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SOIL MICROBIOLOGICAL PROPERTIES AND ENZYME ACTIVITY IN AGROFORESTRY SYSTEMS COMPARED WITH MONOCULTURE, NATURAL REGENERATION, AND NATIVE CAATINGA PROPRIEDADES MICROBIOLÓGICAS E ATIVIDADE ENZIMÁTICA DO SOLO SOB SISTEMAS AGROFLORESTAIS COMPARADO COM MONOCULTURA, REGENERAÇÃO NATURAL E CAATINGA NATIV

The objective of this study was to evaluate the influence of agroforestry systems of different ages (AFS1: one-year old; AFS5: five-years old) on the biological attributes of soil; the following systems were used for comparison: a slash-and-burn (SBF) farming area, Caatinga which has been undergoing regeneration for 6 years (CaR6), and native Caatinga (NCa) in Brazil. Enzyme activity, abundance and composition of arbuscular mycorrhizal fungi (AMF), and production of glomalin-related soil proteins (GRSP) were evaluated at soil depths of 0–0.05 m. AMF species composition in the AFS was more similar to that in the NCa than in the SBF and CaR6 systems. In the rainy season, sporulation was most abundant in the AFS-1, CaR6, and SBF systems, whereas GRSP concentrations were highest in the AFS5 during the dry season. Acid phosphatase and arylsulfatase enzyme activity was lower in the AFS1 soils than in the NCa and SBF soils (rainy period), and levels of β-glucosidase and fluorescein diacetate hydrolysis in the AFS were equal to or higher than those in the NCa in the dry season but lower in the rainy season. AFS thus appear to promote the maintenance of soil biological quality, and may be more sustainable than SBF farming systems in the Brazilian Caatinga over the long term.

INTRODUCTION
Expansion of agriculture and livestock in Brazil can be traced back to the initiation of slashand-burn systems that are still common in northern and northeast Brazil, and which have considerable environmental effects. In the municipality of Pedro II (Central-Northern region of the Piauí), an area dominated by the Caatinga biome, agriculture is almost exclusively based on the slash-and-burn approach. The continuous use of this cultivation system has resulted in depletion of natural soil fertility, leading to increased farmer dependency on chemical fertilizers and pesticides, accelerated soil degradation, and loss of soil biodiversity (LIMA et al., , 2011.
In view of growing concerns about the dominant model of current agricultural production being both unsustainable and environmentally harmful, there is a need to look for alternatives that can maintain agricultural productivity without dramatically affecting terrestrial ecosystems.
Agroforestry systems (AFS), in which agricultural crops are intercropped with forest species, have been shown to increase soil sustainability (CEZAR et al., 2015;DOLLINGER;JOSE, 2018). Forest arboreal components, together with the great biodiversity of species, promote a continuous supply of organic detritus that enhances soil organic matter content, which directly benefits soil physical, chemical, and biological attributes (YADAV et al., 2011;CEZAR et al., 2015;WEERASEKARA et al., 2016;DOLLINGER;JOSE, 2018;YENGWE et al., 2018).
Numerous previous studies (SILVA et al., 2011(SILVA et al., , 2016SOUZA et al., 2018) have examined the effects of AFS on soil chemical and biological attributes in several distinct biomes, such as the Atlantic Forest, but research on the biological attributes of AFS soils in the Caatinga is scarce , and especially so regarding soil enzyme activity and arbuscular mycorrhizal fungi (AMF) in this biome.
Biosci. J., Uberlândia, v. 36, n. 1, p. 1-16, Jan./Feb. 2020 http://dx.doi.org/10.14393/BJ-v36n1a2020-42704 Soil microorganisms are the main sources of soil enzymes, and thus soil enzyme activity can be used as an indicator of alterations in soil microbial activity; however, this is largely restricted to processes in which enzymes are involved, such as the formation and degradation of organic matter or nitrogen mineralization (VIDICAN;STOIN 2015;BALOTA et al., 2013). Evaluations of enzyme activity are known to be efficient indicators that reflect changes that improve soil quality, as well as the decomposition of organic matter and nutrient availability due to cultivation or natural processes (SILVA et al., 2012b;TIAN et al., 2013;WEERASEKARA et al., 2016). Cultivation systems that minimize soil disturbance, maximize organic residue contributions, and incorporate crop rotation practices usually have higher levels of soil quality and microbiological activity, which is reflected in higher levels of enzyme production and, over time, enzyme accumulation in the soil matrix (PEIXOTO et al., 2010;BALOTA et al., 2013;FERREIRA et al., 2017).
In addition to soil enzymes, AMF are also considered to be important indicators of soil quality (DOBO et al., 2018;POSADA et al., 2018), largely due to the diverse soil processes in which these microorganisms are involved (FOLLI-PEREIRA et al., 2012). In addition to influencing plant nutrition, AMF, through their hyphae and the production of glomalin (a glycoprotein that amalgamates soil particles), are also directly involved in soil aggregation WRIGHT et al., 2007). Because of its composition (36-59% carbon) and its role in the stability of soil aggregates, glomalin contributes both directly and indirectly to soil carbon accumulation (KOIDE;PEOPLES, 2013;SINGH et al., 2013;WU et al., 2014). Similar to AMF, this protein (operationally defined as glomalin-related soil protein, GRSP) responds to changes in land-use and soil cultivation practices, and is often incorporated into environmental monitoring protocols because it is considered a good indicator of soil quality and AMF activity (SILVA et al., 2014a;ISLAS et al., 2016).
Quantification and evaluation of the beneficial effects of agroforestry practices are important for scientists, policymakers, and landowners for making significant decisions about land-use practices while at the same time diversifying farm income (KUMAR et al., 2010;WEERASEKARA et al., 2016). Our primary objective was thus to learn more about soil microbiological dynamics in agroforestry systems of different ages. We examined the abundance and diversity of soil AMF, GRSP production, and soil enzymatic activity in five ecosystems: 1-and 5year-old agroforestry systems, native forest, naturally regenerated forestlands, and slash-andburn farming, in the municipality of Pedro II, Piauí, Brazil. Our primary working hypothesis was that AMF, GRSP, and soil enzyme activity would be similar between the soils of AFS and native Caatinga ecosystems. Therefore, this study aimed to evaluate the influence of agroforestry systems of different ages (AFS1: one-year old; AFS5: fiveyears old) on the biological attributes of soil; the following systems were used for comparison: a slash-and-burn (SBF) farming area, Caatinga which has been undergoing regeneration for 6 years (CaR6), and native Caatinga (NCa) in Brazil.

MATERIAL AND METHODS
Our study was conducted in the municipality of Pedro II, Piauí State (04°25′30″ S, 41°27′32″ W; 630 meters above sea level), in a region dominated by Caatinga. According to the Köppen-Geiger classification system, the municipality of Pedro II fits into the climate type Aw (hot and humid). Rainfall occurs between November and March, and averages 900 mm annually (mostly concentrated in December-February). The driest months are August through October; however, dry spells lasting for 4-6 consecutive months are not uncommon. Average annual temperatures range from 18-30°C.
Five distinct areas were evaluated, consisting of 1-year-old agroforestry systems (AFS1), 5-year-old agroforestry systems (AFS5), Caatinga vegetation under regeneration for 6 years (CaR6), slash-and-burn systems under continuous cultivation with annual cycle monocultures (SBF), and areas of native Caatinga vegetation (NCa) ( Table 1). With the exception of AFS1, all areas consisted primarily of desert coverage, which is characteristic of soils in semi-arid regions; this layer hinders cultivation with tools or agricultural machinery under animal traction, which are common in small-scale farming. Each field or area was approximately 1 ha in size.
Sampling was performed in October 2009 (dry season) and April 2010 (rainy season). A 400 m 2 area was selected in each management system from which soil samples were taken; collection points were equidistant and a back-and-forth route was followed to ensure coverage of the entire area. MIGUEL, D. L.et al. 1-year-old agroforestry system: Cultivation of corn (Zea mays L.), cowpea (Vigna unguiculata L.), black mucuna (Mucuna aterrima L.), and cashew (Anacardium occidentale L.); presence of chalk-browed mockingbird (Mimus saturninus arenaceus Chapman) and mororó (Bauhinia cheilantha (Bong.) Steud.), and other shrubs and local species affecting the formation of the sparse brush called capoeira rala. AFS5 5-year-old agroforestry system: Cultivation of maize, cowpea, mango (Mangifera indica L.), and cashew; and the presence of chalk-browed mockingbird, mororó, and catingueira (Caesalpinia pyramidalis Caesalpiniaceae Tul.), and other local shrubs and species that result in the formation of sparse capoeira.

SBF
Slash-and-burn farming with continuous cultivation of annual cycle monocultures: Cultivation of rice (Oryza sativa L.), and grazing of sheep and goats when the cultivated area/pasture is developed. The annual burn produces residues that are used for cultivation the following year. After the removal of livestock, grass production is favored. CaR6 Caatinga vegetation under regeneration for 6 years: Land is cultivated for 6-8 years (sometimes less), following which it is left to regenerate for a period of 9-12 years (sometimes more). This use pattern is typical of itinerant agriculture, in which farmers migrate to different areas within the same region. NCa Native Caatinga: Preserved to maintain native vegetation.
To assess spore abundance and composition of the AMF community, 50 cm 3 of soil was separated from each sample. Spores were extracted by wet sieving (GERDERMANN; NICOLSON, 1963) followed by centrifugation in water and sucrose (JENKINS, 1964), and counted using a magnifying glass and a canelet plate. Spores were then transferred to a Petri dish and divided into two groups, one of which was placed on a slide with polyvinyl alcohol in lactoglycerol (PVLG) under a cover slip and the other placed on the same slide with Melzer's reagent under a second cover slip. Identification of AMF species was performed with the aid of an optical microscope, and followed the descriptions outlined in the International Culture Collection of (Vesicular) Mycorrhizal Fungi (INVAM; http://invam.caf.wvu.edu/) and the Arbuscular Mycorrhizal Fungi Phylogeny (http://www.amfphylogeny.com/index.html).
Glomalin in the soil was quantified as GRSP. Two fractions of GRSP (easily extractable glomalin, EEG; and total glomalin, TG) were differentiated as per the extraction conditions. The Biosci. J., Uberlândia, v. 36, n. 1, p. 1-16, Jan./Feb. 2020 http://dx.doi.org/10.14393/BJ-v36n1a2020-42704 easily extractable glomalin-related soil protein (GRSP-EE) was obtained by autoclaving, using 1 g of soil in 8 mL of sodium citrate at 20 mmoL L -1 (pH 7.4) and 121°C for 30 min, and the total glomalin-related soil protein (GRSP-T) was obtained using 1 g of soil in 8 mL of sodium citrate at 50 mmoL L -1 (pH 8.0), at 121°C for 60 min. More than one autoclaving cycle was necessary for the extraction of this fraction to produce a lightyellow colored sample. After autoclaving, both fractions were centrifuged at 5,000 g for 20 min, and the supernatant was removed for subsequent protein quantification (Bradford method; WRIGHT et al., 1996). Enzymatic analysis was performed for βglucosidase, acid phosphatase, and arylsulfatase based on the colorimetric reading of p-nitrophenol, which varies according to the level of enzyme activity (TABATABAI, 1994). For each sample, 1 g of soil was incubated for 1 h in a buffer solution containing one of the substrates p-nitrophenylglycoside (PNG), p-nitrophenyl-phosphate (PNF), or p-nitrophenyl-sulfate (PNS). Total enzyme activity was also evaluated through the hydrolysis of fluorescein diacetate (FDA) (DICK et al., 1996), whereby the colorless FDA solution changes to a bright yellowish-green when hydrolysis occurs, allowing its spectrophotometric quantification at 490 nm. The standard curve of fluorescein was established from standard solutions at different concentrations of fluorescein.
Data were evaluated for homoscedasticity by Cochran's test and for normal distribution of residues by the Lilliefors test, and results were subjected to ANOVA and the Scott-Knott test, at 5%. All statistical analyses were carried out with SAEG v5.0 (System of Statistical and Genetic Analysis -Federal University of Viçosa) and SISVAR software, with the exception of analysis of cluster grouping and relationships between edaphic attributes (Pearson's correlation analysis), for which the statistical program PAST was used.

