**Abstract:** Banana and plantain (*Musa* spp.) are very important crops in Ecuador. Agricultural production systems based on a single cultivar and high use of external inputs to increase yields may cause changes in the landscape structure and a loss in biodiversity. This loss may be responsible for a decrease in the complexity of arthropod food webs and, at the same time, related to a higher frequency and range of pest outbreaks. Very little is known either about the ecological mechanisms causing destabilization of these systems or the importance of the diversity of natural enemies to keep pests under control. Few studies have focused on this issue in tropical ecosystems. Here, we address this problem, comparing two *Musa*-based agroecosystems (monocultivar and mixed-species plantations) at two sites in Ecuador (La Maná and El Carmen) with different precipitation regimes. The diversity of soil macro fauna, represented by arthropods, was established, as indicators of the abovementioned disturbances. Our ultimate goal is the optimization of pest management by exploring more sustainable cropping systems with improved soil quality. Arthropod abundance was higher in the mixed system at both localities, which was clearly associated with the quality of the soils. In addition, we found Hymenoptera species with predatory or parasitic characteristics over the pests present in the agroecosystems under study. These highly beneficial species were more abundant at the locality of La Maná. The mixed type of production system provides plant diversity, which favors beneficial arthropod abundance and permits lower agrochemical application without yield penalties in comparison to the monoculture. These findings will help in the design of *Musa*-based agroecosystems to enhance pest control.

**Keywords:** monocultivar; mixed-species plantation; biodiversity; arthropod; soil; on-farm biodiversity indicators

## **1. Introduction**

At present, biodiversity is being lost at an unprecedented rate due to human activities. Research has devoted a great deal of effort to assess the importance of biodiversity for the functioning and stability of agroecosystems and for the provision of environmental services. Pest management has become more efficient on numerous occasions, as a valuable environmental service provided by biodiversity. However, this is threatened by human activity [1,2].

It is well known that agricultural production systems are intensified by enhanced use of external inputs to increase yields, causing a change in the landscape structure.  Studies carried out by Michalko and Košuliˇc [3] showed that components of biodiversity peaked at different levels of canopy openness on oak forest stands. Therefore, the restoration and suitable forest management of such conditions will retain important diversification of habitats. Diversity is an index composed of two variables: the abundance of species (or groups of species) and the equitability (or uniform distribution of individuals between groups) [4]. Michalko and Košuliˇc [3] suggested that the permanent presence of small-scale improvements could be suitable conservation tools to prevent the general decline of woodland biodiversity in the intensified landscape, and proposed that all the suggested improvements would promote species diversity, conservation aspects, and functional diversity. A loss in plant diversity decreases the complexity of arthropod food webs that could be related to the observed higher frequency and range of pest outbreaks [5]. However, little is known about the ecological mechanisms that result in this destabilization or the importance of the diversity of the natural enemies to keep the pest under control [6]. In this context, it is essential to identify those species/orders/families that, with their presence or absence, are important indicators of the quality of the agroecosystem under study. The soil microbiota represents the largest group of terrestrial animal organisms; among them, the phylum Arthropoda is considered one of the most important for man; despite its small size, they are visible to the naked eye, have a relatively short life cycle and fulfill numerous environmental services [7].

In the last decade, the diversity of the different production systems has been a matter of concern due to changes in ecosystems by human activities in agriculture, livestock and forestry [8]. These actions cause a great alteration in the processes of configuring the habitat of the organisms occupying that environment, causing negative effects on the diversity of the macro fauna and altering the balance between the ecosystem, the soil and the plants [9]. We suppose that all the suggested improvements would promote species diversity, conservation aspects, and functional diversity. These are essential to ecosystem functions and the restoration of forest environments in landscapes under intense human land use.

The decrease in diversity and abundance of the edaphic macro fauna caused by human activity has generated concerns that have stimulated the development of research on the impact of different types of tillage on soil biota. A clear example is described by Arroyo and Iturrondobeitia [10], who evaluated the diversity of arthropods in forests and different monocultivar agricultural systems, finding high values of species richness in forest areas, in contrast with the agroecosystem receiving fertilization and a general management of the crop. These kinds of studies are very scarce in the tropics, especially those related to agricultural crops.

Banana and plantain monocultivar plantations in Ecuador have been established in areas where primary forest has been eroded. A feature of these tropical soils is their dependence on the biomass and species diversity of the forest that covers it. Once the protective cover of the forest is eliminated, the productivity and fertility per unit area decreases.

Banana monocultivar are affected by many pests due to their weak ecological balance [11]. This drastically reduces the yield after the first two cropping years. For this reason, the banana (*Musa balbisiana*) producers need large areas of land and the consequent expansion to compensate for the fall in production per hectare. Extensive commercial banana crops are governed by technological standards aimed towards the intense production of fruit for export. Plantains (*Musa acuminata*), instead, besides being an important export crop, are also an essential staple food of coastal Ecuador and many other countries along the tropics, where diversity in respect to pest and disease pressures exists. Although farmers try to apply basically the same management practices as banana enterprises, plantain is in the hands of medium and small farmers with limited access to resources and who use lower levels of technology.

There are different approaches to tackle those problems. One approach intensively used is integrated pest management (IPM) strategies, focusing on using agronomic management techniques to reduce pesticide use, but IPM concentrates on modifying the environment around predominantly modern cultivars and has tended to exclude the potential of using within-crop diversity through genetic mixtures (crop variety mixtures) for example, or the planned deployment of different varieties in the same production environment. A diverse genetic basis of resistance (e.g., crop variety mixtures) is beneficial for the farmer because it allows a more stable management of pest and disease pressure than what a monocultivar system allows.

Intraspecific biodiversity increases resistance to pests in crops, developing more biological support, thus, ensuring production. Greater intraspecific biodiversity improves the biota in the soil, creating synergy with the crop. The mixture of natural and human selection gives the particularity of the environment for different agroecosystems. It requires agricultural species with genetic characteristics that adapt to the different environments. The most palpable case is that of maize, conducted in different climatic zones under different constrains [12].

There is very little information and research work of this kind in the region, therefore, the development of a project in progress under an agreement between the Technical University of Quevedo, Ecuador and Bioversity International [13] was used to conduct this study in order to determine possible bioindicators of the use of intraspecific biodiversity in two banana production systems on the Ecuadorian coast. The present work aimed at developing a study of the *Arthropoda* population over two Musa agroecosystems (single and mixed Musa cultivar plantations, at two sites in Ecuador (La Maná and El Carmen)), to establish its value as a bioindicator under Ecuadorian conditions.

## **2. Materials and Methods**

## *2.1. Site Description*

The study was conducted at two sites in Ecuador with different soil characteristics and precipitation regimes (Table 1) in 2014. La Maná site is located in the province of Cotopaxi (00◦53 43" S; 79◦11 05" O) at 354 m altitude. El Carmen site is located in Manabí province (00◦16 14" S; 79◦29 12" O) at 250 m altitude.

**Table 1.** Soil and weather characteristics of both sites and agricultural systems studied during 2014.

1 Source: National Institute of Meteorology and Hydrology (INAMHI), San Juan Station (La Maná) and El Carmen Station (Manabí).

