutrient absorption was enhanced with liming, especially regarding the nutrient uptake of K, Ca and Mg by plants. Significant increases in the yield components and kernel/grain yields of peanut, white oat and maize were obtained through the surface application of limestone. The lime rates estimated to achieve the maximum grain yield, especially in white oat and maize, were very close to the rates necessary to increase the base saturation of a soil sample collected at a depth of 0–0.20 m to 70%, indicating that the surface liming of 2000 kg ha−1 is effective for the studied tropical no-till soil. This lime rate also increases the forage dry matter yield, crude protein concentration and estimated meat production during winter/spring in the maize-palisade grass intercropping, provides the highest total and mean net profit during the four growing seasons, and can improve the long-term sustainability of tropical agriculture in the Brazilian Cerrado. . Introduction No-tillage (NT) is one of the main strategies adopted to miti- ate soil degradation. In this production model, the preservation f agricultural ecosystems is the main objective; additionally, this trategy has the potential to recover areas that are already con- ∗ Corresponding author. E-mail address: crusciol@fca.unesp.br (C.A.C. Crusciol). ttp://dx.doi.org/10.1016/j.eja.2016.07.002 161-0301/© 2016 Elsevier B.V. All rights reserved. © 2016 Elsevier B.V. All rights reserved. sidered unproductive. Because of its adaptability and enormous benefits for soil biodiversity, NT has been adopted in various regions of the world, especially in countries such as Argentina, Australia, Brazil, Canada and the United States (Derpsch et al., 2010). The large expansion of NT systems is primarily related to the productivity gains observed in legume and cereal crops. Soil acidity reduces the availability of nutrients such as calcium (Ca2+) and magnesium (Mg2+) and increases the bioavailability of toxic elements such as aluminum (Al3+) (von Uexküll and Mutert, 1995; Caires et al., 2005). Given these inappropriate conditions for dx.doi.org/10.1016/j.eja.2016.07.002 http://www.sciencedirect.com/science/journal/11610301 http://www.elsevier.com/locate/eja http://crossmark.crossref.org/dialog/?doi=10.1016/j.eja.2016.07.002&domain=pdf mailto:crusciol@fca.unesp.br dx.doi.org/10.1016/j.eja.2016.07.002 . J. Ag c p i o 2 h s 2 r t i G C t w T m s i s c r a S i h i s 2 t b B a a i c [ w d f a 2 f v ( t d r r p i e n f t a r ( p o d c C.A.C. Crusciol et al. / Europ rop development, liming is commonly employed to increase the roductive potential of soil. However, the low solubility and mobil- ty of limestone in soil cause its diminishing effect on soil acidity to ccur slowly once it reaches certain depths in NT soil (Ernani et al., 004). In regions with a regular rainfall distribution, several reports ave indicated an absence of any response of grain production to urface liming for years (Moreira et al., 2001; Caires et al., 2006a, 008a,b,c, 2011, 2015; Joris et al., 2013). However, in the tropical egions where dry spells often occur during the rainy season and he dry winter, chemical disorders due to soil and subsoil acid- ty is an important factor limiting crop productivity (Marsh and rove, 1992; Sumner, 1995; Castro and Crusciol, 2013a,b; Costa and rusciol, 2016; Tiritan et al., 2016). This effect has been attributed o the toxic effects of Al on root growth at certain depths, inducing ater stress and nutrient uptake by plants (Caires et al., 2008b). hus, subsoil acidity alleviation can promote greater root develop- ent, increasing the plants’ tolerance to water stress during dry pells. The amount of soil organic matter has been considered an mportant factor to reduce free Al levels; however, tropical soils uch as Oxisols and Ultisols exhibit a naturally low organic matter ontent. In NT systems, the addition of organic residues helps to egulate Al species in acid surface soils, but cash crops produce low mounts of straw (Alford et al., 2003; Allen et al., 2007; Zobeck and chillinger, 2010). In addition, regions with dry winters (low and rregular rainfall), such as the Brazilian Cerrado or African Savanna, ave large risks in growing a successful dry-season crop, resulting n a long fallow period without productivity (Borghi et al., 2013). In uch warm conditions, straw decomposes rapidly (Nascente et al., 013; Pariz et al., 2011a), and negatively affects success of NT sys- em. In NT systems, tropical forages such as palisade grass {Urochloa rizantha cv. Marandu (Hochst. ex A. Rich.) R.D. Webster [syn. rachiaria brizantha cv. Marandu (Hochst. ex A. Rich.) Stapf]} can be vailable for use in the winter to spring (grazed by animals or cut nd removed as fodder) and can be intercropped with grain crops n the summer (Kluthcouski and Aidar, 2003) using an integrated rop-livestock system. Therefore, intercropping maize, sorghum Sorghum bicolor (L.) Moench], and soybean [Glycine max (L.) Merr.] ith tropical perennial grasses is an excellent alternative for pro- ucing grain and forage for livestock during the dry season and or increasing the supply of straw for the continuity of NT man- gement (Pariz et al., 2011a,b; Borghi et al., 2013; Crusciol et al., 011, 2012, 2013, 2014, 2015; Mateus et al., 2016). Consequently, ood production is increased without the requirement of culti- ating additional areas, and the system is considered sustainable Sani et al., 2011; Surve and Arvadia, 2011). Most of the agricul- ural research in tropical and subtropical regions has focused on eveloping methods to identify liming requirements for soil cor- ection and on determining the rates and application methods that esult in higher crop response (Martins et al., 2014a,b). Intercrop- ing grain with forage crops is a new practice, and it requires more nformation before widespread adoption of the technology (Mateus t al., 2016). Knowledge of species competition for water, light, and utrients is important for successful grain production and adequate orage availability (Pariz et al., 2011b). For example, understanding he changes in soil chemical attributes and their effects on grain nd pasture yield is necessary for establishing and adjusting lime equirements in a crop rotation scheme under NT management Tiritan et al., 2016). This study aimed to evaluate the changes in the soil chemical roperties, plant nutrition and kernel/grain yield of peanuts, white at and maize intercropped with palisade grass, as well as forage ry matter yield of palisade grass in winter/spring, its crude protein oncentration, estimated meat production, and revenue resulting ronomy 80 (2016) 88–104 89 from superficial liming under no-tillage during four growing sea- sons in a region with dry winters, such as that of the Brazilian Cerrado. 2. Materials and methods 2.1. Site description and climatic data The experiment was conducted from October 2004 to October 2008 at the Lageado Experimental Farm of the College of Agricul- tural Sciences, FCA/UNESP, in Botucatu, São Paulo State, Brazil. The geographical coordinates of the study site are 48◦23′W, 22◦51′S and the elevation is 765 m. During the experimental period, rainfall was measured daily using a 50 cm tall plastic rain gauge (pluviometer) placed on the ground at a height of 1.20 m in the experimental area (Fig. 1). The soil is classified as a Typic Hapludox (USDA, 2014), with sand, silt, and clay contents of 54, 11, and 35%, respectively, at a depth of 0–0.20 m. The area had been managed since 2002 under a no-till system: in the growing season of 2002/2003, upland rice (Oryza sativa) in the summer and black oat (Avena strigosa Schreb.) in the fall; in the growing season of 2003/2004, common bean (Phaseolus vulgaris L.) in the summer and black oat in the fall. Prior to the beginning of the experiment (October 2002) and before the last limestone application (August 2004), eight subsam- ples were randomly obtained from useable areas of each plot at depths of 0–0.05, 0.05–0.10, 0.10–0.20, 0.20–0.40, and 0.40–0.60 m and were combined into one composite sample to determine the soil chemical attributes (Table 1). The soil pH was deter- mined in a 0.01 mol L−1 CaCl2 suspension (1:2.5 soil/solution). Soil organic matter was determined via the colorimetric method pro- posed by Haynes (1984) using a sodium dichromate solution. The total acidity at pH 7.0 (H + Al) was evaluated with 0.5 mol L−1 cal- cium acetate at pH 7.0 and determined through titration with a 0.025 mol L−1 NaOH solution. Exchangeable Al was extracted with neutral 1 mol L−1 KCl at a 1:10 soil/solution ratio and deter- mined by titration with a 0.025 mol L−1 NaOH solution. Available P and exchangeable Ca, Mg, and K were extracted using an ion exchange resin. Exchangeable Ca2+, Mg2+ and K+ were determined using a Shimadzu AA-6300 atomic absorption/Flame-Emission spectrophotometer. Phosphorus was determined calorimetrically (Murphy and Riley, 1962) using a FEMTO 600S spectrophotometer. The cation exchange capacity (CEC) was calculated from the sum of the concentrations of the H, Al, K, Ca, and Mg cations. Given the low natural level due to the climate conditions and mineralogical composition of the soil (Leal et al., 2009), exchangeable Na was not measured. Base saturation (BS) values were calculated by dividing the sum for K, Mg, and Ca (the bases) by the CEC and multiplying the result by 100% (van Raij et al., 2001). 