RESULTS AND DISCUSSION
Spore abundance and AMF community composition No significant differences in spore abundance were observed between the study areas in the dry season (Table 3), whereas in the rainy season, the most abundant sporulation was observed in the AFS-1, CaR6, and SBF sites (Table 3). Lower sporulation in the AFS5 may be due to higher phosphorus (P, 8.42 mg dm -3 ) concentrations in this soil (Table 1); for instance, Dobo et al. (2018), in an evaluation of AMF spore abundance in the rhizospheric soils of different plants in Sidama, Ethiopia, reported that spore density was higher when concentrations of P and N were relatively low. Low sporulation also occurred in the NCa, a result similar to that of Piotrowski et al. (2008), who suggested that low sporulation in areas with native vegetation compared to agricultural systems was indicative of greater AMF activity in soils that are still undergoing constructive processes and have not yet attained stability.
Significant differences in spore abundance between seasons were only observed for AFS1 and SBF, with the highest values occurring in the rainy season (Table 3). According to Bonfim et al. (2010), seasonal influence on the occurrence of AFM spores is not yet well understood, and many contradictory results can be found in the literature. MIGUEL, D. L.et al.
Biosci. J., Uberlândia, v. 36, n. 1, p. 1-16, Jan./Feb. 2020 http://dx.doi.org/10.14393/BJ-v36n1a2020-42704 Explanations for the occurrence of higher sporulation, whether in the rainy or the dry season, can differ greatly, and can include factors such as the moisture level in each soil type, the characteristics of host plants, and the genetic tendency of a species for sporulation, among others (MOREIRA et al., 2009;OEHL et al., 2009). Furthermore, according to Gehring et al. (2003), higher light intensity during the rainy season promotes root exudation and carbohydrate concentrations in the roots, both of which contribute to higher rates of sporulation.
In the dry season, AMF species richness was highest in the NCa, with that in the SBF almost reaching equivalent levels (Table 3), whereas in the rainy season, AMF species richness was almost as high in both the AFS1 and AFS5 as in the NCa (Table 3). Total AMF richness increased in the AFS5, AFS1, and CaR6 areas from the dry season to the rainy season (Table 3).. The difficulty in establishing AMF distribution patterns may be due to various biotic and abiotic factors, as well as to the various survival strategies of these fungi (SOUZA et al., 2003). According to Posada et al. (2018), the spatial heterogeneity found in soil parameters may influence the rates and stage of succession by native AMF species, rendering uneven AMF species distribution on a local scale. Hazard et al. (2013) demonstrated the influence of the local environment in determining AMF composition, whereas Silva et al. (2014b) found that soil characteristics in semi-arid regions play a major role in the determination of AMF community composition. Souza et al. (2003) suggested that soil pH and P content are important factors for some species of AMF, and it was observed in this study that Entrophospora infrequens, Acaulospora spinosa, and Racocetra fulgida were observed only in the NCa area, which had soil P contents of 0.22 and 2.63 mg dm -3 in the dry season and the rainy season, respectively. Moreover, Racocetra undulata and Acaulospora cavernata were identified only in the AFS5 area, in which soil pH values were 6.39 and 6.48 in the dry and rainy seasons, respectively, and soil P content was 0.72 and 8.42 mg dm -3 in the dry and rainy seasons, respectively.
A total of 30 morphotypes of AMF spores were identified in different areas and in both seasons (Table 4), which were determined to belong to eight genera and five families (Glomeraceae, Acaulosporaceae, Gigasporaceae, Claroideoglomeraceae, and Ambisporaceae). Souza et al. (2003) and Silva et al. (2014b), in a study on the diversity of AMF in Caatinga ecosystems, identified 24 and 50 AMF taxa, respectively, with greater representation of Acaulosporaceae and Glomeraceae.
Members of the genera Acaulospora and Glomus were found in all areas and in both seasons, with members of Acaulospora most often representing the highest percentage of species compared to other genera (Table 4). Sousa et al. (2014) reported that species of Acaulospora and Glomus were most common in Caatinga systems at different stages of succession. Species in these genera typically exhibit greater adaptability to soils that differ in organic matter levels, lime, and texture, among other factors, suggesting that these species are particularly resistant to adverse environmental conditions (WU et al., 2011;FOKOM et al., 2012   G. macrocarpum was found to occur in all areas except AFS5 in the dry season (Table 4). Sousa et al. (2014) observed the presence of this species in areas of pasture and plant succession in early, middle, and late stages in the Caatinga. Species identified in this study, such as Ambispora leptoticha, Claroideoglomus etunicatum, Acaulospora scrobiculata, Glomus macrocarpum, and Acaulospora rehmii, have been previously reported to occur in the Caatinga (YANO- MELO, 2002;SOUSA et al., 2014). Figures 1A and 1B show the degrees of similarity between the systems studied, with respect to the occurrence of AMF species in the dry season and the rainy season, respectively. AMF composition varies in both seasons when NCa is converted to other systems of land usage; for example, in the dry season, conversion of NCa to AFS results in a 60% change in AMF species composition, and a 70% change with conversion to SBF systems; diversity of AMF was highest in the CaR6 area (90%) compared to the NCa in the dry period. The areas that showed the greatest similarity in both periods were AFS1 and AFS5. In the rainy season, AMF composition in both AFS5 and AFS1 (35% and 45% in the dry and rainy Biosci. J., Uberlândia, v. 36, n. 1, p. 1-16, Jan./Feb. 2020 http://dx.doi.org/10.14393/BJ-v36n1a2020-42704 periods, respectively) were more similar to that of the NCa than any of the other systems.

Entrophospora infrequens Hall, Ames & Schneider
The results of the groupings show that the degrees of similarity between the areas vary with the season, as was previously noted by Miranda et al. (2010), who proposed that this variation may be related to factors that affect mycorrhizal colonization, such as climatic variations that modify the hydrous relationships in the study areas, depending on the season. In addition, factors relating to the host plant in its different phenological phases, and AMF species themselves, which may have different survival strategies in each season, may also contribute to the variation between seasons.

Glomalin-related soil protein
Differences in glomalin levels between the study areas were only observed in the dry season, when AFS5 contained about 40% more GRSP-EE than did AFS1 and NCa, whereas GRSP-EE concentrations were approximately 30% lower in SBF and CaR6 systems than in NCa (Table 5). Fokom et al. (2013) reported that concentrations of GRSP-EE decreased when native forest was converted to agricultural cultivation, but no differences were observed between cultivated areas and an area under regeneration for 5 years. Previous studies have shown that soil disturbances reduce AMF populations (DODD et al., 2000;HAMEL et al., 1994) due primarily to disruptions of the mycelia networks, which may directly result in the inhibition of glomalin synthesis and deposition in the soil (FOKOM et al., 2013). GRSP-EE concentrations were significantly higher in AFS1 and AFS5 than in SBF (Table 5). Slash-and-burn agriculture can suppress production of the external mycelium, which is responsible for the synthesis of glomalin, in many types of AMF. Glomalin levels correlate positively with the production (CURAQUEO et al., 2011) andlength (ZOU et al., 2016) of hyphae. However, agroforestry systems, in which farming practices tend to be less intensive and plant diversity is higher, possibly create a more conducive environment for the production of the external mycelium, and thus higher rates of GRSP deposition, after the hyphae have been decomposed by microorganisms in the soil (DRIVER et al., 2005). The accumulation of GRSP in soil is dependent on numerous factors including plant community composition, AM fungi richness, type of land-use system, and soil properties (TRESEDER; TURNER, 2007;SINGH et al., 2016).
Similar to GRSP-EE, a statistically significant difference in GRSP-T between the areas was detected only in the dry season (Table 5). Concentrations of this fraction were 80% and 40% higher in AFS5 than in SBF and NCa, respectively. Higher GRSP levels may contribute to higher C and N uptake and enhance soil aggregation, given the high positive correlations between these soil properties and GRSP that have been reported by several authors (KOIDE;PEOPLES, 2013;SINGH et al., 2013;WU et al., 2014). Correlations between GRSP-T/GRSP-EE and TOC were highly positive (r = 0.94, p = 0.02, r = 0.84, P = 0.04, respectively) in the rainy season.
The absence of any significant differences between the areas during the rainy season may be due to improved growth conditions for roots due to the higher soil moisture levels; more extensive root systems may result in higher rates of AMF colonization, thereby increasing the production of hyphae  and resulting in a more homogeneous pattern of GRSP production among the different areas.
Concentrations of GRSP-T did not vary between the sampling seasons in most areas (Table  5). GRSP-T is considered the most stable fraction of GRSP, and is deposited in soil over a longer period of time, and thus experiences more biochemical transformations and greater soil particle adherence (NICHOLS; WRIGHT, 2005). As such, variations in this fraction may be smaller over short time periods. On the other hand, concentrations of GRSP-EE, the most labile fraction and which is deposited in soil over shorter time periods (RILLIG, 2004;WU et al., 2014), were higher in AFS-5, AFS-1, and NCa in the dry season than in the rainy season. There is evidence that AMF hyphae produce more glomalin per unit weight or length of hyphae under conditions of stress (e.g., drought, salinity) (HAMMER; RILLIG, 2011); several studies have also shown that concentrations of EE-GRSP increased in the rhizosphere of Trifoliate orange seedlings under drought stress (WU et al., 2008;ZOU et al., 2014) because the drought-stress-induced death/senescence of mycorrhizal hyphae resulted in the release of more GRSP into the soil (DRIVER et al., 2005). Thus, it can be inferred that the higher levels of GRSP-EE in the dry season may be due to greater production of this protein by hyphae and/or more of this protein being released as a result of hyphae death/senescence.
Higher GRSP concentrations in the dry season may indirectly favor the development of plant species, given that its role in enhancing the stability of soil aggregates increases soil moisture retention (WU et al., 2008 and, more directly, the physiological effects the protein has on plants (CHI et al., 2018). In an evaluation of the effect of exogenous GRSP-EE on T. orange seedlings, Chi et al. (2018) observed that application of exogenous GRSP-EE improves the drought tolerance of this species, thereby significantly enhancing leaf water potential, net photosynthetic rate, transpiration rate, stomatal conductance, and intercellular CO 2 concentrations, while at the same time drastically reducing leaf temperature regardless of soil water status.

