Association of morphological and water factors with irrigated forage cactus yield/Associacao de fatores morfologicos e hidricos com a produtividade da palma forrageira irrigada.
Forage cactus has different varieties belonging to the genera Opuntia and Nopalea, and, despite having the same photosynthetic process, the crassulacean acid metabolism (CAM), has characteristics that differentiate them, such as morphology and, thus, water use efficiency (Silva et al., 2014a, 2015).
Different from [C.sub.3] and [C.sub.4] plants, CAM species open their stomata at night to capture the C[O.sub.2] necessary for their metabolism, which reduces water transfer to the atmosphere, i.e., evapotranspiration (ET). The ET varies between species, at the same time is affected by the crop management (Allen et al., 1998).
Studies conducted with forage cactus demonstrate that, although it tolerates extreme drought conditions, the application of supplementary irrigation events favors the productive performance, increasing the availability of food in semi-arid regions, especially in the drought period (Flores-Hernandez et al., 2004; Queiroz et al., 2015).
The contribution of morphological and environmental characteristics to the yield of forage cactus clones has been cited in the literature (Silva et al., 2010, 2015; Pinheiro et al., 2014), but it does not contemplate irrigated cultivation and does not meet water parameters, such as actual crop evapotranspiration.
Hence, the association of water-productive factors of the forage cactus planting system with morphological characteristics can establish new indicators of indirect selection of clones that are more productive and with higher water use efficiency for the different sites or that have wide interannual seasonality of rainfalls (Neder et al., 2013; Pinheiro et al., 2014).
This study aimed to understand the association of morphological and water factors of forage cactus clones with productive capacity under different water regimes.
Material and Methods
Morphological, water and production data of forage cactus were collected in an experimental area (7[degrees]56'20"S; 38[degrees]17'31" W; 498 m) of the Agronomic Institute of Pernambuco, in Serra Talhada, PE, Brazilian semi-arid region, conducted between the years 2012 and 2013. The soil of the area was classified as Red Yellow Argisol.
The studied clones were IPA-Sertania--IPA (Napolea cochenillifera (L.) Salm. Dick), Miuda--MIU (Napolea cochenillifera (L.) Salm. Dick) and Orelha de Elefante Mexicana --OEM (Opuntia stricta (Haw.) Haw.) planted in February 2010, at spacing of 1.6 x 0.2 m, with the crop rows along contour lines. The clones were arranged in a randomized block design, in 3 x 3 x 3 + 3 factorial scheme, with three replicates, plus one control for each clone. Until March 2012, the clones were maintained under rainfed conditions, when plants were harvested, leaving only the basal cladode. From this date on, the data started to be collected for the present study, which ended in August 2013, totaling 532 days.
In this period, irrigation events were applied using a drip system (drippers spaced by 0.20 m), adopting three fixed water depths (L) for water replacement (2.5, 5.0 and 7.5 mm, plots) with three frequencies (F) (7, 14 and 28 days, subplots). The three clones (IPA, MIU and OEM) formed the sub-subplots. Each one was composed of four rows of 20 plants, with total area of 25.6 [m.sup.2] and evaluation area of 11.52 [m.sup.2]. Cultivation practices were performed along the vegetative cycle of the crop.
Irrigation depths and frequencies were established based on information surveyed in areas of forage cactus producers of the Rio Grande do Norte state, which irrigated the crop with events of 5.0 mm every 14 days.
The three forage cactus clones received the equivalent to (water supply): 756 (L7.5 F7), 672 (L5.0 F7), 622 (L7.5 F14), 586 (L2.5 F7), 579 (L5.0 F14), 555 (L7.5 F28), 536 (L2.5 F14), 535 (L5.0 F28), 514 (L2.5 F28) and 493 (Control) mm [year.sup.-1].
At harvest, the measurements of the cladodes (CT thickness, CL--length, CW--width, CP--perimeter and CA--cladode area per order of appearance: B--basal; 1--first order; 2--second order; 3--third order) and of the plant (TNC--total number of cladodes of the plant per order of appearance NC1, NC2 and NC3, PH--height and PW--width of the plant, CAI--cladode area index) were obtained through biometric campaign. The adopted procedures were described in Pinheiro et al. (2014) and Silva et al. (2014b, 2015).