## *2.2. Experimental Design*

The experimental sites consisted of two plots of 1 ha each. One plot is for traditional monocultivar system and the other for the mixed-species. The plot with the traditional system was established more than 30 years ago and was based on the cultivar Orito (Musa acuminata AB type of genome) in La Maná site, and cultivar Barraganete (Musa balbisiana AA type genome) in El Carmen site. The other plot corresponded to a mixed Musaceae system based on 12 different Musa cultivars, planted in 2009 (Table 2). Each cultivar was represented by subplots of 24 plants, with borders of plants of the local cultivar. Each subplot was repeated three times and randomly distributed in the ha. A spacing of 3 × 3 m (1111 plants ha−1) was adopted in all plots.

**Table 2.** *Musa* ecotypes used in the mixed-species plot according to their genome.

## *2.3. Agronomic Management in the Experimental Plots*

In both sites, the traditional systems were established by deep tillage practices, while tillage was minimum in the mixed systems. The main annual field operations are summarized as follows:

## *2.4. Arthropod Abundance and Identification*

Arthropod samples were taken randomly from both (mixed and monocultivar) sites (La Maná and El Carmen) and two seasons of the year (rainy and dry). To collect the most representative taxes, two different trap systems were used: a pitfall trap consisting ofa1L capacity vessel, buried at ground level during 72 h, to catch organisms falling into the container filled with water and liquid detergent. The second type of traps were "Chromatic" traps, which consists of yellow plastic plates (18 cm diameter) placed for 3 h on the floor, with a solution of water and liquid soap. A total of 80 samples were taken—40 for each site—and these, in turn, subdivided into 20 subsamples for each production system and 10 for each type of trap used. The collected individuals were taken to the university laboratory, where they were quantified and classified up to the taxonomic level of the order, dividing them by phylum, class (insect, arachnid, myriapod, mollusks and annelids) and orders. The indicators of biodiversity of individuals that constitute the soil macrobiota were determined for the total of arthropods found, which were described according to criteria of Moreno [14].

To determine the abundance of each arthropod taxon (order), two sampling periods were carried out, one between January and May during the rainy season and another between June and November during the dry season.

Arthropods were identified using taxonomic keys [15] or arriving at the level of orders and family and as far as possible; they were differentiated between beneficial and harmful specimens.

## *2.5. Statistical Analysis*

Based on the total structure and number of the arthropod community collected, comparison of means of the abundance of orders or groups of arthropods were performed and a multifactorial analyses of variance (ANOVA) was carried out with factors: site (El Carmen y La Mana), season (rainy and dry season) and type of cultural system (mixed and monocultivar). Calculations were done using the R Commander program [15]; values of each plot were previously transformed to √(x + 1).

In order to determine the abundance, richness, diversity (Simpson indexes), similarity (Jaccard coefficient) and equitability (J) of the soil macro fauna by site and production system, the PAST statistical program [16] was used.

The Simpson diversity index was calculated as λ = Σpi2, where pi = proportional abundance of species i, that is, the number of individuals of species i divided by the total number of individuals in the sample during the time period considered. This index shows the probability that two individuals taken at random from a sample are of the same species. It is strongly influenced by the importance of the most dominant species [16].

To determine the relationships between factors and arthropod abundances, a Principal Component Analysis (PCA) was performed, in such a way that the distribution of the sites and agricultural systems was visualized both in the rainy and dry season, taking into account the characteristic uncorrelated environmental factors, which explain much of the original total variability.

## **3. Results and Discussion**

The influence of the three factors (mixed and monocultivar agricultural systems; season and sites) on the abundance of different arthropod orders are shown in Table 3. The insect orders, Coleoptera and Diptera, were significantly influenced by the agricultural system, while site was a determinant factor for the orders Hymenoptera, and Prostigmata (*p* > 0.01). Orders Collembola, Hemiptera and Orthoptera were represented in all factors. The season of the year had highly significant influence on the majority of the orders found. There were no significant differences between the two cropping systems and the localities for the orders Araneae, Diptera, Hymenoptera, and Spirobolida. These orders have great adaptation capacity to diverse environments. The long life-cycles of some of these species could be responsible for their presence throughout the year [17]. In addition, some species influence the transformation of biodegradable waste, especially the organic matter deposited on the soil surface, incorporating it into the edaphic system, through the tunnels and channels that the coleoptera excavate. This facilitates infiltration and aeration of the soil [18].

**Table 3.** *Cont*.

Arthropods from our field captures were distributed in three classes and eight orders (Table 4). In La Maná, we found that 56% of individuals belonged to order Collembola and 32% to Hymenoptera, while in El Carmen, 61.5% of them were classified into the order Hymenoptera. The least represented group were from the order Spirobolida and Prostigmata, with less than 0.5% values at both sites.

**Table 4.** Arthropod abundance distributed by taxonomic groups during the rainy and dry seasons in two ecological sites (El Carmen and La Maná) in 2014.

Irrespectively of the site and season factors, the type of agricultural system factor seems to influence the presence of orders Coleoptera, Collembola, Hemiptera and Orthoptera and project as good indicators of the differences between monocultivar vs. mixed systems. Differences were found between ecosystems: La Maná had more abundance in the quantity of specimens than El Carmen. La Maná is an area with higher relative humidity which indicates that this habitat presents more favorable conditions for the development and conservation of these insect groups, making this agroecosystem more stable and diverse.

Our results on species abundance are in accordance with [19], who affirmed that the collembola play an important functional role in the decomposition processes of dead plant matter, the nutrient cycle, and help in the formation of soil characteristics. As observed in agroecosystems from other latitudes [20,21], Collembola abundance can be related to climatic and edaphic factors, availability of nutrients and biodiversity. Collembola seems to represent the diversity and abundance of species in the agroecosystems, since there was a direct relation between their abundance and the edaphic humidity [22–24]. Thus, in La Maná, the abundance in Collembola indicates that there would be sufficient environmental humidity, even in the dry season.

The largest number of individuals (2686) was obtained in the mixed system from La Maná (Table 5), while the amount of arthropods captured in El Carmen was quite similar in both production systems, and in total, had 40% fold inferior to that of La Maná. The difference between sites can be due to the site characteristics where higher precipitation, temperature, relative humidity and the different soil texture, and a higher presence of soil organic matter measured in La Maná. Furthermore, both the stability of the system (5 years vs. 30 years) and the application of agrochemicals higher in the monocultivar than in mixed systems may account for the observed differences. Our results are in concordance with those of Lavelle et al. [8], who found that arthropod in soils in humid tropics, as El Carmen, are inferior in abundance because they must be adapted to habitats where micro-climate fluctuations can be very strong and usually have compact soils with low oxygen concentration and high brightness, and with few open spaces and low availability and quality of food.

La Maná presented an index of 0.59, indicating that this site has the characteristics suitable for the habitat of different organisms, thus, being a balanced ecosystem. In contrast, in El Carmen an index of 0.49 was obtained, being this ecosystem largely unbalanced and with less favorable conditions for the development of many arthropods (Table 6).