Enzyme activity
Total enzyme activity, as assessed by FDA hydrolysis, was higher in AFS1, CaR6, and SBF than in the other areas during the dry season, whereas total enzyme activity levels were similar in AFS5 and NCa ( residues on the soil surface, and the increased availability of organic matter to primary soil decomposers may account for the high FDA activity observed in this area. Previous research has shown that inputs of organic materials, such as plant litter, stimulate soil FDA (LOPES et al., 2010;RODRIGUES et al., 2015). Soil TOC content (Table 2) was highest in the SBF, which may explain why this area also had one of the highest levels of FDA activity. During the rainy season, FDA activity was greater in areas where TOC levels were the highest, specifically in areas of lower cultivation intensity (SBF) and greater conservation (NCa). On the other hand, FDA activity values were also high in CaR6 in the dry season, for which TOC content was the lowest. Thus, it is important to note that the enzymes may be stimulated according to the role they play in the processes that occur in the soil, i.e., enzymes involved in the degradation of a given substrate increase activity when the availability of that substrate in the environment also increases. In the same manner, the activity levels of enzymes associated with stress increase under conditions of environmental stress (WALLENSTEIN;WEINTRAUB, 2008).
In the dry period, β-glucosidase activity was higher in SBF and AFS5 than in NCa, AFS1, and CaR6, although activity levels in the latter two did not differ from that in NCa. In the rainy season, on the other hand, higher levels of β-glucosidase activity were observed in NCa than in AFS1, AFS5, CaR6, and SBF, with no differences observed among the latter four at any time. Variations in the activity of this enzyme between the areas may be due to differences in organic matter inputs to the soil, because β-glucosidase is directly related to the C cycle and it is primarily active in the final stages of cellulose decomposition, in which it hydrolyzes the residues from cellobiose to β-D-glucose (TABATABAI, 1994). Moreover, large quantities of highly complex residues may promote lower activity of this enzyme (MATSUOKA et al., 2003). For example, in this study, we observed a high correlation between β-glucosidase and soil TOC (r = 0.93; P = 0.02) in the rainy season. β-glucosidase activity levels were higher in the dry season than in the rainy season. Differences in acid phosphatase activity between the systems of land usage occurred only during the rainy season, with activity levels higher in NCa than all other areas (Table 6), possibly due to the lower concentrations of soil P in that system (Table  2); this may also explain why the lowest activity levels of this enzyme were observed in AFS5, given that P concentrations were higher in this area than in the others. In general, this pattern was observed when comparing the other areas evaluated. Silva et al. (2018), in an assessment of the activity levels of acid phosphatase in soils under different stages of forest regeneration in the Caatinga, observed that acid phosphatase activity was always higher in soils with lower P concentrations. Balota et al. (2013) proposed that, in some cases, enzymatic activity can increase due to a deficit of, or the form (organic or mineral) in which, certain nutrients appear in the soil, using P and sulfur (S) as specific examples. Activity levels were higher in SBF and CaR6, whereas activity levels in AFS1 and AFS5 were lower than in SBF and CaR6 but similar to one another. The variation in acid phosphatase activity between the areas is most likely due to the concentration of TOC in the soil, as a high positive correlation (r = 0.97, P = 0.005) between acid phosphatase activity and TOC MIGUEL, D. L.et al.  (CARNEIRO et al., 2008, JAKELAITIS et al., 2008. In the dry season, arylsulfatase activity exhibited a pattern similar to that of phosphatase, that is, that water deficit reduces enzymatic activity to the point where activity does not reflect the characteristics and properties of the different landuse systems. Conversely, in the rainy season, increased activity of this enzyme was observed in NCa, to a lesser degree in SBF and AFS5, and even less so in CaR6 and AFS1. This variation may be related to soil TOC levels (ACOSTA-MARTINEZ et al., 2018), given that this enzyme is positively correlated (r = 0.93; P = 0.02) with soil TOC. According to Nogueira & Melo (2003), arilsulfatase activity in soils decreases with reductions in organic matter, because organic matter is the main source of sulfate esters, which are substrates of this enzyme; in contrast, however, McGill & Colle (1981) proposed that a considerable proportion of arylsulfatase found in soils is secreted by bacteria as a result of limited organic material. Under normal conditions, the occurrence of this enzyme in soils is related to microbial biomass and the level of immobilization (BALOTA et al., 2013).
Enzymatic activity was about 1.5-3 times greater in the rainy season than in the dry season for β-glucosidase, arylsulfatase, and FDA, and, in the case of acid phosphatase activity in NCa, as much as 3.5 times greater; as such, the time of year appears to determine the level of enzymatic activity in the soil, considering that the results were higher in the rainy period than in the dry period for all enzymes included in our study (Table  6). These seasonal differences are likely due to variations in soil moisture and temperature (ARAÚJO et al., 2013;RODRIGUES et al., 2015). In general, lower enzymatic activity in the dry season was attributed to water deficits, whereas in the rainy season, when water was not a limiting factor, enzymes were more readily able to hydrolyze decomposable compounds (ARAÚJO, 2013;. Rodrigues et al. (1985) argued that soil microbial community structure tends to vary between seasons and responds differently to dry and humid conditions, as Araújo et al. (2013; observed in the tropical soils of northeastern Brazil. In addition, Caatinga vegetation regeneration during the rainy season likely promotes the addition of easily decomposable residues, which would promote higher levels of soil enzymatic activity.

CONCLUSIONS
Agroforestry systems AFS1 and AFS5 were found to maintain or increase the sporulation of AMF and the production of GRSP compared to native Caatinga. Although AFS operations altered AMF composition, AMF communities in these areas most closely resembled AMF communities in native Caatinga soils compared to slash-and-burn systems and Caatinga under regeneration. AFS helps to maintain or even promote enzymatic activity (FDA, acid phosphatase, βglucosidase, and arylsulfatase) in the dry season, although activity levels of these enzymes are lower in AFS soils than in native Caatinga during the rainy season. Based on most of the biological attributes evaluated in our study, we conclude that agroforestry systems promote the maintenance of, or can even improve, soil biological quality, and may be more sustainable than the slash-and-burn farming systems in Caatinga ecosystems over the long term.


extfieldsofstudy: Medicine Biology

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text:
Long-Term Feeding of Dairy Goats with Broccoli By-Product and Artichoke Silages: Milk Yield, Quality and Composition

Simple Summary In the Mediterranean region, artichoke and broccoli are major crops with a high number of by-products that can be used as alternative feedstuffs for ruminants, lowering feed costs and enhancing milk sustainability while reducing the environmental impact of dairy production. However, the nutritional quality of milk needs to be assured under these production conditions. A 40% inclusion of these silage by-products has proven to be a good short-term option (1 month). Therefore, it is interesting to study the effect of feeding animals with these by-products for a longer time, such as full lactation (6 months). With this type of feeding, the performance of the animals fed with the artichoke plant silage was similar to that of those fed with a conventional diet (alfalfa and cereals), even improving the milk quality from the point of view of bioactive compounds. However, the performance of goats fed with broccoli by-product silage in high amounts worsened due to a low intake of the diet containing that by-product. Therefore, it is desirable to reduce its level of inclusion. Abstract The aim of this experiment was to study the effects of a 40% inclusion of broccoli by-product (BB) and artichoke plant (AP) silages in dairy goat diets on the milk yield, composition and animal health status during a full lactation. Feed consumption was lower in AP and BB animals due to their composition and higher moisture content, and BB animals showed a significant reduction in body weight. Milk from the BB treatment had the highest fat content, total solids and useful dry matter content (5.02, 13.9 and 8.39%, respectively). The Se level was slightly lower in AP and BB animals; however, the milk of these treatments was the lowest in Na and, in the case of BB animals, the richest in Ca (1267 mg/kg). Control and AP milk showed a similar fatty acid profile, although AP had a more beneficial aptitude for human health (lower ratio of n6/n3, 12.5). Plasma components, as metabolic parameters, were adequate for goats. It was concluded that a 40% inclusion of AP is an adequate solution to reduce the cost of feeding without harming the animals’ health or performance and to improve the nutritional milk quality. It is necessary to lower the BB level of inclusion to increase feed consumption.

Introduction
According to the Food and Agriculture Organization (FAO) [1], the production of goat milk is in third place (behind cow and buffalo), with a world production of over 18,600,000 t. The main authorized by the competent public body (Conselleria de Presidència i Agricultura, Pesca, Alimentació i Agua of Generalitat Valenciana, Spain).