Subsequently, all plants of the evaluation area were harvested and weighed to determine the yield per sub-subplot. Five representative cladodes per sub-subplot were sampled to obtain the individual fresh biomass (CFB, g [cladode.sup.-1]) and, after that, for dry biomass (CDB, g [cladode.sup.-1]), the cladodes were dried in an oven at 65 [degrees]C, until constant weight. The ratio between CDB and CFB generated the dry matter content (DMC, %). The data of yield and final plant density were used to calculate the fresh matter yield (FMY, t [ha.sup.-1]). The product between DMC and FMY resulted in dry matter yield (DMY, t [ha.sup.-1]).
The actual evapotranspiration of the clones was calculated over time through the data of water content and physicalhydraulic properties of the soil, according to Silva et al. (2014a). In addition, the soil water balance (SWB) method was used for a control volume with depth of 0.70 m, in intervals of 14 days (ET = P + I [+ or -] Q [+ or -] [DELTA]WS, where P--pluviometric precipitation, I--irrigation, Q--vertical water flow in the soil, represented by capillary rise and deep drainage, and [DELTA]WS--water storage variation in the soil, all in mm).
The data of soil water content were monitored using a capacitive sensor (Diviner 2000[R], Sentek Pty Ltda., Australia). This sensor was inserted in access tubes installed at depth of 0.90 and 0.10 m away from the crop row, in each sub-subplot. The sensor was calibrated locally, as described by Araujo Primo et al. (2015).
ET values were related to the data of reference evapotranspiration (ETo) to obtain the ET/ETo ratio. ETo was estimated through the Penman-Monteith method (Allen et al., 1998), using meteorological data of an automatic station of the National Institute of Meteorology--INMET, situated close to the area. At the end of the cycle, ET values were integrated (ZET).
All data were subjected to the Lilliefors test, to verify normality, and subdivided into four groups: plant morphology --"Plant" (PH, PW, TNC, NC1, etc.); Cladode morphology--"Cladodes" (CLB, CL1, CL2, etc.); evapotranspiration "ET" (ET and ET/ETo) and "YIELD" (CFB, CDB, FMY, DMY and DMC). The latter was considered as response group and the others as explicative groups.
The response variables ("YIELD") were correlated with the explicative variables ("Plant", "Cladodes" and "ET"), through the Pearson's correlation matrix (Cunha et al., 2011). Subsequently, multicollinearity test was applied to the data to detect if the variables within the same group were highly correlated. Then, variables that fitted this condition were removed from the data set as suggested by Toebe & Cargnelutti Filho (2013). Lastly, canonical analysis was applied to evaluate the association between explicative and response groups, and path analysis was applied, in the partitioning of the Pearson's correlation coefficient, to identify its direct and indirect effects (Cunha et al., 2011). The significance of the partial correlation was made through the chi-square test, at 0.01 probability level. All analyses were made in the statistical program "GENES" (Cruz, 2006).
RESULTS AND DISCUSSION
There was no correlation of water supply and actual evapotranspiration of forage cactus (ET and ET/ETo) with crop yield (p > 0.05).
The increment of forage cactus production did not respond directly to the increase in soil water supply, which varied between 493 and 756 mm [year.sup.-1]. Hence, the morphological indicators of identification of the most productive clones are not dependent on the water regime of the cultivation environment.
Flores-Hernandez et al. (2004) and Queiroz et al. (2015), applying increasing water depths from 760 to 1380 mm [year.sup.-1] and from 976 to 1202 mm [year.sup.-1], respectively, also did not find response of forage cactus production. Queiroz et al. (2015) cite that, under full water availability, the immediate response of the forage cactus may not be observed due to its high capacity of water storage in the cladodes, low water demand and reduced conversion into dry matter. Thus, Scalisi et al. (2016) suggest that controlled reductions in the irrigation depths may not affect the biomass of the plants.