According to data from the Simpson (1-D) diversity indexes, it can be observed that the highest diversity of arthropods is found in soils from mixed farming systems. The agricultural system influences the equity (e ˆ H/S). The number of arthropods was higher in the mixed system plots than in the monocultivar ones (Figure 1). On the other hand, agricultural systems and sites influenced the dominance (D) and equity (J) of arthropods. The dominance index was higher in monocultivar systems, while the equity index was higher in mixed systems. To compare different agricultural systems (mixed versus monocultivar), several authors have used the richness of families. Wickramasinghe et al. [19] compared family richness among 24 pairs of mixed and monocultivar farming systems in Great Britain. They observed higher abundance and richness of insect species in mixed systems than in soils with single cultivars.

**Table 5.** Number of individuals registered in each type of production system at both sampling sites in 2014.

**Table 6.** The Simpson diversity index for each taxonomic group at each site in 2014.

**Figure 1.** Simpson diversity index (1-D) and its three components; Dominance (D), Evenness (eˆH/S) and Equitability (J) in function of the agroecosystem and locality, considering the amount of arthropods found.

According to the Jaccard coefficient, the highest similarity based on arthropod families was found between the mixed and the monocultivar systems at both localities, with 80% (Table 7). At the level of orders, a closer similarity of 100% between monocultivar (La Maná) and mixed (El Carmen) systems was observed. Mixed systems (La Maná) and monocultivar (El Carmen) presented low percentage of similarity (Table 8).

**Table 7.** Jaccard index of similarity (%) based on different arthropod families between sites and production systems in 2014.

**Table 8.** Jaccard index of similarity (%) based on different arthropod orders between sites and production systems in 2014.

Among the observed arthropods, there were families categorized as predators or parasitoids of pests. These are highly beneficial for the crops. In banana, parasitoids are important biological control agents that regulate several pests, mainly diverse lepidopteran defoliators [20–23]. Braconidae was one of the most common families with beneficial insects. These parasitoid wasps are recognized as being endo or ectoparasitoids with idiobiont strategy exclusively attacking Lepidoptera, Coleoptera and *Diptera* during different stages of development. The tiny wasps of the Diapriidae family were commonly found in wet microhabitats and shady areas, such as La Maná ecosystem. They are mainly endoparasitoids and primary predators on larvae and pupae of a wide range of insects, especially flies (Diptera) [24,25]. Several studies in different polyculture models have shown that the composition and diversity of plants in or around the production systems have an influence on the number of insects (including parasitoids). The reason is that the diversity of plants attracts natural enemies by offering resources such as micro-habitat and food that, specifically on parasitoids, can be determinant for their species richness, their longevity and their level of parasitism [26,27]. The parasitic Hymenoptera are also good indicators of pesticide impacts, because they are more sensitive to pesticides than most other insects, including their host species [28,29].

Diapriids were the only parasitoids more abundant and with a higher species richness in conventional banana than in the mixture plot [30]. In our study, Braconidae and Diapriids (Table 9) were more abundant in mixed systems with moderate agrochemical inputs than in monocultivar with intensive agrochemical applications in both sites. Taking into consideration all the orders found independent of the agroecosystem, La Maná had three times more beneficial arthropods than El Carmen.

The principal component analysis (PCA) revealed scarce dispersion of the points for sites orders agricultural systems (Figure 2). This could be related to the relatively high number of specific orders favoring the characterization of ecosystems. PCA showed higher dispersion and fewer arthropods in the rainy season than the dry season, where the largest number of arthropods was concentrated. A greater variability among the orders observed in the different localities and agricultural systems were found in both seasons of the year (rainy and dry). In La Maná, Hemiptera and Collembola were closer related to the dry season and the mixed agricultural system, whereas Coleoptera and Orthoptera were more related to dry season and monocultivar production system. In El Carmen site, Hymenoptera, Arachnida and Diptera are more abundant in the dry season and mixed production

system and Prostigmata in the dry season and monocultivar agroecosystem. These two axes account for 55.0% of the total variability observed (Figure 2).

**Table 9.** Number of beneficial arthropods found in the different agroecosystems and sites.

**Figure 2.** Principal component analysis (biplot) between sites, season, agricultural systems and number of arthropod orders found.

One of the most important aspects derived from these results are that a more diverse system presents a greater diversity of associated arthropods, depending on scale and context. This diversity is closely linked to the climatic conditions of the site, and the diversity of plants in the ecosystem [31], and it seems that the intraspecific diversity used in this study is enough to fulfil the needs of a diverse community of arthropods. Haddad et al. [32] demonstrated that the identity of plant species determines the abundance of arthropods, as well as affects different ecological processes within agricultural agroecosystems. The most relevant processes are biomass accumulation, decomposition rates and floor moisture. Thus, the results of arthropod diversity can be more influenced by the effects of the composition of plants than by the effects of the number of species [32,33]. The study by Rzanny et al. [33] was conducted in a temperate region, where winters induce an annual collapse in the abundance of all taxa.  We argue that the relatively stable conditions in the mixed *Musa*-based agroecosystems of

the current study (these systems were at least five years old) allowed a stable plant community over years, and the presence of a perennial crop reinforces this stability. Another factor that contributes to the establishment of a relatively stable arthropod community in this mixed Musaceae agroecosystem is the low to moderate application of pesticide treatments.

## **4. Conclusions**

Abundance analysis of the macro edaphic fauna and identification of the beneficial families allowed the determination of the equilibrium level of the Musaceae agroecosystem. The mixed type of production system provides plant diversity, which favors arthropod abundance and permits lower agrochemical application without yield penalties in comparison to the monocultivar. Within Hexapoda, the orders that presented larger populations were Collembola and Hymenoptera, based on the abundance and distribution they presented. The order Hymenoptera dominated in all the treatments, both by its abundance and by its distribution in the studied localities, even in ecosystems with ecological imbalance. Consistent with results from temperate studies, the mixed Musaceae production system was the one with the greatest presence of soil macro fauna, with the order Collembola being the most diverse, which gives us the guideline to say that this order is associated with the quality of the soils.

The management practices in agroecosystems can alter the community structure of pests' natural enemies, which can consequently influence their biocontrol. Since the functional composition of natural enemy communities, rather than taxonomic diversity, drive pest suppression efficiency, it is necessary to employ the functional approach to investigate the impact of management on natural enemies.

Our findings show that intraspecific diversity could be a good option to include in an IPM strategy for small and medium farmers and may help in the design of Musaceae agroecosystems to enhance the ecological regulation of pest management, without putting on the farmer the constraint of management different crops. Further research should explore the effect of combinations of various cultural intraspecific diversity systems with a more detailed study of the arthropod community present, down to at least genus identification in order to better define biodiversity indicators, especially considering that agricultural biodiversity will be essential to cope with the predicted impacts of climate change, and to detect more resilient farm ecosystems.

**Author Contributions:** Conceptualization, D.V.-A. and C.S.-C., methodology, D.V.-A., C.S.-C., M.L., C.P. and P.D.-S.; formal analysis, D.V.-A. and M.C.-G.; investigation, D.V.-A. and C.S.-C.; writing—original draft preparation, D.V.-A., C.S.-C., M.L., C.P. and P.D.S.; writing—review and editing, D.V.-A. funding acquisition, C.S.-C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Bioversity International Grant contract number: (I-R\_1362).

**Acknowledgments:** We are very grateful to Bioversity International Rome Italy and Technical State University of Quevedo, Ecuador for their financial and scientific support of this work. I sincerely thank the Universitat Autònoma de Barcelona (UAB), Spain for their contribution in this study.