Experimental Design
From a group of 80 goats at the onset of lactation (4th week) fed with a conventional diet (control, C), a pre-experimental individual sampling was performed and 72 animals were selected, with an average body weight (BW) of 44.6 ± 7.81 kg, an average milk yield of 2.43 ± 0.21 kg/day and a somatic cell count (SCC) of 5.14 ± 0.55 Log cells/mL. The animals were divided into three groups with homogeneous characteristics regarding the commented variables. Each group was assigned to one treatment: control (C), which represents the conventional diet of the region (alfalfa hay and a mixture of grains) and two treatment rations that included 40% (on dry matter basis) of broccoli by-product silage (BB) or artichoke plant silage (AP) in a total mixed ration (TMR). Silages were manufactured with a baler-wrapper (Agronic MR 820, Netherlands), with a weight of 300 kg and without additives, according to the work published by Monllor et al. [14]. All rations were calculated according to the formulation recommendations of Fernández et al. [21]. They were isoenergetic and isoproteic and the daily amount offered was 2.22 kg DM/day. BB and AP silages had a lower cost than other feedstuffs used in animal feeding (45 and 42 EUR/t, respectively). Tables 1 and 2 show the amounts of the proportions of ingredients in each diet, as well as costs and the composition and mineral and lipid profiles. Feeding was performed in a group to avoid the stress caused by the individual confinement of the animals during a long-term study (6 months), as was done by Nudda et al. [22], due to the gregarious behaviour of goats [23]. This experimental design allows the average food consumption of the animals to be determined, although not the exact amount of individual feed intake nor ingested nutrients. However, the facilities and devices present on the farm allowed the collection of individual milk samples.  Once the pre-experimental sampling was done, the experiment lasted 23 weeks. The first three weeks for each group served as time to adapt to their diet. In the following 20 weeks, data on BW and milk yield were taken, and blood and individual milk samples were collected for a subsequent laboratory analysis every 5 weeks, with 5 samplings in total during the whole experiment ( Figure 1). In addition, the same week of individual sampling, after the milking of each experimental group, milk samples from the refrigerating tank were collected on three consecutive days for an analysis of the mineral and lipid profile. Then, the leftover milk was transferred to another refrigerated tank to get an empty and clean tank to receive milk from the next experimental group.

Variables Analysed
BW (kg) was determined by weighing animals with a precision scale of 100 g (APC, Baxtran, Spain). Feed consumption data were taken from two consecutive days in each sampling week and calculated as the average of the difference between the feed amount offered and refused, determining the dry matter by dehydration in an oven at 105ºC for 48 h of a representative sample of the feed amount refused by the animals for each treatment. Representative samples were taken from each silage and ration at the start of the experiment for a subsequent laboratory analysis. To avoid providing the animals with spoiled silage, each bale was removed after two days of being open. In this way, it was possible to feed the animals with silage in optimal conditions that would not have been affected by aerobic degradation. The composition of the rations was determined in a similar way to Monllor et al. and Monllor et al. [19,20], using Association of Official Agricultural Chemists (AOAC) methods [25] for dry matter (DM, g/kg; method 930.5), organic matter (OM, g/kg DM; method 942.05), ether extract (EE, g/kg DM; method 920.39) and crude protein (CP, g/kg DM; method 984.13). The contents of neutral detergent fibre (NDF, g/kg DM), acid detergent fibre (ADF, g/kg DM) and acid detergent lignin (ADL, g/kg DM) were analysed according to Van Soest et al. [26]. The total polyphenol content (TP, g/kg DM) was determined by the Folin-Ciocalteu method reported in Kim et al. [27]. The proportion of short chain volatile fatty acids (VFAs, g/kg DM)-acetic, propionic and butyric acid, also including lactic acid and ethanol-were determined by high performance liquid chromatography (HPLC) (Agilent 1200 and Supelcogel C-610H column: 30 cm × 7.8 mm ID; [28]). The apparent in vitro dry matter digestibility (IVDMD, g/kg DM) was determined in duplicate by the method of Menke and Steingass [29]. An analysis of the fatty acid profile in the diets was carried out by direct methylation on the lyophilised samples, without prior extraction of the fat, according to Kramer et al. [30]. Methylated fatty acid esters (FAMEs) from diets were identified and quantified by a gas chromatograph (GC-17A Shimadzu, Japan) coupled with a flame ionisation detector (FID) equipped with a capillary column (CP Sil 88 100 m × 0.25 mm internal diameter and 0.20 µm internal coverage, Agilent, USA). A FAME standard mix (18912-1AMP, Sigma-Aldrich, USA) was used to identify the fatty acids present in the samples.
For the analysis of dietary and milk minerals, the same procedures were followed as in Monllor et al. [20], with a previous digestion of the samples performed according to González Arrojo et al. [4]. Na, Mg, K, Ca, P, S (g/kg DM) and Se, Zn, Cu, Fe and Mn (mg/kg DM) were determined by prior microwave digestion (Ethos Easy, Milestone, Italy) and identified with an ICP-MS octupole chromatograph (Agilent 7500 Reaction System, USA), using an internal standard.
Analysis of the milk fatty acid profile was carried out in duplicate, with an extraction using the Folch method with some variations reported in Romeu-Nadal et al. [33] and a subsequent methylation according to the method of Nudda et al. [34], similar to what was done by Monllor et al. [21]. The chromatograph, the column and the FAME mix for the identification of the milk fatty acids were the same as those used in the diets. The indices related to the nutritional quality of milk fat were calculated-the Atherogenicity Index (AI) and Thrombogenicity Index (TI)-according to Ulbricht and Southgate [35], and the Desaturase Index (DI) for C14:0, C16:0 and C18:0, according to Lock and Garnsworthy [36].
On the same day that the milk sampling was performed, blood samples were taken from fasting animals for an analysis of glucose, urea, β-hydroxybutyrate (BHB), cholesterol, nonesterified fatty acids (NEFAs) and haematocrit, similarly as published by Monllor et al. [20]. Blood samples were analysed by enzymatic spectrophotometry. For glucose and cholesterol (mg/dL), a glucose oxidase/peroxidase kit was used (Refs. 11,503 and 11,505, Biosystems, Spain); for urea (mg/dL), the kinetic method GN 10,125 developed by Gernon was used (Spain); for the BHB (mmol/L), the Ranbut D-3-Hydroxybutyrate kit (RB 1007, Randox, UK) was used, and for the NEFAs (mmol/L), an enzymatic-spectrophotometric method FA 115 (Randox, UK) was used. The haematocrit (%) was determined with a microhaematocrit.

Statistical Analysis
SCC values were transformed into logarithm base 10 to carry out the statistical analysis. A normality test was performed on data (PROC UNIVARIATE. SAS v9.2, 2012) and the result was that all variables complied with normality and variance homogeneity. The variables obtained from individual animals were analysed according to a mixed linear model with repeated measures (PROC GLIMMIX. SAS v9. 2,2012), introducing into the model the covariate of the data obtained in the pre-experimental sampling, according to the following equation: where Y is the dependent variable, µ is the intercept, Di is the fixed effect of the diet (i = C, BB, AP), Wj is the fixed effect of the lactating week (j = 7, 12,17,22,27), DixWj is the interaction of the diet with the lactating week, covY0 is the effect of the value of Y in control 0, Ak is the random effect of the animal and e is the residual error. For each variable, the covariance model that presented lower AIC and BIC statistics was used.
In the case of the milk lipid and mineral profile variables, an ANOVA (PROC. GLM, SAS v9.2, 2012) was performed, similar to the previous one, except that the random effect of the animal was not considered.

Results
No significant differences (p > 0.05) between groups were observed in the pre-experimental sampling (fourth week of lactation) for any of the variables analysed (body weight, milk yield and composition, SCC and milk mineral and fatty acid profile).

Body Weight and Milk Performance
BW remained constant in animals fed with AP throughout the experiment, and a significant increase (p < 0.01) was observed in C animals, while BW of animals fed wit BB fluctuated throughout the experiment, with a decrease in the first half (weeks 7th to 17th) before reaching similar levels at the end of the experiment to those at the outset ( Figure 2a). The C group had the highest average BW in the treatment (43.5 kg; Table 3), with significant differences (p < 0.01) with the BB group (41.5 kg), while the AP group did not show differences compared to any of the other treatments (42.1 kg). The average feed consumption was higher in the C group (1.98 ± 0.112 kg DM/day), followed by AP animals (1.82 ± 0.151 kg DM/day), while a remarkable lower consumption in BB animals (1.59 ± 0.124 kg DM/day) was observed. All three treatments reduced feed consumption mid-lactation: 160 and 180 g DM/day less between weeks 12th and 17th in C and AP animals, respectively, and 100 g DM/day less between weeks 17 and 22 in BB animals. Table 3. Comparison of body weight, milk yield and composition and somatic cell count (SCC) in goat milk, according to the effects considered from 7th to 27th lactation week. The milk yield was slightly lower in the BB group (1.91 kg/day, Table 3) compared to the AP group (2.21 kg/day, p < 0.05), with no differences in the C group (2.11 kg/day) and with a significant and gradual decrease (p < 0.001) throughout the experiment in the three treatments (Figure 2b), although in a different way, which meant the interaction of the treatment with the sampling was significant. The milk yield of animals fed with BB decreased markedly in the first sampling (week 7), increased in week 12 to levels close to the rest of the treatments and then decreased again significantly at week 17, with the three treatments reaching similar values at the end of lactation (Figure 2b); the AP group's milk yield decreased gradually throughout the experiment, and some oscillations were observed in the C group at the end of the experiment (week 22). Regarding the FCM and FPCM, no differences were observed between treatments in terms of mean values (Table 3); the value of both parameters decreased in the C and AP treatments as lactation progressed (p < 0.001), while in the BB treatment, the FCM remained (data not shown). Regarding the milk macrocomposition, BB presented a higher average fat content (p < 0.01) (0.5% more, in absolute terms) than the other treatments (Table 3). A gradual increase (p < 0.001) in the fat content was observed in the three treatments throughout the experiment, generally with an inverse evolution to that observed in the milk yield, and the fat of the BB group was significantly greater than the other treatments at week 17 (Figure 2c), while in the rest of the samplings no significant differences (p > 0.05) were observed between treatments. The C treatment presented the highest content of crude and true protein (3.55 and 3.29%, respectively; p < 0.05. Table 3), while the BB treatment presented significantly lower values (3.35 and 3.12%) without significant differences between the two treatments with AP (3.42 and 3.18%). As observed with fat, the crude protein content increased (p < 0.001) as lactation progressed, and C presented a significantly higher content (p < 0.001) than the other treatments in weeks 17 and 22. The milk protein contents of the C and AP animals were equal at the end of the experiment (Figure 2d), but higher (p < 0.05) than in BB animals. In relation to whey protein, the C and AP groups were significantly higher than the BB group (0.431 and 0.404 vs. 0.356%, respectively; p < 0.001). In relation to TSs and UDM, BB animals presented significantly and slightly higher contents (13.9 and 8.39%, respectively; p < 0.05) than AP animals (13.2 and 7.79%), with no differences of both treatments in C animals (13.5 and 8.05%). These variables evolved in a similar way as fat and protein did throughout lactation (Figure 2e). There were no significant differences in the average content of ash between treatments and it decreased (p < 0.001) over the weeks (Figure 2f). C and AP animals presented higher (p < 0.05) milk urea contents (797 and 793 mg/L, respectively) than BB animals (745 mg/L) and the evolution throughout the experiment was different between treatments-more constant in AP and with 90 mg/L maximum oscillations in C and BB animals, as can be seen in lactation weeks 12 and 17 in Figure 2g. The SCC increased (p < 0.001) in the three treatments as lactation progressed (Figure 2h), with a higher content in C animals (5.73 Log cells/Ml; p < 0.05) than BB and AP animals (5.55 and 5.53 Log cell/mL, respectively).