The ET of the clones showed no correlation with their yields, which is justified by the crassulacean mechanism of forage cactus, which, besides minimizing transpiration, increases the importance of the evaporation component (Han & Felker, 1997) so that the increment in ET does not increase yield proportionally.
The morphological factors showed the highest correlation with the productive expression of the clones.
Significant correlation coefficients for the clones IPA and OEM were obtained of the production variables (IPA: DMY, DMC and CFB; OEM: DMY, DMC, CFB and CDB) with plant morphology (IPA: PW, NC2 and NC3; OEM: PW, NC1 and NC2) and cladode morphology (IPA: CL2, CTB, CT3 and CP2; OEM: CLB, CL1, CW1, CTB, CT2 and CAB). Thus, canonical analysis was applied between these groups, per clone (IPA and OEM).
Only one production variable of the MIU showed correlation with PH and cladode morphology (CLB, CL2, CPB and CAB). Thus, the association between the groups "YIELD" and "Plant" was not evaluated and there was no significant canonical axis between the groups "YIELD" and "Cladode".
The association between the groups "YIELD" and "Cladode" (Table 1) of IPA demonstrated that plants with higher CTB and of third-order showed higher CFB, but in detriment of DMC. There was no significant canonical axis with the group "Plant", indicating that "YIELD" is independent of this group and the effects of their variables occurred separately.
For OEM (Table 1), two canonical axes were significant in the association between the groups "YIELD" and "Plant", and there was no correlation with the group "Cladode".
In the first axis, the highest DMY and DMC occurred in plants with greater dimensions of the basal cladodes (CAB). However, in this condition, there was a reduction in CFB and CDB, when plants with larger basal cladodes (CLB, CAB) also had greater dimensions of the first-order cladode (CL1), according to the second axis (0.994 *).
In the partitioning of the correlation coefficients through the path analysis, the NC3 showed the highest positive explanation for the CFB of IPA (Table 2), with direct and indirect effect (via NC2), followed by PW, explaining together 60.7% of its variability. In terms of cladode, the perimeter of those of second-order (CP2) contributed to the increment in DMY, reaching 60.3%.
In turn, the accumulation of CFB was favored in plants with larger second-order cladodes and thicker third-order cladodes, responsible for 89.1%. Basal cladodes with greater thickness resulted in plants with lower DMC.
For the clone IPA, the contribution of the number of third-order cladodes to the increment in fresh biomass may be related to the greater capture of C[O.sub.2], compared with those of lower orders (Liguori et al., 2013). In turn, plant width has been cited as good variable to be adopted in the selection of more-productive clones (Silva et al., 2010; Pinheiro et al., 2014).
For the clone Miuda, the highest DMY occurred in taller plants and was related to basal cladodes with larger areas (Table 3), either through direct or indirect effect (via CLB, CL2, CPB). These cladodes explained 88.7% of the variation in DMY. Larger basal cladodes are fundamental to guarantee plant support, since MIU has higher number of cladodes in comparison to IPA and OEM (Silva et al., 2015). Nevertheless, Cavalcante et al. (2014) highlight that MIU has lower biomass accumulation compared with the Opuntia clones.
The highest yield of OEM (DMY) was observed in plants with higher number and thickness of second-order cladodes (NC2 and CT2) and thicker basal cladodes (CTB) (Tables 4 and 5). Thus, there were increments in DMC and decrease in CFB, associated with lower first-order cladodes (CW1). Hence, the morphological characteristics of second-order cladodes and vigor of the basal cladode had the highest contributions to the yield of OEM.
Second-order cladodes are those in higher number in the clone OEM; thus, they contribute more to the final crop yield. On the other hand, the growth of these cladodes is expected to result in reduction of the dimensions of lower cladodes. Silva et al. (2010), in study conducted with 50 clones, observed that the basal and first-order cladodes showed the largest morphological dimensions since they support the plant; however, proportional reductions of these dimensions were observed with the appearance of cladodes of higher orders.