**Conflicts of Interest:** The authors declare no conflict of interest.

## **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Intercropping Winter Lupin and Triticale Increases Weed Suppression and Total Yield**

## **Nicolas Carton, Christophe Naudin, Guillaume Piva and Guénaëlle Corre-Hellou \***

USC LEVA, INRA, Ecole Supérieure d'Agricultures, Univ. Bretagne Loire, SFR 4207 QUASAV 1, 55 rue Rabelais, 49007 Angers CEDEX, France<EMAIL_ADDRESS>(N.C<EMAIL_ADDRESS>(C.N<EMAIL_ADDRESS>(G.P.)

**\*** Correspondence<EMAIL_ADDRESS>
Received: 15 June 2020; Accepted: 21 July 2020; Published: 1 August 2020

**Abstract:** Lupin (*Lupinus* sp.) produces protein-rich grains, but its adoption in cropping systems suffers from both its low competitive ability against weeds and its high yield variability. Compared with legume sole cropping, grain legume–cereal intercropping benefits include better weed suppression and higher yield and yield stability. However, the potential of enhancing crop competitive ability against weeds in additive winter grain legume–cereal intercrops is not well-known, and this potential in long crop cycles is even less studied. We studied how intercropping with a triticale (×*Triticosecale*) alters weed biomass and productivity of winter white lupin (*Lupinus albus* L.). The experimental setup consisted of eleven sites during a two-year period in western France. In each site-year, winter white lupin sole cropping was compared to winter white lupin-triticale intercropping in an additive sowing design. We found that intercropping reduced weed biomass at lupin flowering by an average of 63%. The rapid growth and high soil N acquisition of triticale compensated for the low competitive ability of lupin against weeds until lupin flowering. Competition from triticale in the intercrop reduced lupin grain yield (−34%), but intercropping produced a higher total grain yield (+37%) than did lupin sole cropping while maintaining the total protein grain yield.

**Keywords:** intercropping; lupin; triticale; weeds; legumes; nitrogen

## **1. Introduction**

In Europe, the livestock sector mostly relies on imported soybean cake as protein-rich feed [1]. Local protein-rich crop products are needed to increase self-sufficiency. Among candidate crops, lupins (*Lupinus albus* (white lupin), *L. angustifolius* (narrow-leafed lupin)) produce seeds that have the highest grain protein content (30–42%) among grain legumes, and these seeds can partly substitute for soybean in ruminant, pig and poultry diets [2]. Lupins can also be an alternative to soybean in food diets including more plant proteins [3]. Lupins also fix significant amounts of atmospheric nitrogen (N2) with an average fixation rate of 75% [4]. Like other grain legumes, lupins can provide farming systems with additional services by contributing to crop diversification and reducing synthetic nitrogen (N) fertilizer requirement in crop rotations. However, lupins are not widely cultivated in the European Union (EU) (approximately 120,000 ha in 2014; [5]) because of the high yield variability of this crop [6]. This variability is presumably associated with its high susceptibility to biotic and abiotic stresses. High weed infestation levels are usually reported [7,8]; these infestations are likely due to the slow ground cover and the long cropping season of the crop, especially for winter white lupin.

Compared with sole cropping, intercropping of grain legumes with cereals is a cropping strategy that can increase yield and improve yield stability, especially under low-input conditions [9–11]. This phenomenon generally results from the improved use of abiotic resources (light interception and use of both soil mineral N and atmospheric N2). Intercropping can also reduce insect pests [12], diseases [13] and weeds [14,15].

Farmers often cite weeds as the main challenge in grain legumes. Intercropping a grain legume with a cereal can reduce weed growth. For instance, in spring pea, intercrops with barley suppress more weed biomass than do sole pea crops [15,16].

While pea– and faba bean–cereal intercropping has been the subject of numerous studies, white lupin–cereal intercropping for grain harvest is an innovative practice that has received little attention from academic researchers. Among grain legumes, lupin exhibits both the most variable yield [6] and the least competitive ability against weeds [7,17], meaning that intercropping winter white lupin for grain may have a high potential of development.

The objective of the practice described in this study is to produce protein-rich grains and the originality is that lupin is the main crop and the cereal is a companion crop that is also expected to produce grain ("harvested companion crop"). Intercropping lupin with a cereal could promote lupin cropping if the practice can reliably circumvent the two main shortcomings of lupin sole cropping by increasing competitive ability against weeds and securing grain and protein production.

The combination of intercropped species that exhibit contrasting traits could increase both the use of available resources and the competitive ability of the mixture against weeds. Compared with legume sole cropping, intercropping two species supposedly results in a higher competitive ability, especially at the beginning of the crop cycle, due to the contrasting traits of both species. In lupin–cereal intercrops, we expect both lupin early growth and soil N acquisition to be low and cereal growth and N acquisition to compensate for the low early competitive ability of lupin against weeds. In a multisite study in Europe, cereal competitive ability for soil mineral N was decisive regarding the higher weed suppression in organic spring pea–barley intercrops than in sole-cropped pea [16]. Species interactions can vary over time, especially during long cycle crops. To better understand the ultimate performances of legume–cereal intercrops, the systematic description of the relative dominance of each species before the period of maximum growth and maximum N2 fixation rate of the legume would be useful because the benefits of intercropping for resource use start in the early growth phase [18] and because early dominance can shape the interactions in the second half of the growth cycle.

The aim of the present study was to compare the weed suppression and yield performance between winter white lupin-triticale intercropping and lupin sole cropping. The original aspect of this work is to study the effect of the addition of triticale by analyzing two phases: from sowing to lupin flowering and from lupin flowering to maturity. Moreover, the interest of this study lies in the fact that a range of contrasting growth conditions was used which will help to understand the conditions needed to guarantee the success of this practice. This was achieved by comparing winter white lupin–triticale intercropping and lupin sole cropping throughout a set of eleven experiments during a two-year period in western France.

## **2. Materials and Methods**

## *2.1. Field Sites*

Field experiments were carried out in the 2014/15 and 2015/16 growing seasons in western France for a total of eleven site-years (see details in Table 1). The 20-year average annual rainfall in the area is 718 mm, and average annual air temperature is 12.5 ◦C. The weather patterns of the two study years deviated similarly from the 20-year average. Specifically, the main deviation from the average data involved the October-February air temperatures, which were 8.9 ◦C (2014/15) and 9.6 ◦C (2015/16) averaged over the study sites, whereas the 20-year average was 8.2 ◦C (Figure 1). At each site, two winter white lupin cropping strategies were compared: lupin sole cropping and lupin–winter triticale intercropping.

**Figure 1.** (**a**) Monthly average air temperature, (**b**) monthly rainfall, averaged over the study sites in 2015 (dots) and 2016 (triangles) as well as the 20-year average (temperature: open squares, rainfall: bars) and (**c**) average timing of key events of the crop cycle. Arrows represent the two harvests: at lupin main stem flowering and at lupin maturity.