2.2. Experimental design In this study, we adopted a completely randomized block exper- imental design with four treatments, replicated six times. The plot size was 5.4 m × 10.0 m. The plots were subjected to four rates of dolomitic limestone application: (i) Control (no lime); (ii) 1.0 Mg ha−1 (half the recommended dose); (iii) 2.0 Mg ha−1 (calcu- lated to raise base saturation to 70%); and (iv) 4.0 Mg ha−1 (twice the recommended dose). 2.3. Establishment of treatments At the beginning of the experiment (October 15, 2002), lime- stone rates were applied superficially. The reapplication on November 2004 was based on a soil analysis that was performed in August 2004, where the base saturation in the treatment in 90 C.A.C. Crusciol et al. / Europ. J. Agronomy 80 (2016) 88–104 Table 1 Chemical characteristics of the soil before the experiment (October 2002) and before the last application (August 2004). Depth pH (CaCl2) SOM P (resin) H + Al Al K Ca Mg CEC BS m g dm−3 mg dm−3 mmolc dm−3 % October 2002 0–0.05 5.0 27 17 38 4.0 1.6 28 11.7 78 53 0.05–0.10 4.9 25 12 40 3.7 1.0 31 14.2 85 54 0.10–0.20 4.3 24 7 56 9.1 0.4 21 8.2 85 34 0.20–0.40 3.9 22 6 83 17.9 0.2 18 4.8 105 22 0.40–0.60 3.9 23 4 100 24.8 0.2 19 3.7 122 18 0–0.20 4.2 21 9 37 6.5 1.2 14 5.0 58 37 August 2004 0–0.05 5.2 27 61 32 1.6 1.3 31 15.7 79 58 0.05–0.10 4.9 26 32 35 2.3 1.3 23 11.9 71 50 0.10–0.20 4.6 25 28 44 4.8 1.1 15 7.6 68 36 0.20–0.40 4.2 23 14 58 12.9 0.7 10 4.5 73 21 0.40–0.60 4.0 23 15 78 17.6 0.6 8 3.2 90 14 I w e s R w b B w i C M A t 2 y 2 3 s r P 0 t a t 2 a 1 { ( t a w i o t d N m 0–0.20 4.9 27 35 35 talic values signifies the weighted average of the first three depth. hich the limestone was applied reached 50%, which was the pre- stablished critical level for decreasing risk ascribed to soil acidity. The dolomitic limestone rate (R) was calculated to increase base aturation (BS) in the topsoil (0–20 cm) to 70%, as shown in Eq. (1): (kg ha−1) = (BS2 − BS1)CEC/(ECCE/1000) (1) here BS2 is the estimated base saturation (70%), and BS1 is the ase saturation measured in the soil analysis, as shown in Eq. (2): S1 (%) = (Caex + Mgex + Kex)100/CEC (2) here Caex, Mgex, and Kex are basic exchangeable cations, and CEC s the cation exchange capacity, calculated as indicated in Eq. (3): EC (mmolc dm−3) = Caex + Mgex + Kex + total acidity at pH 7.0 (H + Al) (3) The dolomitic limestone was composed of 23.3% CaO, 17.5% gO, and 71% effective calcium carbonate equivalents (ECCE). mong the limestone particles, 68.8, 92.4, and 99.7% passed hrough 50-, 20-, and 10-mesh sieves, respectively. .4. Crop management and the determination of plant nutrition, ield components, and crop yields .4.1. Peanut The Runner IAC 886 cultivar was sown on 22 Nov., 2004 and 0 Nov., 2005. The peanut seeds were sown in both growing sea- ons at a spacing of 0.80 m between rows, with 12 seeds m−1 ow using no-till seeding (Semeato, model Personale Drill 13, asso Fundo, RS, Brazil). Every 100 kg of seeds was treated with .7 g of the active ingredient (a.i.) thiamethoxam {3-(2-chloro- hiazol-5-ylmethyl)-5-methyl-1,3,5-oxadiazinan-4-ylidene (nitro) mine} to control Enneothrips flavens. The basic fertilization (at he time of peanut sowing) in the sowing furrows consisted of 4 kg ha−1 of N as urea, 84 kg ha−1 of P2O5 as triple superphosphate nd 48 kg ha−1 of K2O as KCl + 0.5% Zn + 10% S (Ambrosano et al., 997). Weeds were controlled using 0.4 kg a.i. ha−1 of paraquat 1,1′-dimethyl-4,4′-bipyridium} and 0.2 kg a.i. ha−1 of diurom {3- 3,4-dichlorophenyl)-1,1-dimethylurea} 1 d after sowing. 50% of he plants emerged by 8 d after sowing in the first growing season nd by 10 d after sowing in the second. When the peanut plants were at the full-bloom stage, 40 plants ere sampled per plot (apical cluster of the main branch), accord- ng to Ambrosano et al. (1997). The material was dried in an ven at 65 ◦C to constant weight and then ground for macronu- rient analyses. The concentrations of N, P, K, Ca, Mg, and S were etermined using methods described by Malavolta et al. (1997). itrogen was determined by the Kjeldahl method. For the deter- ination of other nutrients, milled plant material was mineralized 2.3 1.1 24 10.0 70 50 with a nitric-perchloric solution. From this solution, K, Ca, and Mg concentrations were determined using an atomic absorption spec- trophotometer. P and S were measured by colorimeter methods using a spectrophotometer (Malavolta et al., 1997). Peanut harvesting was performed manually on April 07th, 2005 and April 20th, 2006 in the first and second growing season, respec- tively. Yield components [the final population of plants (number of plants in the two central rows in 8-m rows extrapolated to hectare), the number of filled pods per plant (number of pods in 10 plants), the number of kernels per pod (total number of ker- nels in 10 plants/total number of pods in 10 plants), the 100-kernel weight (four samples of 100 kernels) and the hulled-kernel yield (kernel weight/pod weight ratio)] from each plot were determined. Pod yield (moisture content of 90 g kg−1) was determined by man- ually harvesting the plants in the two central rows in 8-m rows. Ten peanut plants per plot were sampled for the evaluation of shoot dry matter at ground level. 2.4.2. White oat Before sowing, the test area was desiccated by applying glyphosate (Roundup Original, 1800 g acid equivalents ha−1, Mon- santo Brazil). White oat cultivar ‘IAC 7’ was sown on April 23, 2005 and May 04, 2006. The seeds were sown in both growing seasons at a within-row spacing of 0.17 m, with 133 viable seeds m−2 using no-till seeding (Semeato, model Personale Drill 13, Passo Fundo, RS, Brazil). The basic fertilization (at the time of white oat sowing) in the sowing furrows consisted of 8 kg ha−1 of N as urea, 40 kg ha−1 of P2O5 as triple superphosphate and 20 kg ha−1 of K2O as KCl + 7% S (Cantarella et al., 1997). After sowing was complete, 50% of the plants emerged by 8 d in the first growing season and by 7 d in the second growing season. Full flowering of plants took place 58 and 66 days after sowing in the first and second growing seasons, respectively. At that stage, 10 plants were sampled for the evaluation of dry matter contents. Additionally, the flag leaves of 50 plants per plot were sampled (Cantarella et al., 1997) for macronutrient determination (N, P, K, Ca, Mg and S) (Malavolta et al., 1997). White oat was harvested on September 06th, 2005 and September 09th, 2006 in the first and second growing seasons, respectively. Yield components [number of panicles per square meter (number of panicles in the two central rows in 8-m rows), number of spikelets per panicle (number of spikelets in 20 panicles), spikelet fertility (number of grain-bearing spikelets/total number of spikelets per panicle) and 1000-grain weight (four samples of 1000 grains)] from each plot were determined. Grain yield (mois- ture content of 130 g kg−1) was determined by manually harvesting the plants in the two central rows in 8-m rows. Ten white oat plants C.A.C. Crusciol et al. / Europ. J. Ag Fig. 1. Monthly rainfall (mm) and average temperature (◦C) at the experimen- t N a p g 2 ( M a h m c r 5 c al area at Botucatu, São Paulo State, Brazil, during the period from November to ovember in the agricultural years of (a) 2004–2005, (b) 2005–2006, (c) 2006–2007 nd 2007–2008. er plot were sampled for the evaluation of shoot dry matter at round level. .4.3. Maize and palisade grass Before sowing, the area was desiccated by applying glyphosate Roundup Original, 1800 g acid equivalents ha−1, Monsanto Brazil). aize was sown on December 2nd, 2006 and December 10th, 2007, t a depth of 3 cm using a no-till drill at a density of 66,000 seeds a−1 and a row spacing of 0.45 m using no-till seeding (Semeato, odel Personale Drill 13, Passo Fundo, RS, Brazil). The hybrid hosen was 2B570, an intermediate maturation-cycle hybrid that equired good soil