Regardless of the clone, the vigor of the basal cladode was determinant for crop yield. Pimienta-Barrios et al. (2005) observed decrease in the daily gain of C[O.sub.2] and reduction in the relative water content of these cladodes in dry periods, indicating water movement to the cladodes above. Thus, besides the function of support, basal cladodes become source of water and maintenance of the plant photosynthetic activity in drought periods.
1. The yield of forage cactus cultivated under water regimes from 493 to 756 mm [year.sup.-1] was more associated with the peculiarities of the morphological characteristics of its clones than with different water supplies or actual evapotranspiration.
2. The increase in water supply from 493 to 756 mm [year.sup.-1] did not promote significant increment of production for the forage cactus.
3. Regardless of water regime and clone, the vigor of the basal cladode was highly decisive for the expression of the productive capacity of irrigated forage cactus.
Ref. 147-2016--Received 12 Sep, 2016 * Accepted 24 Mar, 2017 * Published 25 Jul, 2017
To the Pernambuco Science and Technology Support Foundation (FACEPE), for the financial aid (APQ-02155.01/10) and to the Coordination for the Improvement of Higher Education Personnel (CAPES), for the scholarship.
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Marcela L. Barbosa (1), Thieres G. F. da Silva (2), Sergio Zolnier (1), Servulo M. S. e Silva (3), Jose E. F. de Morais (2) & Mery C. de S. Assis (2)
(1) Universidade Federal de Vicosa/Departamento de Engenharia Agricola. Vicosa, MG. E-mail: email@example.com (Corresponding author); firstname.lastname@example.org
(2) Universidade Federal Rural de Pernambuco/Unidade Academica de Serra Talhada. Serra Talhada, PE. E-mail: email@example.com; firstname.lastname@example.org; email@example.com
(3) Instituto Agronomico de Pernambuco. Arcoverde, PE. E-mail: firstname.lastname@example.org
Table 1. Canonical correlations between the groups "YIELD" and "Cladode" of IPA Sertania (IPA) and "YIELD" and "Plant" of Orelha de Elefante Mexicana (OEM) Groups Variables Canonical factors 1 2 3 4 IPA Sertania I--Yield DMY 0.013 0.850 -0.526 -- DMC -0.721 0.034 0.692 -- CFB 0.716 0.694 -0.081 -- II--Cladode CL2 0.431 0.833 -0.260 -- CTB 0.743 0.131 -0.633 -- CT3 0.801 0.433 0.334 -- CP2 0.004 0.704 -0.703 -- CC 0.995 ** 0.893 0.311 -- [chi square] 31 8 1 -- DF 12 6 2 -- Orelha de Elefante Mexicana I--Yield DMY 0.569 0.266 0.236 -0.741 DMC 0.539 -0.167 -0.589 0.578 CFB -0.402 0.651 0.496 -0.411 CDB 0.091 0.846 -0.477 -0.220 II--Plant CLB 0.202 -0.522 0.775 0.178 CL1 -0.239 -0.769 0.543 0.065 CW1 -0.177 0.302 0.781 -0.471 CTB 0.223 -0.311 0.178 -0.875 CT2 0.154 0.059 0.649 -0.607 CAB 0.389 -0.583 0.541 0.244 CC 0.999 * 0.994 * 0.951 0.849 [chi square] 76 28 13 4 DF 24 15 8 3 DMY--Dry matter yield, DMC--Dry matter content, CFB, CDB--Cladode fresh and dry biomass CLB, CL1, CL2--Length of basal, first- and second-order cladodes, CW1--Width of first-order cladode, CTB, CT2, CT3--Thickness of basal, second- and third-order cladodes, CP2 Perimeter of second-order cladode, CAB--Area of basal cladode. **, * Significant at 0.01 anc 0.05, respectively, by Chi-square test; CC--Canonical correlation, DF--Degrees