Milk Mineral Profile
The only significant differences were observed between treatments regarding the contents of Na, Ca, Mn and Se ( Table 4). The C group obtained higher (p < 0.001) Na and Se contents (378 mg/kg and 17.2 µg/kg, respectively), although with small differences with the other treatments (19 and 47 mg/kg of Na in BB and AP animals, respectively, and 2.8 and 3 µg/kg of Se in BB and AP animals, respectively). BB animals had the highest Ca level (1267 mg/kg; p < 0.001) and AP animals showed the lowest concentration of Mn (31.2 µg/kg; p < 0.001), with little difference from the other treatments (Table 4). In relation to the evolution of each mineral throughout the experiment, a reduction in the concentration of Cu was observed (−27.5 µg/kg), the levels of K and Zn fluctuated during lactation without significant differences between the beginning and the end and, in the rest of the minerals, an increase was observed over lactation in the three treatments-68 mg/kg Na, 40 mg/kg Mg, 204 mg/kg P, 72 mg/kg S, 89 mg/kg Ca, 16.1 µg/kg Mn, 69 µg/kg Fe and 4.1 µg/kg Se-as the average increases from the beginning to the end of the experiment.

Milk Fatty Acid Profile
The greatest differences in the fatty acid contents were found in C15:0, C16:1cis9 and C17:0, where BB and AP animals presented the highest concentrations during the experiment (Figure 3a,b), and in some isomers of oleic acid, such as vaccenic (C18:1trans11) and rumenic acids (C18:2cis9,trans11), in which the highest concentration corresponded to the C animals (Table 5), although all differences were of small magnitude. As lactation progressed, the differences between treatments in linoleic acid (C18:2n6) and PUFA contents (Figure 3c,e respectively) were reduced. However, from week 17, α-linolenic acid (C18:3n3) and the ratio n6/n3 (Figure 3d,i) increased and the AP group obtained better results (higher level of α-linolenic acid and lower ratio n6/n3).   Regarding the fatty acid groups, BB animals presented the worst results from the point of view of functional compounds for human health-a lower content of CLAs, higher ratio of saturated fatty acids (SFAs)/unsaturated fatty acids (UFAs) and a higher AI and TI values ( Table 6). The C group had the highest level (p < 0.001) of the sum of CLA isomers (0.954 g/100 g of total fatty acids (TFA)), followed by the AP group (0.752 g/100 g TFA) and BB group (0.547 g/100 g TFA). The MUFA content was significantly (p < 0.0001) higher in C (27.5 g/100 g TFA) than in AP (27.1 g/100 g TFA) and BB (26.2 g/100 g TFA). The PUFA content was similar in C and AP animals (4.89 and 4.73 g/100 g TFA, respectively), and both were significantly (p < 0.0001) higher than BB animals (3.81 g/100 g TFA). The SFA/UFA ratio was significantly (p < 0.0001) lower in C animals (2.08), followed by AP animals (2.14) and was significantly higher in BB animals (2.33). However, the n6/n3 ratio was lower (p < 0.0001) in AP animals (12.5), followed by BB animals (13.7) and higher in C animals (15.6). Related to odd and branched chain fatty acids (OBCFA), the silage treatments showed a higher content than the C group (3.35 g/100 TFA; p < 0.001), with higher contents in the AP compared to the BB group (3.95 and 3.71 g/100 TFA, respectively). The effect of lactation and diet x sampling interaction was also significant in OBCFA, because although in C animals it remained constant, in AP animals it increased in week 7 and later in week 17, and in BB animals it increased in week 22 (p < 0.001; Figure 3f). Regarding the evolution of the summations throughout the experiment, the three treatments showed similar changes, although at some points of the experiment small oscillations occurred (Figure 3e,g,h), except in the case of the ratio n6/n3 (Figure 3i), which decreased markedly and significantly in the AP group from week 17th, due to the increase in C18:3n3 (Figure 3d).

Plasmatic Metabolites Profile
Significant differences (p < 0.001) were observed between treatments (Table 7 and Figure 4). The glucose content decreased (p < 0.001) in C and AP animals during the first part of the experiment (Figure 4a) and stabilised later in C animals, while in BB animals it increased at the last sampling and oscillated in the last two samplings in AP animals (weeks 22 and 27). Regarding cholesterol, the C group reached levels significantly higher (100 mg/dL; p < 0.0001) than the BB and AP groups (91.9 and 86.1 mg/dL, respectively). The cholesterol levels of the C and BB groups decreased from lactation week 7 (p < 0.001) (−17.09 and −17.55 mg/dL, respectively; Figure 4b). C animals' cholesterol increased again later, while BB animals remained unchanged until the end; AP animals did not show significant differences between any sampling. The plasma urea content was slightly higher in C and AP animals (45.7 and 44.0 mg/dL, respectively) compared to BB animals (40.4 mg/dL), and the evolution throughout the experiment was similar in the three treatments (Figure 4c), except between weeks 17 and 22, where plasma urea content of animals fed with C and BB remained constant and decreased in AP animals. Finally, the urea content increased in the three treatments at the last sampling, although more markedly in C animals (+18.3, +8.1 and +7.4 mg/dL in C, BB and AP animals, respectively). Diet had a significant effect on the BHB concentration: the average value of the BB group was lower than the C and AP groups (0.356 vs. 0.456 and 0.450 mmol/L, respectively for BB, C and AP groups; p < 0.01). BHB levels decreased during the experiment, with slight fluctuations (Figure 4d). The NEFA level was higher in BB and AP animals at the start of the experimental phase (Figure 3e), and an increase in these treatments was observed since by-product silages were included in the diets, as the three treatments had similar levels in the pre-experimental period (0.424, 0.476 and 0.550 mmol/L, respectively for C, BB and AP animals). The NEFA levels of BB and AP animals decreased (p < 0.001) gradually throughout the experiment until they reached a similar level to C animals at week 22. Haematocrit decreased in all three treatments over the whole experiment (Figure 4f), and a slight increase was observed at the last sampling in C animals, which caused significant, although minor differences (p < 0.01) compared to BB animals, and without differences compared to AP animals (30.4, 28.2 and 29.3% for C, BB and AP animals, respectively).

Body Weight and Milk Performance
The BW reduction observed in the first sampling (week 7 of lactation) was due to the fact that the animals had the greatest lactation needs at the same time, which did not coincide with the time of the maximum intake capacity, which encourages weight loss [37]. BW increased later, coinciding with the reduction in milk yield and the recovery of feed consumption typical of this lactation stage. BB animals showed a lower feed consumption than C and AP animals, because the diet that included BB contained a higher concentration of fermentation products, such as acetic acid, ethanol and ammonia N (Table 1), which had a depressive effect on feed consumption, as observed by Huhtanen et al. [38] through a meta-analysis of 240 studies. As in Monllor et al. [20], the higher moisture content of the BB diet and, therefore, the higher ration volume, decreased consumption. Other causes that affected feed consumption were the higher content of polyphenols in BB, which limits intake, as observed by Oliveira et al. [39] in growing calves and in grazing Sardinian goats, as shown by Decandia et al. [40]. The average feed consumption of C and AP animals (1.98 and 1.82 kg DM/day) was higher than that reported by Criscioni and Fernández [41] in Murciano-Granadina goats (1.7 kg DM/day) with an average milk yield of 2.2 kg/day and a BW of 46 kg, fed with alfalfa and concentrate. BB consumption was slightly lower than the values commented (1.59 kg DM/day).
The milk yield values observed in this experiment are similar, or even slightly higher in the case of the AP group, to those obtained from the equation proposed by León et al. [3] to model the lactation curve for Murciano-Granadina goats: y = 1906 + 0.0229t − 0.000254t 2 + 0.000264 (t − 57.28) 2 , where y is the daily production (kg/day) and t represents the days in lactation. The inclusion of BB silage at the level tested (40% DM in the diet) negatively affected the milk yield, as a marked decrease was observed in this treatment compared to C and AP groups in the first 17 weeks in lactation, although after the 22nd week, the differences between treatments disappeared. On the other hand, the AP group's milk yield was not significantly different from C group's, which indicates that the inclusion of 40% of artichoke plant silage during lactation allows for the maintenance of the productive level.
The higher fat content in BB animals' milk (5.02%) was due to its lower milk yield, also related to the higher NEFA content observed, so it indicated a greater tendency for a negative energy balance, probably due to a greater mobilisation of body reserves [42]. Even though the level of NEFA in AP animals was similar to that in BB animals (0.868 mmol/L), the higher milk yield (2.21 kg/day) of the AP group meant that fat concentration was higher in the BB group than the AP group. Another cause that explains the higher fat content of BB animals' milk is the higher concentration of acetic acid (9.50 g/kg DM) in the diet, which served as a precursor for the "de novo" synthesis of fat in the mammary gland [43]. Lough et al. [44], when they supplied a continuous intravenous infusion of acetate in dairy goats at different times of lactation, did not observe changes in milk yield or protein content. The higher fat content of BB animals' milk explains the higher level of TSs and UDM (13.9 and 8.39%, respectively) also observed in this treatment, even though C and AP animals had higher protein values.
The SCC increased as lactation progressed and the yield reduced, as occurred in Strzałkowska et al.'s study [45]. The SCC values are within normality for Murciano-Granadina goats with a correct health status of mammary gland [46].

Milk Mineral Profile
The mineral profile of this study coincides with that shown by Stergiadis et al. [47]. The Ca/P ratio of BB and AP animals is similar to that observed in local breeds in southern Europe [48,49]. The Ca/P ratio of C animals was lower due to its lower Ca content. The higher concentration of Ca in BB animals is due to the fact that the diet of this treatment also presented a higher level of Ca than the other two [50], which is beneficial for milk coagulation and curd firmness [51].