fertility. Every 100 kg of seeds was treated with 0 g of the a.i. carboxin {5,6-dihydro-2-methyl-1,4-oxathi-ine-3- arboxanilide} and 50 g of the a.i. thiram {Tetramethylthiuram ronomy 80 (2016) 88–104 91 disulfide} to control pests (Aspergillus flavus, Acremonium strictum, Fusarium moniliforme and Penicillium oxalicum). The basic fertiliza- tion (at the time of maize sowing) in the sowing furrows consisted of 24 kg ha−1 of N as urea, 84 kg ha−1 of P2O5 as triple superphos- phate and 48 kg ha−1 of K2O as KCl (Cantarella et al., 1997). Palisade grass was simultaneously sown at densities of 15.3 kg ha−1 seed (pure live seed = 34%). The forage seeds were mixed with base fer- tilizer (Mateus et al., 2007) and sown at depths of 8 cm below the soil surface, in the same maize rows, as described by Crusciol et al. (2012). After sowing, 50% of the plants had emerged by 5 d in the first growing season and by 7 d in the second. During both growing seasons, the side dressing fertilization consisted of 90 kg ha−1 of N, applied mechanically between rows as urea. At the full-flowering stage, 10 plants per plot were sampled for the evaluation of dry matter contents. Additionally, the central third part of 30 leaves was sampled at the ear base (Cantarella et al., 1997) for macronutrient determination (N, P, K, Ca, Mg and S) (Malavolta et al., 1997). Maize was harvested on April 1st, 2007 and March 29th, 2008 in the first and second growing seasons, respectively. Yield compo- nents [final plant population (number of plants in the two central rows in 8-m rows extrapolated to hectare), number of ears per plant (number of ears in 8-m rows/total number of plants in 8-m rows), number of grains per ear (number of grains in 10 ears) and 100- grain weight (eight samples of 100 grains)] from each plot were determined. Grain yield (moisture content of 130 g kg−1) was deter- mined by manually harvesting the plants in the two central rows in 8-m rows. Ten corn plants per plot were sampled for the evaluation of shoot dry matter at ground level. The forage dry matter yield values of palisade grass were evalu- ated 70 d (first cut) and 130 d (second cut) after the maize harvests in June and August, respectively. All forage (0.25 m from the soil surface) was cut in three areas of the plots (2 m2 for each area with a row spacing of 0.45 m) using a manual mechanical rotary mower. After cutting, all forage was removed from the plots, also using a manual mechanical rotary mower. This cutting height was used to provide faster forage regrowth. The collected material was dried using forced-air circulation at 65 ◦C for 72 h. The dry matter was weighed, and the data were extrapolated to kg ha−1. For a crude protein evaluation, a sub-sample of palisade grass dry matter was used to determine the nitrogen concentration. Nitrogen was determined by the Kjeldahl method. To calculate the crude protein, the following formula was used: crude protein (%) =%N × 6.25 (Malavolta et al., 1997). 2.5. Estimated meat production Although grazing by animals was not realized for the palisade grass after the grain maize harvest in the winter/spring, meat production was estimated using the Large Ruminant Nutrition System (LRNS; http://nutritionmodels.tamu.edu/lrns.html) model. The LRNS model is based on the Cornell Net Carbohydrate and Pro- tein System (CNCPS), version 5, as described by Fox et al. (2004). The following factors were used to predict the energy and protein requirements, the performance and the dry matter intake by indi- vidual cattle fed in a group: Nellore breed, bull sex, 450 kg body weight, 52% of carcass yield, 22% Body Fat Grading System and con- tinuous grazing. For each treatment, the values of the nutritional palisade grass composition were used to predict the performance values. The dry matter intake by individual cattle fed in a group was 10.0 kg of dry matter day−1. Due to the high forage crude protein (8.4–12.0%), the average daily weight gain (ADWG) was based on the allowable metabolizable energy and protein gain. Therefore, the ADWGs were used to estimate the meat production. The dry matter herbage allowance was the double amount of dry matter http://nutritionmodels.tamu.edu/lrns.html http://nutritionmodels.tamu.edu/lrns.html http://nutritionmodels.tamu.edu/lrns.html http://nutritionmodels.tamu.edu/lrns.html http://nutritionmodels.tamu.edu/lrns.html http://nutritionmodels.tamu.edu/lrns.html 9 . J. Ag i a i c w a o a f g s t t a g p 2 s c t p t G c f p i p ( 2 b 0 m p b s t 2 W I t o v F s M d L e s s s A i s 2 C.A.C. Crusciol et al. / Europ ntake by individual cattle, considering a grazing efficiency of 60%, ccording to Braga et al. (2007). The time of animal grazing was calculated using a method sim- lar to that used by Crusciol et al. (2012). A period of 365 d was onsidered, including an average maize life cycle of 115 d, a 70-d aiting period (an important waiting period after the maize harvest nd before animal grazing of palisade grass pasture), and a period f 60 d after animal grazing on palisade grass pasture for regrowth nd desiccation to produce straw under NT management. There- ore, 120 d (365 d – 115 d – 70 d – 60 d) were available for animal razing for all of the treatments (60 d in each cut). Then, the animal tocking rate was estimated from the forage dry matter yield data, he time of animal grazing (days per cut), the dry matter intake by he individual cattle fed in a group and the grazing efficiency. The nimal stocking rate was multiplied by ADWG, the time of animal razing and the carcass yield (52%) to estimate the total cattle meat roduced per hectare. .6. Economic evaluation An economic evaluation of each surface-applied dolomitic lime- tone rate was also conducted. The cost per hectare to produce each rop was calculated (CONAB, 2010). The only difference between reatments was the dolomitic limestone rates used before the eanut crop (November 2004) and the pasture costs as a function of he animal stocking rate. The average peanut, white oat and maize Y (kg ha−1) and the estimated meat production (kg ha−1) were alculated, and the result was multiplied by the price per kg. The net profit realization per hectare was calculated using the ollowing formula: (revenue – cost). The total profit and mean net rofit were the sum of all growing seasons and the mean by grow- ng season, respectively. We used the Brazilian national average rices from the last five years and converted these values to euro D ) (Agrolink, 2016). .7. Determination of soil chemical characteristics Soil chemical characteristics (pH, H + Al, Al, P, Ca, Mg, K and ase saturation) were evaluated at depths of 0.00–0.05, 0.05–0.10, .10–0.20, 0.20–0.40 and 0.40–0.60 m at twelve and twenty four onths after the application of correction material. Six simple sam- les were collected at random from the useful area of each plot and etween rows of the previous crop to form a composite sample. The amples were dried, sieved (2-mm sieves) and analyzed according o van Raij et al. (2001). .8. Statistical analyses All data were initially tested for normality using the Shapiro- ilk test from the UNIVARIATE procedure of SAS (version 9.3; SAS nst. Inc., Cary, NC), and the results indicated that all data were dis- ributed normally (W ≥ 0.90). The assumption for the homogeneity f variances was tested using Levene’s test for residual errors. When ariances could not be considered homogeneous (P ≤ 0.10), Welch’s -test was performed to determine the overall significance for the tatistic of interest. The data were then analyzed using the PROC IXED procedure of SAS and the Satterthwaite approximation to etermine the degrees of freedom for the tests of fixed factors. ime rates were considered fixed factor, and blocks were consid- red random factor. A repeated statement was used with growing eason specified as the repeated variable and block × lime rates pecified as the subject. The covariance structure used in the analy- es was autoregressive, which provided the best fit according to the kaike information criterion. Only the soil results were compared ndividually for each season (12 and 24 months after lime rates urface reapplication). The results are reported as the least square ronomy 80 (2016) 88–104 means and are separated using the probability of differences option (PDIFF). The lime rates were analyzed using the PROC REG proce- dure of SAS, and the best adjustments were chosen as those with the greatest coefficients of determination and considering the values of the t-test. Error bars are presented as standard errors (SEs) and were determined using the PROC MEAN procedure of SAS. The growing season (only for the plant results and estimated meat production) means were compared via Fisher’s protected LSD test. The effects of main factor and interaction (lime rates × growing seasons) were considered statistically significant at P < 0.05. 