The eleven sites were seven lupin-triticale intercropped farm fields (minimum 1 ha) on real farms including a wide strip (minimum 10 × 100 m) of sole-cropped lupin and four microplot experiments in a randomized block design with four replicates, including sole-cropped winter white lupin and lupin-triticale intercrop (individual plots ranging from 3 × 10 m to 6 × 17 m). The interest of these two sources of data was to involve different actors (farmers, advisers, researchers) and investigate the effect of intercropping in a wide range of situations. All fields were managed with conventional farming practices. Decisions of agricultural practices including cultivars; sowing date; preceding crop; row width; intercrop spatial arrangement; and pest, disease and weed control were made by both farmers and experiment managers and varied among sites (Table 1). However, the sole-cropped lupin and intercrop were managed identically, with the exception of the site F, in which the intercrop seedbed preparation included less soil tillage than did the lupin sole crop and 50 kg N·ha−1 was added only to the intercrop in April. The other sites were managed without N fertilization both in sole and intercrops. Lupin row spacing ranged between 12 and 75 cm (average: 34 cm). In all intercrops, the two species were sown on the same day or within one day. The lupin cultivars were chosen from the dwarf determinate branched cultivars that are typically cultivated in western France. Triticale cultivars were chosen for their late maturity, which is desirable for the simultaneous maturity of both species in the intercrop. Tested lupin cultivars do not show major differences except for the greater plant height of cv. Magnus. Tested triticale cultivars do not show major differences; all have a medium plant height. The lupin seeds were inoculated with *Bradyrhizobium lupini* in accordance with commonly recommended practices. The sole-cropped lupin was sown at an average of 27 kernels·m−2 (Table 1; SD = 2.4 kernels·m−2). Lupin in the lupin-triticale intercrop was sown at the same density as lupin in the sole crop; the average triticale sowing density was 73 kernels·m−2 (SD = 6.1 m−2), corresponding to

an additive design in which lupin (L) was sown at the recommended density, and triticale (T) was sown at 30% of the sole crop recommended sowing density (L100:T30). All sites were rain fed and received no supplemental irrigation. All sites except H received a chemical control with only one application before emergence for sites A, B, C, D, F, G, I, J and with two applications, before emergence and during the winter for sites E and K. One site (A) also received a mechanical weeding in spring. These chemical or mechanical operations were similar in intercrops and sole crops.

## *2.2. Measurements, Sampling and Analysis*

At all sites, the aboveground parts of lupin, triticale and weeds were hand-harvested twice throughout the crop cycle: at lupin main-stem flowering (April) and at lupin maturity (July until beginning of August). In the seven real farms, six plots (20 × 30 m) were defined randomly: three in the intercropped strips and three in the sole-cropped lupin strips. In each plot, the plants were harvested in three randomly defined subplots that covered 1 m × 2 lupin or lupin + triticale rows, and the values were averaged across subplots. In the four randomized block design experiments, plot size ranged between 1.7 × 10 m to 4.5 × 20 m and the plants were harvested in each plot in a randomly defined subplot (minimum area: 0.3 m2; maximum area: 1.05 m2). In the same plots later used for biomass sampling, all weeds were identified. The aboveground dry matter (DM) was determined after oven drying at 70 ◦C for 48 h until constant weight. At harvest, the grain and straw were threshed and then weighed. For N content measurements on aboveground biomass of lupin and triticale at lupin flowering, and on lupin and triticale grain and straw at maturity and aboveground weed biomass at flowering and maturity, the samples were pooled each across blocks and ground (120-mm mesh netting; "Pulverisette 19" universal cutting mill, "Laborette 27" sampler, and "Pulverisette 14" variable speed rotor mill; Fritsch, Idaroberstein, Germany). The total N concentration and 15N:14N ratio measurements were performed using a CHN analyser (EA3000; Euro Vector, Milan, Italy) and a mass spectrometer (IsoPrime; Elementar, Hanau, Germany). The mineral soil N content of representative soil samples from a 0–90-cm depth at sowing was measured via segmented flow analysis (Skalar Analytical B.V., Breda, Netherlands), which enables the determination of nitrate and ammonium contents by extraction with KCl [19]. At eight sites (A, C, E, F, G, H, I, J), crop plant density after emergence and lupin density after winter were recorded, and he mineral soil N content was measured after winter (0–90 cm). Protein content was determined by N content multiplied by 6.25.

## *2.3. Calculations*

Weed reduction (WR) was assessed to characterize the ability of the intercrop to suppress weeds compared to the lupin sole crop. The index was determined according to the following equation:

$$\text{WR} = 100 \times \text{((weed DM in the liquid scale crop --week DM in the intercept }) \newline \qquad \text{(weed DM in the liquid scale crop))} \tag{1}$$

The percentage of accumulated N derived from the air (%Ndfa) in lupin was determined on the two sampling dates using the 15N natural abundance method [20]. Triticale served as the non-fixing reference in the calculation. The following equation was used:

$$\text{\textbullet\textquotedblleftNdfa} = 100 \times \left( \text{\textquotedblleft N}\_{\text{legume}} - \delta^{15} \text{N}\_{\text{reference}} \right) - \left( \beta \text{fix} - \delta^{15} \text{N}\_{\text{reference}} \right) \tag{2}$$

where δ15Nlegume and δ15Nreference are the natural 15N enrichment values of the legume and triticale, respectively. The β-values for lupin ("βfix") were derived from the minimum values attained by δ15N at all the sites: −0.88 at flowering (site H) and −1.03 at maturity (site D).

The normality and homoscedasticity of model residuals were tested using Shapiro s and Levene s tests, respectively (α = 0.05). For the across-sites statistics, per site means of the data were used and the differences between sole-cropped lupin and the intercrop were assessed by Student's paired *T*-tests (α = 0.05) except when non-normality was detected for model residuals. In those cases, Wilcoxon's signed-rank test (α = 0.05) was used. Linear regressions using model II (Reduced Major Axis) were computed to assess relationships between variables. The absence of outliers in the data was assessed with Grubbs' test of model residuals [21]. For the per site statistics, per block data were used and the differences between sole-cropped lupin and the intercrop on each site were assessed by Student s *T*-tests (α = 0.05), using the pooled variance estimate calculated using all sites. The Benjamini and Hochberg method was used to control the false discovery rate, i.e., the expected proportion of false discoveries amongst the rejected hypotheses [22]. Individual per site *T*-tests were used for lupin grain yield and crop total grain yield, where the global model was not applicable because of variance heterogeneity. All statistical analyses were performed using R software [23] version 3.3.2.

## **3. Results**

## *3.1. Weed Suppression*

The treatments were compared under various situations of weed infestation and growing conditions. In the lupin sole crop, at maturity, weed biomass ranged from 0 g·m−2 (site K) to 567 g·m−2 (site G; Table 2). The sites differed also in weed communities (Table 3).

Crop biomass of the different treatments at maturity also varied widely across sites, from 109 g·m−2 to 1238 g·m−2 in the lupin sole crop and from 416 g·m−2 to 1850 g·m−2 in the lupin-triticale intercrop. The variability of crop and weed growth was higher among sites than between the two years; therefore, the year effect was not isolated in the analyses.

The weed biomass at lupin flowering was lower in the intercrop (on average 38 g·m−2; Table 4) than in the lupin sole crop (average of 100 g·m−2).

The weed reduction (WR) reached an average of 63%. The difference in weed biomass between the lupin sole crop and the intercrop was higher with higher levels of weed biomass (Figure 2a).