Milk Fatty Acid Profile
OBCFA levels were within the normal range for milk, between 2 and 3% of total fat, according to Patel et al. [52]. These same authors found a higher concentration of OBCFA in milk from cows fed grass silage, indicating that the more NDF content in the diet, the more OBCFA will be synthesised by ruminal microorganisms due to a higher production of VFAs. This could be the explanation for the higher total OBCFA content in AP animals, as well as C11:0, C17:1 cis9, iso C13:0, iso C14:0, iso C15:0 and iso C17:0. The higher levels of C15:0 and C17:0 in BB animals' milk come from a greater reserve mobilisation in the animals of this treatment, as these fatty acids are synthesised de novo from adipose tissue [53]. The initial increase (week 7) of OBCFA in AP animals could also be due to a mobilisation of reserves, coinciding with the peak of lactation; however, the OBCFA content stabilised in this group and the next increase that occurred at week 17 may already have been due to an adaptive response to the higher NDF content of the AP diet following the microbial origin of these fatty acids.
The lower concentration of linoleic, vaccenic and rumenic acids in BB was because the diet that included this by-product contained a lower proportion of linoleic and α-linolenic, precursors that are biohydrogenated in the rumen to form the aforementioned acids [54]. The Short Chain Fatty Acid (SCFA) concentrations in this study are similar to those found by Arco-Pérez et al. [55] in milk from Murciano-Granadina goats fed with a tomato by-product silage and to those commonly found in cow's milk [56]. This means that the inclusion of these by-product silages in the goat diet at the tested concentrations (40%) seems to have little impact on the flavour of milk, due to the role that SCFAs play in the organoleptic properties of milk [57]. From week 17 of lactation, the nutritional quality fatty acid profile of AP improved from the point of view of human health, because of a lower n6/n3 ratio [58], which gives added value to the milk of goats fed with the silage, due to the current high demand for high quality healthy products for human consumption.

Plasmatic Metabolites Profile
Glucose levels coincided with those observed by Hamzaoui et al. [59] in goats of the same species. The glucose concentration was higher at the beginning of the experiment due to gluconeogenesis that takes place at the beginning of lactation [60], whereby the animals are able to reach the peak of production despite the limited intake capacity that occurs in this stage. The same could be the cause of the increase in glucose in BB animals at the last stage of the experiment, due to the lower feed consumption of the animals in this treatment, which caused the mobilisation of reserves and the reduction in BW. In addition, higher contents of TP in BB animals could cause an improvement in carbohydrate catabolism, as Makkar et al. and Zhong et al. [61,62] observed when glucose levels increased as the content of catechins and condensed tannins were higher in goat diets. The beneficial effects of TP on the lipid metabolism of small ruminants have been also demonstrated [63]. As a result of the lower feed consumption in AP and BB treatments in the stages with the highest energy demands, the NEFA content was higher in these treatments due to the mobilisation of adipose tissue to meet the animals' requirements [64,65]. These differences in NEFAs were not accompanied by differences in BHB, due to the availability of glucose (a result of the glycogenesis observed in BB), which is used to oxidise NEFA in the liver and produce energy [66]. As the level of feed consumption was adjusted to the milk yield, the concentration of NEFA was reduced, as occurred in Ríos et al. [67].
With the inclusion of BB in the diet, the cholesterol level increased between the 4th and 7th week of lactation, as in the C group, due to the synthesis of lipoproteins responsible for the transport of lipids to the mammary gland, habitual in this lactation stage [68]. This increase in cholesterol at the onset of lactation has been corroborated by other authors, such as Ruas et al. and Guedon et al. [69,70], who established that in beef cattle the minimum cholesterol level occurred at birth and the maximum was reached nine weeks later. The blood cholesterol content decreased as lactation progressed and milk production decreased, according to the Wood model [71]. However, it increased in the C group from week 17, probably due to a positive energy balance (excess of energy intake compared to the level of production at that time), as observed by Ríos et al. [67], coinciding with the increase observed in BW. The cholesterol levels observed in this experiment are within the normal range of values (69.2-239 mg/dL) proposed by Merck et al. [72], in all treatments, indicating that the diets were adequate for the observed production levels.
The plasma urea level was higher at the onset of lactation due to the energy deficit typical of this stage [66]. The increase in urea in C animals at the end of the experiment is due to an excess of protein ingested in the ration [73], which, together with the excess of ingested energy discussed above, would indicate that the energy/protein balance was adequate, but the total amount of feed consumed was higher than the animals' requirements. This can be confirmed by the fact that the significant increase in the body weight of the goats fed the C diet (also compared to the by-products containing diets) at the end of the experiment. The average urea content of BB animals was lower than that of the other treatments due to the lower protein intake along with the higher TP content of this diet [74].
Taking into account the cost of the AP ration presented in Table 1, the feeding was reduced by 14.64% compared to diet C (76.19 EUR and 89.26 EUR/animal, respectively, during the 23 weeks that the study lasted), without major changes in the milk yield and composition, and with the benefit of a healthier lipid profile for human health, as already explained. On the other hand, in the BB diet, the feeding expenditure was even lower (66.56 EUR/animal), although with the consequent reduction in milk yield.

Conclusions
The use of artichoke plant silage in diets of dairy goats in the tested proportion (40% on a dry matter basis) over a full lactation did not lead to marked differences in milk yield and quality or compromise the animal health status compared to the diet commonly used in intensive farms (alfalfa hay and mix of cereals and legumes). Regarding the lipid profile, from a nutraceutical point of view, the milk of goats fed with AP, especially at the end of lactation, is more beneficial as it has a lower n6/n3 ratio. AP is therefore considered an efficient alternative for the use of residues derived from the agro-industrial sector, which allows the production of animal feed of high biological value for humans, with potential benefits for human health.
Regarding the inclusion of broccoli by-product silage in 40% DM in the diet during a full lactation, with a composition similar to that tested in this study, it could generate a dietary selection that is superior to what is desired, which would reduce the feed intake and lead to a lower milk yield, and at the same time promote a lower recovery of body reserves at the end of lactation, together with worse AI and TI indices in milk. Therefore, it is concluded that it is convenient to study the inclusion of a lower amount of this ingredient in the ration, especially during the peak of lactation, or the presentation of BB in other ways that stimulate its intake when it is part of a TMR, in order to maintain animal performance while benefits for the circular economy are achieved.  Acknowledgments: Aprovertia S.L., a technology-based company of Miguel Hernández University of Elche, provided the facilities for manufacturing the silages.

Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript, or in the decision to publish the results.


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Medicine
Computer Science

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Electric Drive Supervisor for Milling Process 4.0 Automation: A Process Analytical Approach with IIoT NIR Devices for Common Wheat

The milling industry envisions solutions to become fully compatible with the industry 4.0 technology where sensors interconnect devices, machines and processes. In this contest, the work presents an integrated solution merging a deeper understanding and control of the process due to real-time data collection by MicroNIR sensors (VIAVI, Santa Rosa, CA)—directly from the manufacturing process—and data analysis by Chemometrics. To the aim the sensors were positioned at wheat cleaning and at the flour blends phase and near infrared spectra (951–1608 nm) were collected online. Regression models were developed merging the spectra information with the results obtained by reference analyses, i.e., chemical composition and rheological properties of dough by Farinograph® (Brabender GmbH and Co., Duisburg, Germany), Alveograph® (Chopin, NG Villeneuve-la-Garenne Cedex, France) and Extensograph®.(Brabender GmbH and Co., Duisburg, Germany) The model performance was tested by an external dataset obtaining, for most of the parameters, RPRED higher than 0.80 and Root Mean Squares Errors in prediction lower than two-fold the value of the reference method errors. The real-time implementation resulted in optimal (100% of samples) or really good (99.9%–80% of samples) prediction ability. The proposed work succeeded in the implementation of a process analytical approach with Industrial Internet of Things near infrared (IIoT NIR) devices for the prediction of relevant grain and flour characteristics of common wheat at the industrial level.

Introduction
In recent years, the needs along the value chain of wheat have changed. For each one of the many wheat uses, specific grain-quality requirements are preferred. The possibility to obtain reliable and quick information about kernels quality is constantly becoming more important for all the players of the wheat value chain. For instance, milling industry requires fast, simple and reliable methods for the control of grain quality during the reception phase, whereas the bread-making industry is searching for suitable methods able to predict the end-products quality. In the milling industry, quality control is of great importance for productivity maximization and standardization since wheat and flour have to respect specific compositional and functional requirements to satisfy customers' requests. Indeed, the physico-chemical properties of the raw materials strongly affect the properties of the doughs during kneading, the consistency of the endproducts and the process efficiency. In this context, along decades, several approaches have been developed and proposed to assess quality along the allow us to estimate ash, moisture, protein content, as well as dough rheological properties, on wheat kernel and flour directly on the plant, without the sampling procedure, thus to mix kernels and flours according to the desired quality characteristics of the final product.
The reader will find an explanation of the materials and methods used for the present industrial implementation in Section 2. In detail, the reference analyses, i.e., physico-chemical analysis and empiric rheological tests are presented in Section 2.1. Furthermore, the milling process and structure are schematically introduced together with the implementation of MicroNIR (VIAVI, Santa Rosa, CA) sensors (Section 2.2). The data analysis procedure and the used algorithm are presented in Section 2.3. The results structure will follow the same outline by presenting the results of the reference analysis and then the prediction capability of the developed models in term of the coefficient of determination (R 2 ) and error (Root Mean Square Error) and compared with the Standard Error of Laboratory (SEL). Lastly, the results of real time implementation of the models will be presented in terms of prediction by comparing the actual data, including the SEL, with the value predicted by the PLS models.