3. Results 3.1. Soil chemical attributes After 12 months of superficial liming, a positive effect on the main soil properties was verified (Table 2). Notably, the potential (H + Al) and exchangeable (Al3+) acidity were reduced in propor- tion to the applied carbonate dose; this effect was reflected in the soil pH values. The effects of superficial liming on pH levels were observed up to a depth of 0.40–0.60 m after 12 months; how- ever, after 24 months, the effect was limited to the upper 0.20 m. Although there were no differences in pH values, reductions in potential and exchangeable acidity were observed down to 0.60 m depth after 24 months. Liming also increased macronutrient availability in soil. Regard- ing the effects on the availability of phosphorus, calcium, potassium and magnesium, after 12 months, the correction raised P, Ca and Mg in all layers evaluated; however, K availability was unchanged in the deeper layers (0.20–0.40 and 0.40–0.60 m). After 24 months, the correction increased the levels of K, Ca, Mg and P at depths down to 0.60 m. The increase in the K, Ca and Mg levels was also positively reflected in base saturation values (BS%) down to 0.60 m. 3.2. Peanut, white oat, and maize nutrition Superficial liming increased the foliar concentrations of N, P, K, Ca and Mg in peanut; the effect for K, Ca and Mg was linear, while N, P and S were better explained by a quadratic equation (Fig. 2a–e). In the first growing season, the N, P and K concentrations were lower than in the second growing season, while for the other nutrients, no effect of the growing season was observed (Table 3). The improve- ment of plant nutrition with liming was reflected in greater shoot dry matter production (Fig. 3a); in addition, the second growing season plants showed more vegetative development. In the white oat crop, quadratic responses of K, Mg and S were verified (Fig. 4a–d) as well as linear responses for the Ca con- centrations (Fig. 4b). However, it was observed that the second growing season showed the highest macronutrient concentration in the leaves, except for N and S (Table 4). Shoot dry matter pro- duction was influenced by both factors, with a quadratic increase being observed for liming (Fig. 5a and d) and superior results in the first growing season. Regarding maize nutrition, higher concentrations of all macronutrients were observed in the second growing season (Table 5). Positive responses were also associated with liming for all nutrients except for P (Fig. 6). As observed for the peanut and white oat crops, shoot dry matter production in maize was also increased with liming, and the highest production occurred in the second growing season. 3.3. Peanut, white oat, and maize components and kernel/grain yield Liming positively affected the yield components and ker- nel/grain yield of peanut, white oat and maize (Tables 3–5). In C.A.C. Crusciol et al. / Europ. J. Agronomy 80 (2016) 88–104 93 Table 2 Regression equations and coefficients of determination between some soil chemical attributes and limestone rates applied 12 and 24 months before soil samples were collected from a long-term no-till soil and ANOVA significance. Soil depth (m) Limestone rate, kg ha−1 Regression R2 ANOVA (p > F) 0 1000 2000 4000 pH (CaCl2), 12 months 0–0.05 4.5 4.6 5.0 5.2 y = 0.00017x + 4.5 87.8 0.0091 0.05–0.10 4.2 4.5 4.8 5.2 y = 0.000243x + 4.25 99.5 <0.0001 0.10–0.20 3.9 4.1 4.2 4.6 y = 0.00016x + 3.895 96.9 0.0014 0.20–0.40 3.5 3.7 3.9 4.0 y = 0.0001x + 3.56 89.5 0.0328 0.40–0.60 3.6 3.7 3.9 4.1 y = 0.0001x + 3.6 98.1 0.0064 pH (CaCl2), 24 months 0–0.05 4.7 5.0 5.6 6.1 y = 0.00037x + 4.74 97.3 <0.0001 0.05–0.10 4.6 4.8 5.2 6.0 y = 0.000358x + 4.53 99.1 <0.0001 0.10–0.20 4.3 4.4 4.5 5.0 y = 0.00018x + 4.27 93.5 <0.0001 0.20–0.40 4.0 4.2 4.3 4.3 – – ns 0.40–0.60 4.0 4.0 4.1 4.1 – – ns H + Al, 12 months 0−0.05 58 45 27 19 y = −0.009671x + 53.86 90.6 <0.0001 0.05−0.10 83 50 45 34 y = −0.011x + 71.81 76.7 <0.0001 0.10−0.20 85 70 53 44 y = −0.01x + 80.37 90.9 <0.0001 0.20−0.40 167 104 90 70 y = 9E−06x2 − 0.0578x + 163.35 96.5 <0.0001 0.40−0.60 145 105 80 69 y = 7E−06x2 − 0.047x + 145.26 99.9 0.0016 H + Al, 24 months 0–0.05 47 40 23 15 y = 1E-06x2 − 0.0141x + 48.76 94.5 <0.0001 0.05–0.10 50 48 41 27 y = −0.006x + 52.273 97.1 <0.0001 0.10–0.20 71 69 54 43 y = −0.0076x + 72.56 94.3 <0.0001 0.20–0.40 93 83 71 63 y = −0.00756x + 90.60 93.0 <0.0001 0.40–0.60 109 103 96 82 y = −0.0069x + 109.8 99.9 <0.0001 Exchangeable Al3+, 12 months 0–0.05 1.6 1.6 1.2 1.1 y = −0.000131 + 1.61 85.2 <0.0001 0.05–0.10 4.2 2.1 1.6 1.1 y = −0.000688x + 3.44 76.1 <0.0001 0.10–0.20 4.5 3.5 2.4 1.9 y = −0.00064x + 4.16 88.7 <0.0001 0.20–0.40 8.5 7.8 6.8 5.8 y = −0.00068x + 8.40 98.6 0.0146 0.40–0.60 9.1 8.4 7.1 6.4 y = −0.00068x + 8.91 93.3 <0.0001 Exchangeable Al3+, 24 months 0–0.05 2.4 1.4 1.4 1.1 y = 1E−07x2 − 0.0007x + 2.27 91.0 0.0041 0.05–0.10 3.4 2.2 1.9 1.3 y = 1E−07x2 − 0.001x + 3.2758 97.0 0.0225 0.10–0.20 6.4 5.6 4.7 2.6 y = −0.00095x + 6.48 99.5 <0.0001 0.20–0.40 11.2 10.3 8.7 7.8 y = −0.00088 + 11.02 93.3 0.0049 0.40–0.60 13.4 12.6 12.4 11.0 – – ns P resin, 12 months 0–0.05 27.6 36.1 41.6 50.4 y = 0.0055x + 29.25 97.3 <0.0001 0.05–0.10 11.4 16.5 18.9 24.7 y = 0.0032x + 12.26 97.7 <0.0001 0.10–0.20 8.2 8.5 10.7 12.2 y = 0.0011x + 7.987 93.7 <0.0001 0.20–0.40 3.8 4.6 5.1 5.8 y = 0.00048x + 4.00 96.0 <0.0001 0.40–0.60 3.8 5.2 5.2 9.3 y = 0.0013x + 3.54 92.2 <0.0001 P resin, 24 months 0–0.05 26 34 46 55 y = 0.0075x + 27.08 96.2 <0.0001 0.05–0.10 15 20 33 23 y = −3E−06x2 + 0.0135x + 13.37 81.1 <0.0001 0.10–0.20 5.8 7.8 12.0 12.9 y = −5E−07x2 + 0.0039x + 5.40 94.8 0.0071 0.20–0.40 2.4 2.7 3.1 4.2 y = 0.00045x + 2.31 98.1 <0.0001 0.40–0.60 2.1 2.4 2.6 2.9 y = 0.0002x + 2.14 94.9 0.0044 Exchangeable K+, 12 months 0–0.05 3.4 3.5 4.8 4.2 y = −2E−07x2 + 0.001x + 3.23 66.0 0.0011 0.05–0.10 2.2 2.5 3.2 2.7 y = −2E−07x2 + 0.0008x + 2.09 85.5 0.0122 0.10–0.20 2.0 2.1 2.3 2.4 y = 0.00011x + 2.023 83.7 0.0132 0.20–0.40 2.0 2.2 2.2 2.2 – – ns 0.40–0.60 2.0 2.0 2.3 2.3 – – ns Exchangeable K+, 24 months 0–0.05 1.4 2.5 3.4 2.3 y = −4E−07x2 + 0,0017x + 1,3647 96.5 <0.0001 0.05–0.10 0.9 1.0 2.3 1.2 y = −2E−07x2 + 0.0011x + 0.68 62.1 <0.0001 0.10–0.20 0.5 0.7 1.0 0.8 y = −6E−08x2 + 0.0003x + 0.450 88.1 0.0018 0.20–0.40 0.3 0.4 1.0 0.5 y = −1E−07x2 + 0.0005x + 0.19 76.2 <0.0001 0.40–0.60 0.3 0.4 0.5 0.6 y = 0.00008x + 0.31 95.2 0.0009 Exchangeable Ca2+, 12 months 0–0.05 14 28 46 66 y = 0,013x + 15.66 98.0 <0.0001 0.05–0.10 10 17 25 32 y = 0.00554x + 11.28 94.5 <0.0001 0.10–0.20 10 14 15 18 y = 0.00183x + 10.864 90.8 <0.0001 0.20–0.40 6.6 7.4 9.1 9.1 y = 0.00064x + 6.90 76.9 0.0014 0.40–0.60 6.1 7.8 9.1 10.3 y = 0.001x + 6.536 94.1 <0.0001 94 C.A.C. Crusciol et al. / Europ. J. Agronomy 80 (2016) 88–104 Table 2 (Continued) Soil depth (m) Limestone rate, kg ha−1 Regression R2 ANOVA (p > F) 0 1000 2000 4000 Exchangeable Ca2+, 24 months 0–0.05 17.9 35.4 60.6 82.2 y = 0.016x + 20.58 96.8 <0.0001 0.05–0.10 15.4 21.4 24.7 40.1 y = 0.0061x + 14.72 97.9 <0.0001 0.10–0.20 11.6 13.0 14.2 26.6 y = 0.0038x + 9.73 88.5 <0.0001 0.20–0.40 7.7 9.1 10.0 10.5 y = 0.00067x + 8.15 85.3 0.0029 0.40–0.60 6.3 6.6 7.5 8.4 y = 0.0005x + 6.24 97.5 0.0033 Exchangeable Mg2+, 12 months 0–0.05 7.7 15.9 29.6 36.6 y = 0.00735x + 9.582 92.5 <0.0001 0.05–0.10 4.9 8.1 9.5 24.3 y = 0.0048x + 3.268 92.1 <0.0001 0.10–0.20 5.2 7.3 10.9 12.5 y = 0.001857x + 5.73 91.4 <0.0001 0.20–0.40 2.7 3.3 4.5 5.0 y = 0.00059x + 2.82 91.8 <0.0001 0.40–0.60 3.0 3.6 4.3 5.1 y = 0.00053x + 3.055 98.7 <0.0001 Exchangeable Mg2+, 24 months 0–0.05 11.4 16.5 25.0 35.3 y = 0.0061x + 11.38 99.1 <0.0001 0.05–0.10 9.2 10.5 14.7 21.5 y = 0.0032x + 8.37 97.9 <0.0001 0.10–0.20 7.4 8.8 8.9 15.7 y = 0.002x + 6.58 88.1 <0.0001 0.20–0.40 5.0 6.0 6.0 7.1 y = 0.00049x + 5.16 92.7 <0.0001 0.40–0.60 3.6 3.9 4.1 4.2 y = 0.00015x + 3.70 87.6 0.0192 Base saturation, 12 months 0–0.05 30 52 75 85 y = −0.000004 ×2 + 0.029x + 29.26 99.1 <0.0001 0.05–0.10 17 36 46 64 y = 0.01123x + 20.915 96.3 <0.0001 0.10–0.20 17 25 35 43 y = 0.00645x + 18.53 94.9 <0.0001 0.20–0.40 6 11 15 19 y = 0.003x + 7.46 95.2 <0.0001 0.40–0.60 7 11 17 20 y = −6E−07x2 + 0.0057x + 6.90 99.3 0.0044 Base saturation, 24 months 0–0.05 40 57 80 89 y = −3E−06x2 + 0.026x + 38.16 98.4 <0.0001 0.05–0.10 34 41 50 70 y = 0.0092x + 32.54 99.5 <0.0001 0.10–0.20 22 25 31 50 y = 1E−06x2 + 0.002x + 21.48 99.9 <0.0001 0.20–0.40 12 16 19 22 y = 0.0025x + 13.06 94.8 <0.0001 0.40–0.60 8 9 11 14 y = 0.00137x + 8.40 99.2 <0.0001 Table 3 