**Figure 2.** (**a**) Weed biomass in the intercrop against weed biomass in the lupin sole crop at lupin flowering and (**b**) at lupin maturity. Letters next to points identify study sites. The dashed lines represent the theoretical situations with equal weed biomass in both cropping strategies.

The significance comparisons lupin using pooled using ("global *t*-test") per site *t*-tests where the global model was not applicable. n.s.: not significant, n.a.: not applicable; 1 on site F, lupin biomass at flowering was significantly higher in the intercrop than in the sole crop. \*\*\*, \*\*, indicate significant differences among species at *p* < 0.001, *p* < 0.05 respectively.

**Table 3.** List of weed species (species present with more than one plant per m2 in at least one third of the subplots at lupin flowering) in lupin sole crops (SC) and intercrops (IC).

**Table 4.** Crop and weed biomass production and soil N acquisition at the 11 sites in lupin sole crops (SCs) and lupin–triticale intercrops (ICs) during two periods, from sowing to lupin flowering, and from lupin flowering to maturity.

The significance levels of comparisons were assessed with *T*-tests except where *V*, the test statistic of Wilcoxon s signed-rank test, is given. n.d.: not determined; n.s.: not significant. All values are the means (SDs) of plant aboveground dry matter and soil N uptake, *n* = 11. \*\*\*, \*\*, indicate significant differences among species at *p* < 0.01, *p* < 0.05 respectively.

A significant effect of intercrop on weed biomass was observed on four sites (B, F, G, H) (Table 2). These sites had a high weed biomass (higher than 110 g·m2).

The weed biomass at maturity was also significantly lower in the intercrop (on average 70 g·m−2) than in the lupin sole crop (on average 166 g·m−2). However, the WR was lower (average of 43%) at crop maturity than at flowering. Weed reduction in the intercrop compared to sole-cropped lupin occurred mainly from sowing until lupin flowering, and to a lesser extent from lupin flowering until crop maturity. The variability across sites of WR at maturity (coefficient of variation (CV) of 151%) was higher than WR at flowering. (CV of 30%). The average WR at maturity was only 15% for the six sites that had the lowest weed biomass in the sole crop and weed reduction was not statistically significant for these sites. However, the WR was highly significant at maturity and reached 60% for the five sites on which weed biomass in the sole crop surpassed 200 g·m−2 (Table 2).

Triticale produced more biomass than did lupin in the intercrop from sowing until lupin flowering (Table 4). The addition of triticale systematically significantly increased total crop biomass at lupin flowering except on site I (average of +387 g·m−2, i.e., +157% DM, t10 = 6.3, *p* = 8 E−5, minimum: +32% (site I), maximum: +549% (site G), Figure 3. At lupin flowering, the WR was linearly correlated with the crop total biomass gain allowed by the integration of triticale (Figure 4).

Intercropping allowed a median crop biomass of 551 g·m−2, and the weed biomass was maintained at less than 50 g·m−2 on more than 60% of the sites, even the sites with the lowest crop biomass (Figure 5). At half of the sites, the biomass of sole-cropped lupin at flowering was less than 243 g·m−2. At this level of lupin biomass, weed growth was below 50 g·m−2 at a probability of less than 10% (Figure 5). At sites with a higher lupin biomass (higher than 243 g·m−2), weed biomass at lupin flowering could be maintained under 50 g·m−2 for 65% of those sites.

**Figure 3.** (**a**) Crop biomass in the intercrop against lupin biomass in the lupin sole crop at lupin flowering. (**b**) Crop N content from the soil in the intercrop against lupin N content from the soil in the lupin sole crop at lupin flowering. Letters next to points identify study sites. The dashed lines represent the theoretical situations with equal values in both cropping strategies.

**Figure 4.** Correlation between weed biomass reduction in the intercrop compared to the lupin sole crop (WR) (%) and crop biomass gain allowed by addition of triticale at lupin flowering. Letters next to points identify study sites. \*\*\* indicate that the correlation is significant at *p* < 0.001.

From lupin flowering to maturity, the intercrop and the lupin sole crop produced similar amounts of DM. In the intercrop, the lupin biomass increased at a higher rate than did triticale biomass (+189% vs. +40%, respectively, Table 4). The variability of WR at maturity across sites was not explained by increases in crop or lupin biomass (no correlation, *p* = 0.3 and *p* = 0.4, respectively). However, the weed growth after lupin flowering was maintained at less than 60 g·m−2 when the crop biomass at lupin flowering attained the threshold value of 400 g·m−2 (Figure 6). This crop biomass value was attained in the intercrop at nine of eleven sites and in the lupin sole crop at two of eleven sites. The weed growth between lupin flowering and maturity was negative at some sites because of low weed growth and the decomposition of weed biomass in the end of the crop cycle.

**Figure 5.** Distribution of weed biomass at lupin flowering across the 11 sites for different crop biomasses in the intercrop and in the lupin sole crop. Lupin sole crop and intercrop were separated in two pools according to median crop biomass so that for each cropping strategy, the sites with lowest crop biomass are represented with circles and the sites with highest crop biomass are represented with squares. Median crop biomass in the lupin sole crop: 243 g·m−2, median crop biomass in the intercrop: 550 g·m−2.

**Figure 6.** Weed growth after flowering against crop biomass at lupin flowering. Open symbols refer to lupin sole crops and full symbols refer to intercrops. Letters next to points identify study sites.

The mean proportion of triticale in the intercrop biomass was 75% at lupin flowering (CV: 14%) This proportion decreased to 59% at maturity and with a higher variability (CV = 28%). The addition of triticale reduced the proportion of weeds in total plant biomass. At flowering, the weed biomass represented 33% of the canopy in pure lupin and 7% in the intercrop. At maturity, the weed biomass represented 24% of the canopy in pure lupin and 8% in the intercrop.

The addition of triticale reduced weed biomass but also the diversity of weed species compared to lupin sole crop (Figure 7a). However, the proportion of mono/dicotyledonous species in the total number of weed species was not greatly modified except in site C (Figure 7b).

**Figure 7.** (**a**) Number of weed species in intercrops against number of weed species in lupin sole crops. (**b**) The percentage of dicotyledonous species in the total number of weed species in intercrops against the percentage in lupin sole crop. The dashed lines represent the theoretical situations with equal values in both cropping strategies.

## *3.2. Soil N Acquisition by Crops and Weeds before and after Flowering*

Soil N acquisition was very low in lupin sole crop at the beginning of the crop cycle. It reached only 2.5 g·m−2 at the beginning of lupin flowering. Crop mineral soil N acquisition until lupin flowering was enhanced by the addition of triticale: the average crop mineral soil N acquisition gain was +4.5 g·m−2, i.e., +181%, Table 4). In the intercrop, triticale acquired 88% of the crop mineral soil N acquired by the mixture until flowering. Triticale biomass was on average 3 times higher than lupin biomass and triticale soil mineral N acquisition was on average 8 times higher than lupin soil mineral N acquisition until flowering. Lupin acquired on average 5 mg of soil N per g of crop biomass produced, whereas triticale acquired 13 mg of soil N per g of crop biomass.