of freedom Table 2. Partitioning of the correlation coefficient in direct and indirect effects between the variables of the groups "YIELD", "Plant" and "Cladode" of IPA Sertania (IPA) Variable Effect DMY DMC CFB "Plant" Group PW Direct effect PW -- -- 0.303 Indirect effect via NC2 -- -- 0.127 Indirect effect via NC3 -- -- 0.284 Total -- -- 0.714 NC2 Direct effect NC2 -- -- 0.149 Indirect effect via PW -- -- 0.258 Indirect effect via NC3 -- -- 0.298 Total -- -- 0.705 NC3 Direct effect NC3 -- -- 0.393 Indirect effect via PW -- -- 0.220 Indirect effect via NC2 -- -- 0.113 Total -- -- 0.725 [R.sup.2] -- -- 0.607 "Cladode" Group CL2 Direct effect CL2 0.324 -- 0.455 Indirect effect via CTB -0.278 -- 0.084 Indirect effect via CT3 0.138 -- 0.293 Indirect effect via CP2 0.496 -- -0.003 Total 0.680 -- 0.829 CTB Direct effect CTB -- -0.746 -- Indirect effect via CL2 -- 0.109 -- Indirect effect via CT3 -- -0.118 -- Indirect effect via CP2 -- 0.090 -- Total -- -0.665 -- CT3 Direct effect CT3 -- -- 0.522 Indirect effect via CL2 -- -- 0.256 Indirect effect via CTB -- -- 0.053 Indirect effect via CP2 -- -- 0.000 Total -- -- 0.830 CP2 Direct effect CP2 0.625 -- -- Indirect effect via CL2 0.257 -- -- Indirect effect via CTB -0.245 -- -- Indirect effect via CT3 0.012 -- -- Total 0.650 -- -- [R.sup.2] 0.603 0.561 0.891 DMY--Dry matter yield, DMC--Dry matter content, CFB--Cladode fresh biomass; PW--Plant width, NC2, NC3--Number of second- and third-order cladodes; CL2--Length of second-order cladode, CTB, CT3--Thickness of basal and third-order cladodes, CP2--Perimeter of second-order cladode. "-" Indicates that there was correlation between the variables Table 3. Partitioning of the correlation coefficient in direct and indirect effects between the variables of the groups "YIELD" and "Cladode" of Miuda (MIU) Variable Effect DMY CLB Direct effect CLB -0.082 Indirect effect via CL2 0.151 Indirect effect via CPB -0.265 Indirect effect via CAB 0.926 Total 0.730 CL2 Direct effect CL2 0.193 Indirect effect via CLB -0.064 Indirect effect via CPB -0.215 Indirect effect via CAB 0.735 Total 10.648 CPB Direct effect CPB -0.279 Indirect effect via CLB -0.078 Indirect effect via CL2 0.149 Indirect effect via CAB 0.887 Total 0.678 CAB Direct effect CAB 1.092 Indirect effect via CLB -0.070 Indirect effect via CL2 0.130 Indirect effect via CPB -0.227 Total 0.926 Determination coefficient 0.887 DMY--Dry matter yield; CLB, CL2--Length of basal and second -order cladodes, CPB -Perimeter of basal cladode, CAB--Area of basal cladode Table 4. Partitioning of the correlation coefficient in direct and indirect effects between the variables of the groups "YIELD" and "Plant" of Orelha de Elefante Mexicana (OEM) Variable Effect DMY DMC CFB CDB PW Direct effect PW -0.211 -- -- -- Indirect effect via NC1 -0.002 -- -- -- Indirect effect via NC2 -0.452 -- -- -- Total 0.661 -- -- -- NC1 Direct effect NC1 -- -- -- 0.726 Indirect effect via PW -- -- -- -0.005 Indirect effect via NC2 -- -- -- -0.037 Total -- -- -- 0.684 NC2 Direct effect NC2 -0.571 -- -- -- Indirect effect via PW 0.167 -- -- -- Indirect effect via NC1 -0.031 -- -- -- Total 0.769 -- -- -- Determination coefficient -0.612 -- -- 0.500 DMY--Dry matter yield, DMC--Dry matter