Influence of surface-applied limestone rates on crop nutrition, shoot dry matter, yield components, and peanut pod yield in a long-term no-till soil and ANOVA significance. Growing season N P K Ca Mg S Shoot dry matter g kg−1 kg ha−1 2004/2005 33.5 b1 4.7 b 18.9 b 13.6 a 5.9 a 4.0 a 3181 b 2005/2006 37.7 a 6.6 a 24.3 a 14.2 a 5.7 a 3.9 a 3746 a ANOVA (F probability) Blocks 0.1622 0.5998 0.6668 0.9371 0.5038 0.7854 0.6598 Limestone rates (R) 0.0467 <0.0001 <0.0001 0.0427 0.0431 0.1050 <0.0001 Growing season (S) 0.0005 <0.0001 <0.0001 0.5094 0.6413 0.7031 <0.0001 R × S 0.3145 0.6166 0.6511 0.7509 0.9669 0.9447 0.4356 Growing season Final population Filled pods per plant Kernels per pod 100-kernel weight Peanut pod yield Hulled-kernel yield n◦ n◦ n◦ g kg ha−1 % 2004/2005 123055 a1 20 a 1.2 b 47.4 b 2476 b 57 b 2005/2006 116319 a 21 a 1.6 a 53.2 a 3423 a 61 a ANOVA (F probability) Blocks 0.1635 0.8043 0.6589 0.4788 0.2423 0.4102 Limestone rates (R) 0.0458 0.0004 0.2987 0.0315 0.0011 0.0182 Growing season (S) 0.2016 0.2523 <0.0001 <0.0001 0.0008 0.0415 54 test (p p e n a t a k t y i R × S 0.5796 0.2241 0.87 1 Means followed by different letters in the column differ statistically by the LSD eanut, except for the 100-kernel weight, which responded lin- arly (Fig. 3d), and the number of kernels per pod, which was ot affected, shoot dry matter, number of plants per hectare and ll reproductive parameters were explained by quadratic equa- ions (Fig. 3a–f) and the maximum hulled-kernel yield (62%) was chieved under 2800 kg ha−1 of lime application. Moreover, more ernels per pod and a higher 100-kernel weight were observed in he second cropping season, with a positive impact on the kernel ield and hulled-kernel yield. In white oat, liming also positively affected the number of pan- cles per square meter and the number of spikelets per panicle 0.2844 0.2126 0.8254 < 0.05). (Fig. 5b and c); these increases were reflected in the grain yield (Fig. 5d). All these relationships were quadratic and the maxi- mum grain yield (3191 kg ha−1) was achieved under 2000 kg ha−1 of lime application. Regarding the growing season, it is important to note that environmental conditions in the second growing season favored the plants’ development, resulting in better growth and development of reproductive structures (number of panicles and spikelets), leading to a higher yield. For the maize crop, the grain yield increased quadratically with lime application, due to its beneficial effects on stand establish- ment and the increased development of reproductive structures C.A.C. Crusciol et al. / Europ. J. Agronomy 80 (2016) 88–104 95 y = -2E-07x2 + 0.0017x + 33.786 (r ² = 0.99; p<0.0001) 33 34 35 36 37 38 0 10 00 20 00 30 00 40 00 N ( g k g -1 ) (a) y = -2E-07x2 + 0.0017x + 4.04 (r ² = 0.99; p<0.0001) 2 3 4 5 6 7 8 0 10 00 20 00 3000 40 00 P ( g k g -1 ) (b) y = 0. 0009 x + 12 .32 (r ² = 0. 96; p<0. 0001) 10 11 12 13 14 15 16 17 18 0 10 00 20 00 30 00 40 00 C a (g k g -1 ) (d)y = 0.0 035x + 15. 48 (r ² = 0.9 8; p<0. 0001) 10 15 20 25 30 35 0 1000 20 00 30 00 4000 K ( g k g -1 ) (c) y = 0. 0005x + 4. 92 (r² = 0. 91; p=0.0065) 3 4 5 6 7 8 0 10 00 20 00 30 00 40 00 M g ( g k g -1 ) Limestone (kg ha-1) (e) y = -1E-07x2 + 0.0008x + 3.337 (r ² = 0.99; p<0.0395) 2 3 4 5 0 10 00 20 00 30 00 40 00 S ( g k g -1 ) Limestone (kg ha-1) (f) F (f) su a . Bars s ( d g 3 w ( e t e o d ig. 2. (a) Nitrogen, (b) phosphorus, (c) potassium, (d) calcium, (e) magnesium and s affected by surface-applied dolomitic limestone rates in a long-term no-till soil easons). Fig. 7a–c). In the second growing season, the values for all pro- uction components were higher, with a significant increase in the rain yield of 2542 kg ha−1 being recorded. .4. Forage characteristics and estimated meat production In the first and second cuttings, the forage dry matter yield as influenced by the interaction of lime rate × growing season Table 6). In the first and second cuttings, crude protein was influ- nced by the lime rates. In the first and second cuttings and in the otal of both cuttings, the estimated meat production was influ- nced by the interaction of lime rate × growing season. All results f forage dry matter yield, crude protein and estimated meat pro- uction were quadratically related to the lime rate (Figs. 8 and 9). lfur concentrations in peanut (sampled from the apical cluster of the main branch) indicate the standard error at each lime rate (n = 8; four replicates × two growing 3.5. Economic evaluation The lowest total profit and mean net profit (0 kg ha−1, D 1198 and D 299 per ha, respectively) were achieved with no surface application of dolomitic limestone, and a rate of 2000 kg ha−1 of dolomitic limestone resulted in the highest total and mean net profit (D 4064 and D 1016 per ha, respectively) (Table 7). 4. Discussion 4.1. Soil chemical attributes Despite the low solubility and mobility of limestone in the soil, effects of superficial liming were observed at depths down to 0.60 m after 12 months, with a significant reduction in the active acid- 96 C.A.C. Crusciol et al. / Europ. J. Agronomy 80 (2016) 88–104 y = -8E -05x2 + 0.479x + 3064 (r ² = 0.99; p<0.0001) 2500 2700 2900 3100 3300 3500 3700 3900 0 1000 20 00 30 00 40 00 S h o o t d ry m a tt e r (k g h a -1 ) (a) y = -0.002x2 + 12.332x + 1 08529 (r ² = 0.9 9; p< 0.0211) 100000 105000 110000 115000 120000 125000 130000 0 1000 2000 3000 4000 N o . o f p la n ts h a -1 (b) y = 0.0006x + 49.2 (r² = 0.94; p<0.0001) 48 49 50 51 52 53 0 10 00 20 00 30 00 40 00 1 0 0 -k e rn e l w e ig h t (g ) (d)y = -2E-07x2 + 0.0023x + 17.7 (r ² = 0.94; p=0011) 15 17 19 21 23 25 0 10 00 20 00 30 00 40 00 N o . o f fi ll e d p o d s p la n t-1 (c) y = -3E-05x2 + 0. 5229x + 2198. 7 (r ² = 0.9 9; p<0. 0001) 1500 2000 2500 3000 3500 4000 4500 0 10 00 20 00 30 00 40 00 P e a n u t p o d y ie ld ( k g h a -1 ) Limestone (kg ha-1) (e) y = -1E-06x2 + 0.0056x + 54.62 (r ² = 0.97; p=0.0465) 53 55 57 59 61 63 65 0 10 00 20 00 30 00 40 00 H u ll e d -k e rn e l y ie ld ( % ) Limestone (kg ha-1) (f) F rain w s he sta i d t N u s o m l c a p t m ig. 3. (a) Shoot dry matter, (b) number of plants, (c) number of pods, (d) 100-g urface-applied dolomitic limestone rates in a long-term no-till soil. Bars indicate t ty and potential and exchangeable values (Table 2). This result emonstrates the effectiveness of superficial liming in improving he chemical properties of highly acidic subsoils within a short time. otably, even with a high base concentration in the top 0–0.05 m, nder the highest applied dose (4.0 Mg ha−1), the increase of the oil pH at 12 months was not excessive enough to harm the devel- pment of agricultural crops. According to Alleoni et al. (2005), this ay be explained by the high buffering capacity existing in this ayer, due to the accumulation of organic matter at the soil surface. It is important to emphasize that the absence of mechani- al mobilization promotes benefits related to the maintenance of ggregates and channels formed by soil biological activity. The reservation of soil physical properties is an important factor in he percolation of limestone particles into the subsoil; i.e., NT favors obilization of the carbonate and the lime reaction at depth (Corrêa eight, (e) peanut pod yield and (f) hulled-kernel yield of peanut as affected by ndard error at each lime rate (n = 8; four replicates × two growing seasons). et al., 2009; Castro et al., 2011; Briedis et al., 2012). In addition to the soil physical conditions, experimental results have emphasized the importance of soil anions on Ca2+ and Mg2+ mobility into the subsoil. After 24 months, liming decreased total and exchangeable acid- ity but it did not increased pH (Table 2). At 24 months, the effect of liming decreasing potential and exchangeable (Al3+) acidity in the 0.40–0.60 m and 0.20–0.40 depths was probably related to the action of water-soluble acids derived from the decomposition of the roots of previous crops, which can interfere with exchangeable and potential acidity by reducing the activity of Al3+ (Miyazawa et al., 2002; Soratto and Crusciol, 2007). Caires et al. (2000) found that potential acidity was reduced for up to approximately 28 months after superficial liming; however, the pH began to show decreases C.A.C. Crusciol et al. / Europ. J. Agronomy 80 (2016) 88–104 97 y = -3E-06x2 + 0.0157x + 23.43 (r ² = 0.92; p<0.0001) 10 15 20 25 30 35 40 45 50 55 0 10 00 20 00 3000 40 00 K ( g k g -1 ) (a) y = 0.0005x + 9.94 (r² = 0.96; p<0.0001) 6 7 8 9 10 11 12 13 14 0 10 00 20 00 30 00 40 00 C a ( g k g -1 ) (b) y = -6E-08x2 + 0. 0006x + 2.7 727 (r ² = 0.9 9; p<0. 