Materials and Methods
Common wheat (Triticum aestivum L.) milling was monitored from October 2018 to August 2019 directly in the milling plant in two strategic points, i.e., at wheat cleaning (with the probe placed on a drop pipe before the wetting phase) and at flour blending phase, on the homogenization conveyor. For physico-chemical analysis and empiric rheological tests, conventionally used in the cereal chain, samples were collected from the process line at defined points ensuring the correspondence with the sample undergone to spectral analysis. Furthermore, NIR spectra were collected during the month of September 2019 to be used for the real-time validation of the IIoT NIR system.
By Farinograph analysis the mixing properties of the dough were determined by pasting 300 g of flour with water. The volume of water required to hydrate the flour to a predetermined value is adjusted according to the moisture content of the sample and then the amount of water required for the dough to reach a definite consistency (500 UB) is added according to the ISO 5530-1: 1997 approved method [19]. From the mixing profile from which the following indices were extrapolated: water absorption (Far-Abs) corresponding to the water per 100 g flour to reach the optimal consistency (500 Farinographic Units, FU) and expressed as g/100 g; water absorption at 14% (Far-Abs14%); dough development time (Far-Dev) defined as the interval from the first addition of water to the point in maximum consistency range immediately before the first indication of weakening and expressed in minutes; stability (Far-Stab) defined as the time difference between the point where the top curve first intersects 500-FU and the point where the top curve leaves 500-FU line and expressed in minutes and degree of softening (Far-DS) is the difference between the consistency value of the curve center at the end of the developing time and the curve centre 10 min after starting the test and it is expressed as FU.
The three-dimensional extension properties of dough were determined by an Alveograph analysis according to AACC 54-30.02: 1999 [20]. A thin sheet of dough is inflated by air pressure to form a bubble; by determining the pressure and length of time needed to burst the bubble, information about the dough tenacity, elasticity and baking strength can be obtained. From the raw profile the following indices were extrapolated: tenacity (Alv-P) corresponding to the maximum pressure on deformation and expressed in mm H 2 O; extensibility (Alv-L) corresponding to the length of the curve and expressed as mm; P/L ratio (Alv-P/L); strength (Alv-W) corresponding to the area under the curve Sensors 2020, 20, 1147 4 of 14 and expressed in ×10 −4 J; index of swelling (Alv-G) expressed in mm and elasticity index (Alv-Ie), which compares pressure after 200 mL volume of air have been blown into the dough test piece and expressed as percentage.
From the Extensograph-E (ISO 5530-2:2012) [21] dough extensibility was determined at 135 min. The analysis was performed with 300 g of flour mix with a sodium chloride solution (2.5% weight/volume), rounded and molded into a cylinder under constant conditions then it is left to rest at 30 • C. After that, the piece is extruded by a hook, which travels downwards at a constant rate, and the load on the dough piece is recorded, at the end of the test the consistency should arrive to 500 BU. Moreover, the Extensograph curve was obtained and the following indexes were determined: energy (Ext-En) corresponding to the area under the curve and expressed as cm 2 ; extensibility (Ext-Ext) corresponding to the length of the curve and expressed as mm; resistance to extension (Ext-Res) related to the elastic properties and expressed as a Brabender Unit (BU) measured 50 mm after the curve has started and maximum resistance to extension (Ext-ResMAX) expressed as BU. Figure 1 shows a scheme of the overall milling process and structure. A series of electric motors was controlled, each one actuating a screw conveyor. The speed reference was generated for each motor by the smart reference generator. The generator was a component programmed in a computer that produced the speed signal based on the NIR feedback. Finally, the inverter blocks were power converters typically used in electric drives to control electric motors.

Online MicroNIR Sensor Implementation and Data Acquisition
Sensors 2020, 20, 1147 4 of 14 length of the curve and expressed as mm; P/L ratio (Alv-P/L); strength (Alv-W) corresponding to the area under the curve and expressed in ×10 −4 J; index of swelling (Alv-G) expressed in mm and elasticity index (Alv-Ie), which compares pressure after 200 mL volume of air have been blown into the dough test piece and expressed as percentage. From the Extensograph-E (ISO 5530-2:2012) [21] dough extensibility was determined at 135 min. The analysis was performed with 300 g of flour mix with a sodium chloride solution (2.5% weight/volume), rounded and molded into a cylinder under constant conditions then it is left to rest at 30 °C. After that, the piece is extruded by a hook, which travels downwards at a constant rate, and the load on the dough piece is recorded, at the end of the test the consistency should arrive to 500 BU. Moreover, the Extensograph curve was obtained and the following indexes were determined: energy (Ext-En) corresponding to the area under the curve and expressed as cm 2 ; extensibility (Ext-Ext) corresponding to the length of the curve and expressed as mm; resistance to extension (Ext-Res) related to the elastic properties and expressed as a Brabender Unit (BU) measured 50 mm after the curve has started and maximum resistance to extension (Ext-ResMAX) expressed as BU. Figure 1 shows a scheme of the overall milling process and structure. A series of electric motors was controlled, each one actuating a screw conveyor. The speed reference was generated for each motor by the smart reference generator. The generator was a component programmed in a computer that produced the speed signal based on the NIR feedback. Finally, the inverter blocks were power converters typically used in electric drives to control electric motors.

Online MicroNIR Sensor Implementation and Data Acquisition
In detail, a screw conveyor is the base of a typical milling process: by using a rotating helical screw installed within a tube or trough, the screw conveyor is responsible for the movement of the flour. The amount of volume transferred is proportional to the rotation of the screw. For this reason and since an electric motor actuates the conveyor, the motor itself must be controlled. The control is performed so to grant the correct flow of flour needed in the process. MicroNIR sensors (VIAVI, Santa Rosa, CA) were installed inside the screw conveyor structure so to record and analyze wheat data in real-time in a non-destructive manner.  In detail, a screw conveyor is the base of a typical milling process: by using a rotating helical screw installed within a tube or trough, the screw conveyor is responsible for the movement of the flour. The amount of volume transferred is proportional to the rotation of the screw. For this reason and since an electric motor actuates the conveyor, the motor itself must be controlled. The control is performed so to grant the correct flow of flour needed in the process. MicroNIR sensors (VIAVI, Santa Rosa, CA) were installed inside the screw conveyor structure so to record and analyze wheat data in real-time in a non-destructive manner.
The NIR spectra were collected for each sampling point using a MicroNIR PAT-W (VIAVI, Santa Rosa, CA) and a MicroNIR PAT-U (VIAVI, Santa Rosa, CA) directly interfaced with the milling plant. The spectra were acquired in the spectral range of 950-1650 nm in diffuse reflectance, every 60 s with 7.3 µs of integration time. The collection was performed on kernels and flours for a total of more than 5000 spectra, considering that for each sample, i.e., grain or flour sampled from the process line, 10 spectra were measured.

Data Analysis
The spectra collected from October 2018 to August 2019 were averaged on sample bases, i.e., averaging the 10 spectra corresponding to the same grain or flour removed from the process line for the reference analyses. The spectra dataset was transformed by smoothing (Savitzky-Golay, 3 wavelengths gap size) followed by first derivative (Savitzky-Golay, 3 wavelengths gap size and 2nd order polynomial) and mean center in order to highlight the spectral features covered by the characteristic broad bands present in the NIR signal, thus allowing one to solve better the information retained in this kind of data. The pretreated spectra were used as the independent variable, X, whereas, physico-chemical and rheological results were used as the dependent variable, Y, for the Partial Least Squares (PLS) regression model development. PLS is one of the most common multivariate algorithms for linear regression development in chemistry and technology. It well manages the problem of noise, collinearity and large number of variables, thus, being ideal for NIR data. Indeed, PLS returns a model of X as a bilinear projection considering that the information retained in X is uncorrelated to the Y solving noise and irregularities of X and not related to Y. In detail, the totality of the data was randomly split into a calibration dataset to be used for model development and a validation dataset to test the prediction capability for both kernel and flour samples.
The number of samples used for calibration differs for each PLS model as not all the destructive analyses were performed on all the samples due to complexity and time required for their execution; however at least 90 samples/objects were used for this purpose.
A regression model was calibrated for each physico-chemical compound or empiric rheological feature, i.e., each single parameter determined with the conventional methods was considered separately as a dependent variable for the construction of the calibration models. The models were cross-validated by venetian blind to choose the optimal number of latent variables (LVs) by minimizing the error in cross-validation and then tested in prediction by the validation dataset.
The performance of the models in calibration, cross-validation and prediction was evaluated in term of coefficient of determination, R 2 , and Root Mean Square Error (RMSE), which reports the error of the PLS model expressed in the same units of the dependent variable. Furthermore, the prediction capability was evaluated by comparison of the predicted value for each parameter and the laboratory reproducibility, i.e., coefficient of variation (CV) or Standard Error of Laboratory (SEL).
The prediction ability of the developed models was tested online with the sampling occurred on September 2019. This further test was performed to consider the model robustness with different type of kernels and flour and in a different season. The sampling was performed as previously described, and then the new spectra were used as an independent test set. The predicted values of these new data by the previously developed models were compared with the measured characteristics. The performance of the model was evaluated in terms of prediction by comparing the actual data, including the laboratory error, with the value predicted by the PLS model.
All the data analyses were performed in Unscrambler X software version 10.4 (CAMO, Trondheim, Norway).

Chemical Composition
The chemical composition of the samples is summarized in Table 1. The moisture content of the samples was in the range required by the Italian law [22], with a maximum content of 15.5%. The ash Sensors 2020, 20, 1147 6 of 14 content ranged from 0.5 to 1.72 g/100 g, suggesting that the selected samples belonged to a wide range of extraction rate (from refined flour to whole meal flour). According to the Italian law [22] the refined flour must have a maximum level of ash of 0.55 g/100 g whereas the ash content in the whole meal flour has to range between 1.3 and 1.7 g/100 g. As regards the protein content, the values were above 9.00 g/100 g, which is the minimum values required according to the Italian law. Based on the Italian wheat classification, the protein content-together with protein quality-is related to the end-use of the flour. Specifically, flours with a protein content below 11% are used for biscuit or pastry, flours with a protein content between 11 and 13 are used for bread, whereas flours with protein content higher than 14% are used to produce baked products that require long fermentation time. Finally, the gluten content of the samples ranged from 7.10 to 14.50 g/100 g. Neither the Italian law nor the classification includes the gluten content as an index of quality. On the contrary, dough rheology is used to define wheat quality (see paragraph below). For all the indices here considered, the SEL values ranged from 3% and 7%, for gluten and protein content, respectively.