Weeds had a high ability to acquire soil mineral N: they acquired on average 19 mg of soil N per g of weed biomass at the beginning of lupin flowering. However, weed mineral soil N acquisition was reduced in the intercrop (−63% on average, Table 4) in comparison with weeds observed in the lupin sole crop. The lupin %Ndfa at the beginning of lupin flowering was significantly higher in the intercrop (84%, SD = 12) than in the lupin sole crop (66%, SD = 14, *t10* = 3.0, *p* = 0.01; Table 5). The intercrop acquired more mineral soil N than did the lupin sole crop despite lupin depending less on mineral soil N in the intercrop.

At the end of winter, the integration of triticale had a tendency to reduce the mineral soil N content (53 kg·ha−1 in the sole crop and 43 kg·ha−1 in the intercrop, *t7* = −2.2, *p* = 0.06; Table 5), showing that an effect on the available N occurred rather early in the cropping season.

Total crop mineral soil N acquisition until lupin flowering varied less in the intercrop than in the lupin sole crop (Table 4). The CV for sole-cropped lupin was 64%, whereas the CV for the intercrop was 45%.

The significance levels of comparisons were assessed with *T*-tests. n.d.: not determined; n.s.: not significant. \*\*, \*, indicate significant differences among species at *p* < 0.05, *p* < 0.01, respectively.

In the lupin sole crop, only 27% of soil N was acquired before flowering, whereas in the intercrop, 67% of soil N was acquired before flowering. Thus, from lupin flowering until maturity, the lupin sole crop acquired more mineral soil N than did the intercrop (Table 4). As observed at lupin flowering, the lupin %Ndfa at maturity was significantly higher in the intercrop (73%, SD = 19) than in the lupin sole crop (62%, SD = 17, *t10* = 3.4, *p* = 6 E−3; Table 5).

## *3.3. Grain and Productivity Yield of the Lupin Sole and Intercrop*

In the lupin sole crop, the mean grain yield was 296 g·m−2 (minimum: 33 (site A), maximum: 663 (site K), Table 2), and the mean protein yield was 104 g·m−2. The mean lupin yield was 34% lower in the intercrop than in the sole crop, but the effect of the addition of triticale on lupin yield differed among sites (Figure 8). Lupin yield was significantly lower in the intercrop than in the sole crop on four sites (Sites E, F, J and K) (Table 2). At sites A and B, two sites with low-to-medium lupin yields, the lupin grain yield tended to be higher in the intercrop than in the lupin sole crop. The lupin protein concentration did not significantly differ between the lupin sole crop and the intercrop (35.7% and 35.2%, respectively, with a CV of 17% in the sole crop and 15% in the intercrop; data not shown).

**Figure 8.** (**a**) Lupin grain yield in the intercrop against lupin grain yield in the lupin sole crop. (**b**) Total grain yield in the intercrop against lupin grain yield in the lupin sole crop. (**c**) Total protein yield in the intercrop against protein grain yield in the lupin sole crop. Letters next to points identify study sites. The dashed lines represent the theoretical situations with equal values in both cropping strategies.

Triticale produced on average 201 g·m−2 grain (minimum: 28 (site G), maximum: 485 (site E), Table 2), i.e., a similar yield as that of lupin in the intercrop (195 g·m−2 on average, *t10* = 0.12, *p* = 0.9). Triticale grain had a mean protein concentration of 10.0% (data not shown). When considering total grain production, the intercrop produced more grain than did the lupin sole crop on average over all sites (+37%, mean: 395 g·m−2, minimum: 68 (site G), maximum: 628 g·m−2 (site F)). At site A and site H, the lupin yield did not differ between the intercrop and the sole crop, but the total intercrop yield was significantly higher than the lupin sole crop yield (Table 2). At site A, the lupin grain yield was less than 100 g·m−2 in the sole crop and the addition of triticale allowed a total production of 248 g·m−2 grain (Table 2). At the four sites where the lupin yield was significantly lower in the intercrop than in the lupin sole crop, the intercrop total yield did not differ from the lupin sole crop yield (Table 2). The triticale proportion in intercrop grain biomass ranged from 27% to 80% (mean 49.5%).

On average, the total protein yield of the intercrop did not significantly differ from that of the lupin sole crop, but differences across sites were recorded (Figure 8). The reduction in protein production due to intercropping was highest at the site with the highest lupin yield in the sole crop (site K, −103 g·m−2 protein), as triticale protein production could not compensate for the reduction of protein-rich lupin grain production. However, on site B, the intercrop produced 43 g·m−2 more protein than did the lupin sole crop (Figure 8). In the intercrop, lupin represented on average 49% of grain yield and 78% of protein yield. The total grain and protein production values varied less across the eleven site-years in the intercrop than in the lupin sole crop (Table 2).

## **4. Discussion**

Our results showed that the addition of triticale has a great ability to reduce weed biomass in lupin crops, especially when weed pressure is high, while maintaining protein yield. Weed biomass reduction can be explained by the increased crop biomass and mineral soil N acquisition especially at the beginning of the crop cycle through the addition of triticale. Moreover, crop biomass, mineral soil N acquisition, grain yield and protein yield were more stable in intercrops.

## *4.1. Weed Reduction Allowed by the Addition of Triticale and Underlying Processes*

Weed reduction values at lupin flowering were consistently greater than 37% across a wide range of practices, pedo-climatic conditions and weed growth potentials (Figure 2, Table 2), demonstrating that the addition of triticale at 30% of its recommended density in the sole crop is effective at reducing weed growth. This finding is in line with previous studies on other intercropping systems. Focusing on intercrops that have a short growing season, Corre-Hellou et al. [16] studied spring pea–barley additive intercrops in which barley was sown at 150 kernels·m−2 (100:50) in five countries in Europe and obtained a mean WR of 55% at the beginning of pea flowering. Using an additive intercrop design consisting of spring pea and oat (60 kernels·m−2, 100:20), Gronle et al. [24] reported WR values of 14% and 27% at the beginning and end of pea flowering, respectively.

Weed growth in winter white lupin seems to utilize an available ecological niche related to the low growth rate of lupin until flowering. The addition of triticale in lupin occupied this niche; this occupation strongly and systematically increased crop weed suppression before flowering. Intercropping allowed attainment of crop biomass levels that ensure high weed control; this high crop biomass was rarely observed in lupin sole crops and is consistent with the finding that triticale can particularly compensate for the low competitive ability of a legume crop that produces low levels of crop biomass [24]. The less competitive a legume sole crop is, the more the addition of a cereal facilitates weed suppression.

In the lupin sole crop, mineral soil N not used by lupin was taken up by weeds, whereas in the intercrop, triticale acquired a large amount of mineral soil N to the detriment of weeds (Table 4). Cereals have a higher soil N requirement than legumes, and this demand is often associated with rapid root growth and a dense root system [9,25–28]. Integrating a cereal into a legume crop can result in the use of mineral soil N to produce cereal grain instead of weed biomass [15]. Lupin acquired much less soil mineral N in the intercrop than in the lupin sole crop due to the combined effects of higher %Ndfa and lower biomass, mitigating the effects of triticale soil mineral N acquisition. Hauggaard-Nielsen et al. [29] also reported that grain legumes accumulated less soil N when intercropped than was expected from sole crop acquisition.