0001) 2 3 4 5 0 10 00 20 00 30 00 40 00 M g (g k g -1 ) Limesto ne (kg ha-1) (c) y = -2E-07x2 + 0.0011x + 4.484 (r ² = 0.99; p<0.0001) 3 4 5 6 7 0 10 00 20 00 30 00 4000 S ( g k g -1 ) Limestone (kg ha-1) (d) Fig. 4. (a) Potassium, (b) calcium, (c) magnesium and (d) sulfur concentrations in white oat flag leaves as affected by surface-applied dolomitic limestone rates in a long-term no-till soil. Bars indicate the standard error at each lime rate (n = 8; four replicates × two growing seasons). Table 4 Influence of surface-applied limestone rates on crop nutrition, shoot dry matter, yield components, and white oat grain yield in a long-term no-till soil and ANOVA significance. Growing season N P K Ca Mg S Shoot dry matter g kg−1 kg ha−1 2005 37.4 a1 3.5 b 29.8 b 10.1 b 2.6 b 5.6 a 5574 a 2006 34.5 b 4.9 a 40.4 a 11.7 a 4.3 a 5.6 a 5011 b ANOVA (F probability) Blocks 0.5462 0.7241 0.6611 0.2412 0.1960 0.1209 0.1082 Limestone rates (R) 0.6083 0.2619 <0.0001 0.0006 0.0288 <0.0001 0.0002 Growing season (S) 0.0450 <0.0001 <0.0001 <0.0001 <0.0001 0.9354 0.0001 R × S 0.9891 0.8000 0.4311 0.7977 0.8291 0.5531 0.9928 Growing season Panicles per square meter Spikelet per panicles Spikelet fertility 1000-grain weight Grain yield n◦ n◦ % g kg ha−1 2005 296 b1 37 b 95 a 21.3 a 2197 b 2006 336 a 52 a 94 a 20.4 a 3292 a ANOVA (F probability) Blocks 0.1264 0.7143 0.7789 0.2137 0.3943 Limestone rates (R) 0.0024 0.0470 0.0865 0.1210 0.0002 test (p f t s s l a p ( Growing season (S) 0.0010 < 0.0001 R × S 0.6818 0.7879 1 Means followed by different letters in the column differ statistically by the LSD rom 12 months onwards. Thus, there is not always an inverse rela- ionship between potential acidity and pH. Due to the mineralogical attributes of Oxisols, phosphate is trongly adsorbed on Al- and Fe-(oxy) hydroxide surfaces, irre- pective of the nature of the charges. However, the effect of the ime reaction on increasing phosphorus availability (Table 2) can be ttributed to the competitive adsorption between OH− and phos- hate for the same site, resulting in lower P adsorption by oxides Sato and Comerford, 2005). In addition, Haynes (1984) reported 0.6527 0.0641 < 0.0001 0.8995 0.3671 0.3604 < 0.05). that the anion repulsion effect by increasing negative charges on oxides could also contribute to P bioavailability. The influence of liming on the availability of phosphorus in acid soils has also been reported by other researchers (Haynes, 1982; Jaskulska et al., 2014); however, the increased availability in the subsurface layers is notable because phosphorus generally shows limited mobility in soil, and increasing its availability contributes greatly to increasing its interception and absorption by plant roots. 98 C.A.C. Crusciol et al. / Europ. J. Agronomy 80 (2016) 88–104 Table 5 Influence of surface-applied limestone rates on crop nutrition, shoot dry matter, yield components, and maize grain yield in a long-term no-till soil and ANOVA significance. Growing season N P K Ca Mg S Shoot dry matter g kg−1 kg ha−1 2006/2007 28 b1 2.3 b 24 b 3.5 b 2.9 b 1.7 b 14235 b 2007/2008 32 a 2.5 a 27 a 3.8 a 2.0 a 1.9 a 20174 a ANOVA (F probability) Blocks 0.4528 0.2610 0.7628 0.6299 0.3422 0.6890 0.3432 Limestone rates (R) <0.0001 0.8208 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 Growing season (S) <0.0001 0.0137 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 R × S 0.4632 0.9999 0.7264 0.1230 0.6977 0.4298 0.6815 Growing season Final population Ear per plant Grains per ear 100-grain weight Grain yield n◦ n◦ n◦ g kg ha−1 2006/2007 63350 a1 0.80 b 348 b 36 b 6090 b 2007/2008 60332 b 0.84 a 435 a 40 a 8632 a ANOVA (F probability) Blocks 0.2683 0.3282 0.3318 0.1170 0.1152 Limestone rates (R) <0.0001 0.0711 <0.0001 0.5514 <0.0001 Growing season (S) <0.0001 <0.0001 <0.0001 0.0019 <0.0001 R × S 0.3629 0.9999 0.0808 0.9999 0.4551 1 Means followed by different letters in the column differ statistically by the LSD test (p < 0.05). Table 6 Influence of surface-applied limestone rates on forage dry matter yield (FDMP), forage crude protein concentration and estimated meat production in a pasture of palisadegrass in a long-term no-till soil and ANOVA significance. FDMP (kg ha−1) Crude protein (%) Estimated meat production (kg ha−1)� First cut2 Second cut2 First cut2 Second cut2 First cut2 Second cut2 Total Growing season 2007 4127 b1 4986 b 11.0 a 10.3 a 199.0 b 225.1 b 424.1 b 2008 5447 a 6581 a 10.8 b 10.1 b 266.5 a 298.9 a 565.4 a ANOVA (F probability) Blocks 0.8675 0.6870 0.9745 0.5891 0.4025 0.6201 0.7001 Limestone rates (R) <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 Growing season (S) <0.0001 <0.0001 0.0037 0.0440 <0.0001 <0.0001 <0.0001 R × S 0.0281 0.0242 0.9921 0.9858 0.0059 0.0002 <0.0001 � Estimated meat production = kg of body weight gain (cattle) per ha (estimated) × 52% of carcass yield. 1 Means followed by different letters in the column differ statistically by the LSD test (p < 0.05). 2 First and second cuts in June and August, respectively. Table 7 Economic evaluation of the peanut, white oat, maize intercropped with palisadegrass and pasture as a function of the surface-applied dolomitic limestone rates in a long-term no-till soil. Limestone rates (kg ha−1) Peanut White oat Peanut White oat Maize Pasture (meat) Maize Pasture (meat) Total4 Mean5 2004/2005 2005 2005/2006 2006 2006/2007 2007 2007/2008 2008 – – Cost1 (D ha−1) 0 726 206 726 206 576 131 576 131 3278 820 1000 752 206 726 206 576 212 576 215 3470 868 2000 779 206 726 206 576 328 576 344 3741 935 4000 832 206 726 206 576 288 576 300 3709 927 Revenue2 (D ha−1) 0 513 137 709 205 708 518 1018 669 4476 1119 1000 628 181 868 271 784 838 1127 1098 5794 1449 2000 728 233 1005 349 1002 1295 1442 1752 7805 1951 4000 885 176 1222 265 925 1135 1331 1528 7465 1866 Net profit3 (D ha−1) 0 −213 −69 −17 −1 131 386 442 538 1198 299 1000 −124 −25 142 65 207 625 551 882 2324 581 2000 −51 27 279 143 426 967 866 1408 4064 1016 4000 53 −30 496 59 349 847 755 1228 3756 939 1 Mean costs and production costs of crops; the only difference was the dolomitic limestone rates used before the peanut crop (November 2004) and pasture costs as a function of the animal stocking rate. 2 Revenue = kg of peanut, white oat and maize grain yields and estimated meat production per ha × D 0.28, D 0.08, D 0.14 and D 2.23, respectively. 3 Net profit is the realization per ha, which was calculated using the following formula: revenue – cost. 4 Total = sum of all growing seasons. 5 Mean = mean by growing season. C.A.C. Crusciol et al. / Europ. J. Agronomy 80 (2016) 88–104 99 y = -0.0002x2 + 0.81x + 483 1 (r ² = 0.99; p<0.0001) 3000 3500 4000 4500 5000 5500 6000 6500 0 10 00 20 00 30 00 4000 S h o o t d ry m a tt e r (k g h a -1 ) (a) y = -1E-05x2 + 0.055x + 281.5 (r ² = 0.95; p=0.0016) 200 240 280 320 360 400 0 1000 2000 3000 4000 N o . o f p a n ic le m -2 (b) y = -1E-06x2 + 0.007x + 39.78 (r ² = 0.99; p<0.0001) 30 35 40 45 50 55 0 10 00 20 00 30 00 40 00 N o . o f sp ik e le ts p a n ic le - 1 Limestone (kg ha-1) (c) y = -0.0 003x2 + 1.2 x + 1991 (r² = 0.9 3; p<0.0 001) 1500 2000 2500 3000 3500 4000 0 10 00 2000 3000 4000 G ra in y ie ld ( k g h a -1 ) Limestone (kg ha-1) (d) F of sp d r at ea a i e t 1 t a l o l t t i C H o d c c ( a m ( ig. 5. (a) Shoot dry matter, (b) number of panicles per square meter, (c) number olomitic limestone rates in a long-term no-till soil. Bars indicate the standard erro The K levels increased in the upper soil layers (0–0.05, 0.05–0.10 nd 0.10–0.20 m) with liming after 12 months (Table 2). This ncrease can be explained by the increase in the soil pH, which nhanced K adsorption capacity by the soil, reduced K losses hrough leaching, and improved the K fertilizer efficiency (Krause, 965). In a relatively short period, this effect was not observed in he deeper layers, possibly due to the lower lime mobility; however, fter 24 months, liming increased K availability in all soil profiles, ikely in response to an enhanced sorptive complex. Regarding the increases in Ca2+ and Mg2+ availability (Table 2) bserved in all layers after 12 months, it should be considered that imestone is a major source for the replacement of these nutrients in he soil. Several researchers have reported the fundamental role of his corrective process as an important Ca2+ and Mg2+ complement n cropping systems (Caires et al., 2005, 2008a,b, 2015; Soratto and rusciol, 2008a; Fageria et al., 2010; Castro and Crusciol, 2013b). owever, the present study emphasizes the increase in the levels f these nutrients in the subsoil, with significant increases observed own to a depth of 0.60 m. The displacement of exchangeable ations in the soil profile may be related to the formation of stable omplexes between Ca2+ and Mg2+ and soluble organic compounds Franchini et al., 1999), although this mechanism is not common, nd to the presence and quantity of porous channels that allow the ovement of limestone downward through water displacement Amaral et al., 2004). ikelets per panicle and (d) grain yield of white oat as affected by surface-applied ch lime rate (n = 8; four replicates × two growing seasons). We highlight that after 24 months, there was a small increase in Ca2+ levels compared to the first sampling (12 months). This phe- nomenon was most likely related to the reaction of some portion of the applied limestone in the period between samplings. Further- more, the low nutrient export by the grains may have contributed to this increase. The increase in base saturation in all of the evaluated layers was the effect of the reduction in acidity and the increased Ca and Mg levels, as evidenced by Ciotta et al. (2004). 