Rheological Properties
Data from the main rheological tests carried out on either kernels or refined wheat flours are reported in Table 2.
The Farinograph test is widely used to measure dough behavior during mixing (i.e., water and time required to make a dough, and its stability during mixing), whereas, the Alveograph test is widely used to measure the tenacity (Alv-P), strength (Alv-W) and the extensibility (Alv-L) of a dough subjected to a 3D-extension of the dough due to the insufflation of air. On the contrary, the dough resistance to a uniaxial extension is given by the Extensograph test.
According to the Italian wheat classification, improved wheats are characterized by W ≥ 300 × 10 −4 J, a P/L between 0.6 and 1 and a farinographic stability (Far-Stab) ≥ 15 min; superior bread making wheats have W between 160 and 220 × 10 −4 J, P/L ≤ 0.6 and stability between 10 and 15 min. In addition, wheats with W 115 and 160 ×10 −4 J, P/L ≤ 0.6 and stability between 5 and 10 min are classified as ordinary bread making wheat. Finally, wheat flours with W ≤ 115 × 10 −4 J, P/L ≤ 0.5 and stability < 5 min are used for biscuits [4]. The samples considered cover the whole variability of the parameters defined by the Italian law, also exceeding the classification ranges as to represent the possible variability of raw materials managed by the milling plant, thus providing a more robust starting point for the regression model development.  [24] attributed features in the regions between 1154-1166 and 1456-1472 nm related to moisture content and the signal between 1578 and 1582 nm to starch absorption. Similar spectra profiles were observed for the spectra collected for kernel and flour even though the kernel spectra are characterized by higher absorbance Figure 2a, which remains visible in the profiles after smoothing (Savitzky-Golay, 3 wavelengths gap size) and first derivative transformation (Savitzky-Golay, 3 wavelengths gap size and 2nd order polynomial) Figure 2b.   Degree of Softening (Far-DS); tenacity (Alv-P); extensibility (Alv-L); P/L ratio (Alv-P/L); strength (Alv-W); index of swelling (Alv-G); elasticity index (Alv-Ie); Energy (Ext-En); Extensibility (Ext-Ext); Maximum Resistance to extension (Ext-ResMAX); Brabender Unit (BU).

Kernel and Flour Characterisation by Near Infrared Spectroscopy
According to the Italian wheat classification, improved wheats are characterized by W ≥ 300 × 10 −4 J, a P/L between 0.6 and 1 and a farinographic stability (Far-Stab) ≥ 15 min; superior bread making wheats have W between 160 and 220 × 10 −4 J, P/L ≤ 0.6 and stability between 10 and 15 min. In addition, wheats with W 115 and 160 ×10 −4 J, P/L ≤ 0.6 and stability between 5 and 10 min are classified as ordinary bread making wheat. Finally, wheat flours with W ≤ 115 × 10 −4 J, P/L ≤ 0.5 and stability < 5 min are used for biscuits [4]. The samples considered cover the whole variability of the parameters defined by the Italian law, also exceeding the classification ranges as to represent the possible variability of raw materials managed by the milling plant, thus providing a more robust starting point for the regression model development.  The expected difference is related to both physical effect (different kernel size and flour particle size) and chemical characteristics of the two products, mainly water content. However, it is difficult to associate bands in the considered region of NIR spectra for samples so rich in moisture to specific vibrations; clearly a multivariate approach is needed to uncover the information hidden in the broad band characterizing the NIR spectra. The expected difference is related to both physical effect (different kernel size and flour particle size) and chemical characteristics of the two products, mainly water content. However, it is difficult to associate bands in the considered region of NIR spectra for samples so rich in moisture to specific vibrations; clearly a multivariate approach is needed to uncover the information hidden in the broad band characterizing the NIR spectra.

PLS Regression Models for Kernel and Flour Characterisation
The results of the PLS models for the 18 reference parameters are reported in Table 3 in terms of samples used to calibrate and validate the models, model dimensionality (i.e., number of selected latent variables, LVs), RMSE and R 2 or R statistics. The representativeness of the validation set has been Thirteen out of eighteen models reached R PRED higher than 0.80 and RMSEP lower or comparable to the CV of the reference analysis. As expected, models developed for composition parameters (ash, moisture and protein) obtained the highest R PRED , being 0.96, 0.98 and 0.93, respectively.
Wheat protein content has been one of the major targets for PLS models development from NIR data reaching optimal coefficient of determination and RMSE in prediction in different studies such as 0.97 and 0.31 g/100 g, respectively [25]. Even though in our study the two figure of merits were slightly lower, it should be considered that the number of samples used to build the model by Shi and Peiqiang (2017) [25] was lower (n = 80) than ours (n = 223); furthermore their study was performed by benchtop instrument analyzing the whole Vis-NIR range (680-2500 nm) and at laboratory scale. Similar consideration could be done for moisture and ash. Indeed, Dong and Sun (2013) [26] obtained a moisture predictive model with RMSEP of 0.088 g/100 g and a R PRED of 0.98 and an ash predictive model with RMSEP of 0.019 g/100 g and a R PRED of 0.91. As far as concern gluten, PLS models developed by different research groups led to controversial results, however high performances were obtained in prediction RMSEP of 0.81 g/100 g and a R 2 PRED of 0.98 [27], close to our validation performances. The model prediction ability for Far-Abs is in line with literature survay, which also in this case considered mainly laboratory scale studies. Indeed, the resulting RMSEP (1.21 g/100 g) is included in the 1.0-1.6 g/100 g range [11,12]. Far-Stab models gave higher error in prediction (RMSEP of 6.24 min) when compare to literature results (around 1 min of error in prediction, SEP) [13], however, the mentioned authors worked with few samples (n = 79) at the laboratory scale.
The degree of softening, Far-DS, has been generally not well predicted in previous studies such as the models reported by Chen  PLS models developed for the indexes extrapolated from the alveographic analysis gave reliable results with the exception of Alv-P/L, however the poor P/L regression curve is not even a relevant issue as the single parameters, Alv-P and Alv-L could be predicted by the specific PLS models for then calculating their ratio, thus skipping the modeling failure.
The model developed for W gave really good results, being the error in prediction of 28.67 × 10 −4 J in line with a previous work [12] or even lower with respect to Dowell et al. (2006) [14]. The performance of the model has a quite relevant impact in the cereal industry as it is related to flour strength, generally considered to define the technological behavior of flours and their end use. Good results were also obtained for Alv-G and Alv-Ie, however it is difficult to compare with previous findings as, in both cases, only one study developed a PLS model. Moreover, the model developed for Alv-G was not tested in prediction [14] and the Alv-Ie model did not give reliable results [13].
Regarding the PLS models developed from the Extensograph analysis, the Ext-En and Ext-Ext models gave good results, with low error and high R, however the Ext-ResMAX was not that reliable in terms of R, being the lowest value obtained, and error as well.
To establish the effectiveness of the prediction ability it is relevant to compare the regression models' error with the errors of the reference methods. According to Shenk and Westerhaus (1996) [28] if the SEP is less than two times the value of reference method error (SEL), the model's performance in terms of error in prediction should be considered an excellent estimation. In Table 4 are reported the minimum and maximum SEL for each parameter and the 2SEL, i.e., the threshold below which the SEP should be to have an excellent estimation by the PLS models. The models tested for moisture, protein, Far-DS, Alv-G, Ext-En and Ext-Ext are characterized by SEP lower than the 2SEL calculated for the minimum value of each considered parameter. All the other PLS models predicted each parameter with a SEP lower or equal to the maximum 2SEP, except for Far-Abs 14% and dough development time (Far-Dev) Table 4.

Online Testing of the Prediction Ability of the PLS Models
The models developed in the first phase, i.e., using the data acquired from October 2018 to August 2019, were tested for real-time prediction. In this way, the underlying idea is to exploit the PLS model prediction to generate the reference signal sent to the motor so to adapt the process behavior based on the real-time wheat and flour characteristics and the desired quality of the product. In other words, the desired speed of the motor is automatically adjusted in order to meet the production requirements.
The predicted values for all the developed models were compared with the measured characteristics. The performance of the model was evaluated in terms of prediction by comparing the actual data, including the laboratory error, with the value predicted by the PLS model. Table 5 summarizes the prediction ability for wheat kernels and flour for all the evaluated parameters. The models that obtained more than 100% of correctly predicted samples were judged as optimal, from 99.9 to 80 really good, from 79.9 to 70 good, from 70 to 50 fairly good and below 50 scarce. From Table 5 it is possible to understand that all the models gave optimal or really good prediction ability when used in prediction on kernels, with the exception of Far-Stab and Far-DS. P/L index was not considered due to the scarce prediction ability obtained for the models in the previous steps and the possibility of calculating the ratio from the P and L prediction.
On the other hand, the real-time testing for flour data had poorer performances when considering the prediction of farinographic results, whereas for the other parameters the real-time monitoring gave promising results for the industrial implementation. For the future it is envisioned the inclusion or new flour data in the calibration set to increase the variability of flour especially in terms of farinographic indexes.

Conclusions
In the described process, the interconnection between milling plants and the outputs of the MicroNIR sensors made possible the automatic control of the electric motors in real-time, avoiding the need for a manual tune of the screw conveyor behavior and the motor controller. Moreover, the interconnection between devices allowed for coordinating each motor. In this way, higher productivity and quality was reached when compared to a non-automatic and non-interconnected control of the drives. The developed models guaranteed the prediction of chemical composition and rheological properties of dough (indexes extrapolated from Farinograph®, Alveograph®and Extensograph®) with R PRED higher than 0.80 and RMSEP lower than two-fold the value of the reference method errors. Furthermore, the real-time implementation resulted in optimal (100% of samples) or really good (99.9%-80% of samples) prediction ability.
The presented idea is an innovative solution for the milling process control since it exploits the potentialities offered by Industry 4.0 where sensors interconnect devices, machines and processes. The result was optimal management of the production process that can successfully predict different product characteristics and automatically adapt itself to grant the desired level of quality to the product. In accordance with the principle of Zero-Defect Manufacturing, the outcome was the minimization of the probability of product failures and complaints from customers. Indeed, in the milling industry the bland of flours/kernels with specific properties was the key point in order to obtain final product respecting the quality characteristics required by costumers according to the end use of the product itself. Due to the proposed approach-combining NIR spectroscopy, Chemometrics and the integration of intelligent systems-the process parameters could be controlled in real time. Thus, unforeseen issues about the production policies or product characteristics could be predicted, and the loss of the final product could be avoided.
Even though a robust sampling plan was performed along an entire year, a future perspective could be the transfer of the developed models to other milling plants, thus creating an IoT not just along one transformation chain but horizontally along more production lines. Together with that, NIR sensors could be positioned at other strategic plant levels, such as at raw materials delivery allowing the kernels control for characteristic parameters. Funding: Part of this research was carried out in GreenMill4.0 project (grant number N7P89U5) co-funded by Apulia Region-Italy through the Regional Operational Program under the European Regional Development Fund for 2014-2020.