content, CFB, CDB --Cladode fresh and dry biomass DW--Plant width, NC1, NC2--Number of first- and second-order cladodes; "-" Indicates that there was correlation between the variables Table 5. Partitioning of the correlation coefficient in direct and indirect effects between the variables of the groups "YIELD" and "Cladode" of Orelha de Elefante Mexicana (OEM) Variable Effect DMY DMC Direct effect CLB -- -- Indirect effect via CL1 -- -- Indirect effect via CW1 -- -- CLB Indirect effect via CTB -- -- Indirect effect via CT2 -- -- Indirect effect via CAB -- -- Total -- -- Direct effect CL1 -- -- Indirect effect via CLB -- -- Indirect effect via CW1 -- -- CL1 Indirect effect via CTB -- -- Indirect effect via CT2 -- -- Indirect effect via CAB -- -- Total -- -- Direct effect CW1 -- 0.765 Indirect effect via CLB -- 0.014 Indirect effect via CL1 -- 0.133 CW1 Indirect effect via CTB -- 0.005 Indirect effect via CT2 -- 0.018 Indirect effect via CAB -- 0.083 Total -- 0.815 Direct effect CTB 0.452 -- Indirect effect via CLB -0.009 -- Indirect effect via CL1 -0.148 -- CTB Indirect effect via CW1 -0.025 -- Indirect effect via CT2 0.267 -- Indirect effect via CAB -0.048 -- Total 0.635 -- Direct effect CT2 -0.456 -- Indirect effect via CLB -0.012 -- Indirect effect via CL1 -0.247 -- CT2 Indirect effect via CW1 0.044 -- Indirect effect via CTB -0.265 -- Indirect effect via CAB 0.125 -- Total -0.630 -- Direct effect CAB -- -- Indirect effect via CLB -- -- Indirect effect via CL1 -- -- CAB Indirect effect via CW1 -- -- Indirect effect via CTB -- -- Indirect effect via CT2 -- -- Total -- -- Determination coefficient -0.840 0.873 Variable Effect CFB CDB Direct effect CLB -- -0.383 Indirect effect via CL1 -- -0.554 Indirect effect via CW1 -- 0.064 CLB Indirect effect via CTB -- -0.023 Indirect effect via CT2 -- -0.040 Indirect effect via CAB -- 0.051 Total -- -0.805 Direct effect CL1 -- -0.757 Indirect effect via CLB -- -0.281 Indirect effect via CW1 -- 0.044 CL1 Indirect effect via CTB -- -0.016 Indirect effect via CT2 -- 0.035 Indirect effect via CAB -- -0.049 Total -- -0.926 Direct effect CW1 0.884 -- Indirect effect via CLB 0.004 -- Indirect effect via CL1 0.003 -- CW1 Indirect effect via CTB -0.129 -- Indirect effect via CT2 10.101 -- Indirect effect via CAB -0.065 -- Total 10.799 -- Direct effect CTB -- -- Indirect effect via CLB -- -- Indirect effect via CL1 -- -- CTB Indirect effect via CW1 -- -- Indirect effect via CT2 -- -- Indirect effect via CAB -- -- Total -- -- Direct effect CT2 -- -- Indirect effect via CLB -- -- Indirect effect via CL1 -- -- CT2 Indirect effect via CW1 -- -- Indirect effect via CTB -- -- Indirect effect via CAB -- -- Total -- -- Direct effect CAB -- 0.066 Indirect effect via CLB -- -0.298 Indirect effect via CL1 -- -0.559 CAB Indirect effect via CW1 -- -0.026 Indirect effect via CTB -- -0.008 Indirect effect via CT2 -- -0.028 Total -- -0.746 Determination coefficient 10.924 -0.956 DMY--Dry matter yield, DMC--Dry matter content, CFB, CDB--Cladode fresh and dry biomass CLB, CL1--Length of basal and first-order cladodes, CW1--Width of first-order cladode CTB, CT2--Thickness of basal and second-order cladodes, CAB--Area of basal cladode. "-" ndicates that there was correlation between the variables