4.2. Peanut, white oat, and maize nutrition The highest N concentrations observed in the peanut and maize leaves (Figs. 2a and 6a) were probably related to the effects of liming on soil nitrate availability, as nitrate is one of the main forms of N absorbed by plants. The availability of nitrate may increase with an increasing pH due to liming because nitrification activity is lower at acidic pH (Islam et al., 2006) and explains the increased nitrate concentration observed in soils with acidity correction (Silva and Vale, 2000). Observed N concentration in maize was within the range con- sidered adequate for peanuts and maize (Ambrosano et al., 1997; Cantarella et al., 1997), independent of the lime rate applied. In the white oat crop, N concentrations were not influenced by lim- ing (Table 3), but the N concentration was above that considered adequate (Cantarella et al., 1997). 100 C.A.C. Crusciol et al. / Europ. J. Agronomy 80 (2016) 88–104 y = -5E-07x2 + 0.0026x + 27.68 (r ² = 0.85; p<0.0001) 27 28 29 30 31 32 33 0 1000 2000 3000 4000 N ( g k g -1 ) (a) y = -2E-07x2 + 0.0017x + 23. 115 (r ² = 0.97; p<0.0001) 22 23 24 25 26 27 28 0 10 00 20 00 30 00 40 00 K ( g k g -1 ) (b) y = 0.0004x + 2.2 (r² = 0.96; p<0.0001) 1 2 3 4 5 0 10 00 20 00 30 00 40 00 M g ( g k g -1 ) (d)y = 0.0005x + 2.69 (r² = 0.98; p<0.0001) 1 2 3 4 5 6 0 1000 20 00 30 00 40 00 C a ( g k g -1 ) (c) y = -5E-08x2 + 0.0 003x + 1. 52 (r ² = 0.9 2; p< 0.00 01) 1.0 1.5 2.0 2.5 0 1000 2000 3000 4000 S ( g k g -1 ) Limestone (kg ha-1) (e) y = -0. 0007x2 + 3.4x + 14757 (r ² = 0.76; p<0.0001) 12000 14000 16000 18000 20000 22000 0 10 00 20 00 3000 40 00 S h o o t d ry m a tt e r (k g h a -1 ) Limestone (kg ha-1) (f) F tratio b e the c F c P t t s c i c c a ( ig. 6. (a) Nitrogen, (b) potassium, (c) calcium, (d) magnesium, and (e) sulfur concen y surface-applied dolomitic limestone rates in a long-term no-till soil. Bars indicat The P concentration was affected by liming only in the peanut rop (Fig. 2b); this effect may be related to the lower P adsorbed by e- and Al-(oxy) hydroxides. In other words, the recent lime appli- ation increased P bioavailability in the upper soil layers, increasing utilization by peanut plants grown after application. In addition, he improved chemical conditions due to liming may have favored he root architecture, increasing plants’ ability to extract P from oil. Considering these aspects, it was notable that the highest P oncentration in peanut leaves was observed in the second grow- ng season (Table 3), and this value was considered to be above the ritical range proposed by Ambrosano et al. (1997). In the other rops, despite the increase in soil P bioavailability due to limestone pplication, the P concentrations in plants remained unaffected Tables 4 and 5). However, it is important to note that the P con- ns in maize leaf collected from the base of the ear and (f) shoot dry matter as affected standard error at each lime rate (n = 8; four replicates × two growing seasons). centrations in white oat and maize leaves were within the range that is considered sufficient (Cantarella et al., 1997). The increases in K, Ca, Mg and S levels observed in peanut (Fig. 2c–f), white oat (Fig. 4a–d) and maize (Fig. 6b–e) leaves as a function of liming were related to the greater availability of these macronutrients in the soil (Table 2). The benefits of liming to shoot dry matter production (peanuts, white oat and maize) (Figs. 3a, 5a, and 6f, respectively) reflect improved soil chemical characteristics (Table 2). The increased shoot growth probably resulted from bet- ter root development, which increased the absorption capacity of nutrients and water. Caires et al. (2006b) confirmed the existence of a correlation between wheat (Triticum spp.) root growth and soil chemical properties, mainly regarding an increased pH, increased Ca2+ availability, increased base saturation, and reduced Al3+. C.A.C. Crusciol et al. / Europ. J. Agronomy 80 (2016) 88–104 101 y = -0.0003x2 + 1.87x + 60224 (r ² = 0.98; p<0.0001) 59500 60000 60500 61000 61500 62000 62500 63000 63500 0 10 00 20 00 30 00 40 00 N o . o f p la n ts h a-1 (a) y = -6E-06x2 + 0.0471x + 340.6 (r ² = 0.96; p<0.0001) 300 320 340 360 380 400 420 440 460 480 0 1000 2000 3000 4000 N o . o f g ra in e ar -1 (b) y = -0.0003x2 + 1.7 724x + 5874 (r ² = 0.8 5; p<0. 0001) 5000 6000 7000 8000 9000 10000 0 1000 2000 3000 4000 G ra in y ie ld ( k g h a-1 ) Limestone (kg ha-1) (c) Fig. 7. (a) Number of plants per area, (b) number of grains per ear, and (c) grain yield o t g 4 y t a e t b e s ( ( y = -0.0003x2 + 1.9990x + 2,430.5 (r² = 0.9463; p<0.0001) y = -0.0005x2 + 2.7501x + 2,785.6 (r² = 0.9519; p<0.0001) 0 2000 4000 6000 8000 10000 0 10 00 20 00 30 00 40 00 F o ra g e d ry m at te r y ie ld ( k g h a-1 ) ___ First Growing Season _ _ Second Growing Season First Cut y = -0.0005x2 + 2.6384 x + 3,208.3 (r² = 0.9463; p<0.0001) y = -0.0007x2 + 3.6297 x + 3,677.2 (r² = 0.9520; p<0.0001) 0 2000 4000 6000 8000 10000 0 10 00 20 00 30 00 40 00 F o ra g e d ry m at te r y ie ld ( k g h a-1 ) ___ First Gro wing Season _ _ Sec ond Gro wing Sea son Second Cut y = -4E-07x2 + 0.0024 x + 8.7328 (r² = 0.9275; p<0.0001) y = -4E-07x2 + 0.0022 x + 8.2242 (r² = 0.9285; p<0.0001) 0 3 6 9 12 15 0 10 00 20 00 30 00 40 00 C ru d e p ro te in ( % ) Limestone (kg ha-1) ___ First Cut _ _ Second Cut Fig. 8. Forage dry matter yield and crude protein content in a pasture of palisade- grass in two cuts and two growing seasons as affected by surface-applied dolomitic f maize as affected by surface-applied dolomitic limestone rates in a long-term no- ill soil. Bars indicate the standard error at each lime rate (n = 8; four replicates × two rowing seasons). .3. Peanut, white oat, and maize components and kernel/grain ields The positive results of surface liming on yield components and he kernel/grain yield (peanuts, white oat and maize) (Figs. 3, 5, nd 7) were the result of reduced acidity and increased soil nutri- nt availability (Table 2). These results are in accordance with hose observed by Caires et al. (2000), who reported correlations etween cumulative grain production and soil chemical properties, specially regarding increases in pH, exchangeable Ca2+, and base aturation as well as reductions in exchangeable Al3+. The increase in white oat grain yields observed after liming Fig. 5d) was similar to the results presented by Castro and Crusciol 2013b), who reported positive effects on white oat yield compo- limestone rates in a long-term no-till soil. Bars indicate the standard error at each lime rate (n = 4; four replicates – forage dry matter production; n = 8; four repli- cates × two growing seasons – crude protein). nents due to reduced acidity and also minimizes the harmful effects of acidity on crop development and thereby increases the number of panicles per area and the number of spikelets per panicle, with significant effects on the grain yield. As observed for the other species, the significant increases in the maize yield are justified by the chemical changes brought about by liming (Table 2). Castro and Crusciol (2013a,b) also demonstrated the benefits of these changes on the yield components responsible for determining the grain yield. According to Caires et al. (2000), the morphological parameters of maize roots are modified by superfi- cial lime application in NT. These authors observed a reduction in the relative root length in the soil surface layer (0–0.10 m) and an increase in the subsoil (0.20–0.60 m) as a function of increasing doses of liming. In other words, in higher-acidity conditions, the maize root system is restricted to the surface, which makes plants vulnerable to water deficits. Similar effects may have occurred dur- 102 C.A.C. Crusciol et al. / Europ. J. Ag y = -2E-05x2 + 0. 1103 x + 10 5.1 (r² = 0.9 379; p <0.0 001) y = -3E-05x2 + 0.1506 x + 138.43 (r² = 0.9407; p<0.0001) 0 100 200 300 400 500 0 10 00 20 00 30 00 40 00 E st im at ed m ea t p ro d u ct io n ( k g h a-1 ) ___ First Gro wing Sea son _ _ Sec ond Gro wing Sea son First Cut y = -3E-05x2 + 0.145 x + 108.67 (r² = 0.9469; p<0.0001) y = -4E-05x2 + 0.2024 x + 134.49 (r² = 0.9317; p<0.0001) 0 100 200 300 400 500 0 10 00 20 00 30 00 40 00 E st im at ed m ea t p ro d u ct io n ( k g h a-1 ) ___ First Gro wing Sea son _ _ Second Gro wing Sea son Second Cut y = -5E-05x2 + 0.2552 x + 213.77 (r² = 0.9428; p<0.0001) y = -6E-05x2 + 0.353 x + 272.91 (r² = 0.9357; p<0.0001) 0 200 400 600 800 1000 0 10 00 20 00 30 00 40 00 E st im at ed m ea t p ro d u ct io n ( k g h a-1 ) Limestone (kg ha-1) ___ First Gro wing Season _ _ Second Gro wing Season Total Fig. 9. Estimated meat production in a pasture of palisade grass in two cuts and total in two growing seasons as affected by surface-applied dolomitic limestone rates in a r i 2 o y t ( m i e o s i t o r o compared to white oat. Thus, 232–785 kg of meat per ha could be long-term no-till soil. Bars indicate the standard error at each lime rate (n = 4; four eplicates). ng maize development because dry spells occurred in February 007 and 2008 (Fig. 1c and d). However, attention should be paid to the use of higher doses f lime, which caused reductions in white oat and maize grain ields in this study (Figs. 5d and 7c). These results are similar to hose reported by Caires et al. (2000) and Soratto and Crusciol 2008b), who attributed this effect to the lower availability of some icronutrients, especially cationic micronutrients, whose solubil- ty decreases with an increasing pH. We note that the lime rates stimated to achieve the maximum grain yield, especially in white at and maize, were very close to those calculated to adjust the base aturation of a soil sample collected at the 0–0.20 m depth to 70%, ndicating that this surface liming recommendation is effective for he studied tropical no-till soil. The higher kernel/grain yield (peanut, white oat and maize) bserved in the second growing season (Tables 3–5) may have esulted from soil and weather conditions during the plants’ devel- pment, which greatly affected crop productivity (Fig. 1). ronomy 80 (2016) 88–104 4.4. Forage characteristics and estimated meat production The forage dry matter yield and estimated meat production were highest in the second growing season (2008) compared to the first growing season (2007) (Table 6 and Fig. 8); as a function of the stim- ulus from tilling after the first cut, the forage dry matter yield was higher during the second cut in both growing seasons. The low tem- peratures and low rainfall, mainly during August and September in the first growing season (2007), contributed to the lower forage dry matter yield. According to Costa et al. (2005), the optimal temper- ature range for palisade grass development was between 30 and 35 ◦C, and its growth was greatly reduced between 10 and 15 ◦C. In addition, Costa et al. (2005) reported that low rainfall, which is characteristic of regions with dry winters, such as the Brazilian Cer- rado during June and July, is another cause of the reduced palisade grass development observed in our trial for the first cutting. The observed two-year-average crude protein concentration of approx- imately 8.2–12.5% is higher than the crude protein of 7% reported by van Soest (1994) as the minimum concentration required for maintaining microbial populations in the rumen of cattle. The forage dry matter yield (2573–6642 kg ha−1 in the first cut and 3397–8767 kg ha−1 in the second cut) in this study (Table 6; Fig. 8) can be considered high during this season (winter/spring). The forage dry matter yield can be used as an index for mechanical cutting or in the fields for grazing by animals (Pariz et al., 2011b) and to increase the estimated meat production (Fig. 9) and rev- enue of the farmer. Typically, during this time of the year (June to September), the availability of forage in areas with dry winters is limited (Borghi et al., 2013). Sowing tropical forage after a maize grain harvest does not provide sufficient fodder during the autumn, winter and part of the spring in regions with dry winters, such as the Brazilian Cerrado or African Savanna. However, in this inter- cropping system, the rain that falls after the maize is harvested (in April and May) allows for adequate development of palisade grass. The forage dry matter yield, crude protein concentrations and estimated meat production increased quadratically as a function of the lime rate (Figs. 8 and 9). This result indicates that when low rates of surface limestone are used for maize intercropped with palisade grass, the forage dry matter yield is also low in the winter/spring, consequently reducing the estimated meat production. In the sub- tropical Brazilian regions, the surface limestone (application or reapplication) in an integrated crop-livestock system (soybean- beef cattle) also increased the forage dry matter yield [mix of black oat + Italian ryegrass (Lolium multiflorum)] and reduced the long- term soil acidification, with a higher base saturation and lower aluminum saturation, mainly in the grazed areas compared to the non-grazed areas (Martins et al., 2014a, 2014b, 2016). 4.5. Economic evaluation The surface application of no dolomitic limestone resulted in a negative net profit for peanut and white oat in both growing seasons (Table 7). This result demonstrates the importance of the limestone practice in an annual crop rotation of tropical pastures under no-till. All treatments resulted in a positive total profit and a mean net profit, mainly using 2000 kg ha−1 of limestone. An integrated crop-livestock system using maize intercropped with palisade grass is a good option in a tropical agricultural system because in addition to maize grain produce in summer/autumn, the farmers can use the forage dry matter yield of palisade grass (Table 6 and Fig. 8) for animal fodder in winter/spring. The pas- ture of palisade grass in winter/spring provided a higher net profit produced in both cuts (Fig. 9), with net profits of D 386–1408 per ha, depending on the surface limestone rate applied-reapplied in the crop rotation (a rate of 2000 kg ha−1 of limestone results in higher . J. Ag n m a p i i 5 e o s t a u n o m o r d t p c f o A t I g fi f f R A A A A A A B B B C.A.C. Crusciol et al. / Europ et profits, as a function of higher forage dry matter and estimated eat production). Therefore, our data indicated that an annual crop rotation of tropical pasture under no-till using maize intercropped with alisade grass is a promising option for farmers and liming can mprove crop-livestock systems involving the crop rotation stud- ed. . Conclusions The surface application of limestone in a tropical no-till soil ffectively reduced soil acidity from the surface down to a depth f 0.60 m, resulting in greater availability of P and K near the soil urface. The Ca and Mg availability in the soil also increased with he limestone application rates up to a depth of 0.60 m. Nutrient bsorption was enhanced by liming, especially regarding the plant ptake of K, Ca and Mg. Significant increases in the yield compo- ents and kernel/grain yields of peanuts, white oat and maize were btained with surface application of limestone. The lime rates esti- ated to achieve the maximum grain yield, especially in white at and maize, were very close to the rates that are necessary to aise the base saturation of a soil sample collected at the 0–0.20 m epth to 70%. This lime rate also increases the forage dry mat- er yield, the crude protein concentration and the estimated meat roduction during winter/spring in the maize-palisade grass inter- ropping, provides the highest total and mean net profit during the our growing seasons and can improve the long-term sustainability f tropical agriculture in the Brazilian Cerrado. cknowledgements The authors would like to thank the São Paulo Research Founda- ion (FAPESP, grant #2003/09914-3) and the Coordination for the mprovement of Higher Education Personnel (CAPES, PROAP – Pro- ram to Support Graduate) for financial support. In addition, the rst and fourth authors would like to thank the National Council or Scientific and Technological Development (CNPq) for an award or excellence in research. eferences grolink, 2016. Quotations to Farming, Available at: www.agrolink.com.br (accessed 05.02.16.). lford, C.M., Krall, J.M., Miller, S.D., 2003. 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