Despite not being measured, competition for light is also assumed to be an important mechanism in the higher competitive ability of the intercrop than sole crop, as observed in other intercropping systems [30] due to complementary traits for leaf area distribution in the canopy and an increase in spatial homogeneity [31,32]. The spatial homogeneity may depend on sowing patterns of intercrops. Sowing the triticale in alternating rows in an additive design may decrease early heterogeneity of crop ground cover by filling the wide inter-row space in the lupin sole crop [33]. In our study, in the five sites (sites D, E, F, H, K) with highest WR (higher than 70%) at lupin flowering, intercrops were sown with triticale and lupin on alternating rows (Tables 1 and 2).

Lupin and triticale differ in their growth dynamics, and two contrasting periods were studied in the long crop cycle; the limit was the time of lupin flowering. Until lupin flowering, lupin had a low biomass production, which favored weeds in the lupin sole crop because the weeds could develop virtually without crop competition during autumn and winter and could gain an initial advantage over lupin during the first growing period (Tables 2 and 4). Triticale had a high biomass production during the initial growth stages (Tables 2 and 4). As reported in spring barley and pea [26,34], the maximum growth rate of the cereal occurred before that of the grain legume. Beginning at lupin flowering, the lupin growth rate strongly increased, whereas the triticale growth rate strongly decreased (Table 4). The clearly offset period of maximum growth rate and opposite growth patterns between lupin and triticale may allow temporal complementarity of resource use. In our study, compared with winter legume sole cropping, intercropping with triticale reduced weed growth before flowering but not after flowering (Table 4).

Complementarity of resource use in time and space in intercropping may be not the unique mechanism explaining weed suppression. Allelopathy effects can also contribute to weed suppression [35], but this mechanism was not investigated in our study and would require specific experiments. A better understanding on the mechanisms behind weed control and other benefits of intercropping systems can guide the design of various species combinations with traits that maximize positive or minimize negative interactions and reach expected services [36,37].

Weed species complexes encountered in the field network may have interacted with the effects of triticale. Nevertheless, the effect of intercropping seems effective for a wide range of weed compositions. Some weed species taller than triticale might limit the intercropping effect, but such situations were rare (only observed punctually during the crop cycle with *Erodium cicutarium* (site B), *Triplospermum inodorum* (site G), *Dactylis glomerata* (site G), and *Poa trivialis* (site J)). Our results indicated a reduction of the number of weed species in intercropping but without a modification of the repartition of the

species in monocotyledonous and dicotyledonous classes. Nevertheless, these results need to be confirmed with a wider range of situations and with additional data (plant density and biomass per weed species) to investigate more in depth the effect of intercropping on the structure of the weed community in relation to weed and crop functional traits.

## *4.2. Productivity of Intercropping Compared to Lupin Sole Cropping*

This study confirmed the high potential of lupin to yield large amounts of grain and protein both in sole cropping and in intercropping systems. Although triticale reduced the lupin growth and yield, triticale grain production increased average total grain production by 37%. The mean lupin yield reduction by triticale (34%) was lower than that obtained by Hauggaard-Nielsen et al. [29] using a 50:50 substitutive design with narrow-leafed lupin and spring barley during a three-year period, during which intercropping reduced the lupin grain yield by 62%. It is likely that the lupin 100:triticale 30 additive design used in our study better maintains lupin yield than does a balanced substitutive design and still allows satisfactory cereal production. In our study, lupin compensated for the reduced soil N availability and fulfilled its N requirements by increasing the proportion of N derived from fixation; this mechanism is in agreement with the results of numerous studies [9,10,26,38,39]. However, the lupin biomass decreased, hence the reduced total N amount in lupin. The sites H and A showed promising results: despite triticale proportions in the intercrop biomass being higher than 80%, lupin yield was little decreased at site H (−16%) and even increased at site A (+60%). The reasons for these results are not clear, but they may have been in part favored by the sowing design, in which lupin and triticale were not sown on the same rows [40,41]. However, site A showed low lupin yields in both the sole crop and intercrop. A minimum of lupin plant density is important to reach satisfactory lupin yield and weed control levels: on the site with the lowest lupin density after winter (14 plants·m−2 in the lupin sole crop and in the intercrop, site G), lupin biomass at flowering and lupin grain yield in the intercrop were the lowest of all sites and weed biomass at maturity were by far the highest of all sites in the sole crop and in the intercrop.

Willey [42] reported that the growth of species sown in intercrops at the same density as that of sole crops is always less than the growth the sole crop. This phenomenon shows that full complementarity between intercropped species cannot be achieved. Lupin-triticale intercropping is a system in which the crop producing favored yield lets the companion crop have an initial advantage. This phenomenon is not ideal but seems inevitable when the main species exhibits slow early growth, as observed in grain legumes. In our study, this effect has possibly been further enhanced by the particularly mild autumn and winter during both study years. A lower temperature during crop establishment and early growth would probably have mitigated the cereal growth and dominance in the intercrop because it would have delayed the beginning of cereal maximal growth phase and reduced tillering, whereas lupin maximal growth phase and branching takes place later and would not have been affected.

Grain yield variability across a wide range of situations was lower when considering total intercrop yield rather than lupin sole crop yield. This result is consistent with previous results on both spring intercrops [9] and winter intercrops [43] but contrasts with those of Hauggaard-Nielsen et al. [29], who reported no yield stability differences between narrow-leafed (spring) lupin-barley intercrops and narrow-leafed lupin sole crops. The higher yield stability measured in this study needs to be assessed in long-term studies. The level of yield variability remained high in the intercrop; however, here, we mostly characterized between-site variability, whereas farmers may be more interested in ways to increase inter-annual stability. If for lupin, intercropping proves to be an efficient way to secure yield, this could be a convincing argument for some farmers who could decide to replace lupin sole crop with lupin–cereal intercrop or start to grow lupin using the intercropping strategy.

#### *4.3. Perspectives for the Use of Lupin-Based Intercrops*

Effective weed control combined with the maintenance of lupin yield in the intercrop occurred for instance on site F where the combination of increased total crop biomass and a high proportion of lupin in the crop biomass occurred. In situations where sole-cropped lupin can perform well (limited biotic and abiotic stresses under a favorable climate, the absence of water logging and the use of pesticides and herbicides, e.g., site K in our study), adding a cereal will very likely reduce protein yields. However, intercropping has a high potential for lupin growth in suboptimal conditions that are becoming increasingly frequent and unpredictable due to climate change. Intercropping should also be promoted as part of an integrated agronomic strategy in combination with other agronomic measures such as crop rotation, cover cropping and mechanical weeding to limit or forfeit the use of herbicides. Adding cereals in grain legume crops seems to allow maintaining protein productivity while keeping weed biomass within acceptable thresholds without or with a low use of herbicides. This additive intercropping design should therefore be promoted as a strategy to facilitate production of grain legumes following the need to reduce the use of herbicides, in the same way as other (mostly substitutive) intercropping designs have been promoted as strategies to increase total grain production and cereal protein concentration in low-input systems [44,45].

Although the triticale cultivars and density levels used in this study showed good performances, further adjustment of cereal species or cultivar choice or density fine-tuning is needed, as competition from the companion crop on lupin must be reduced. We hypothesize that on two sites (F and H), the alternating row design played a role in allowing the competition of triticale against lupin to be lower than that at other sites that had the same level of triticale proportion in the intercrop biomass. Specific experiments are needed to compare different spatial arrangements. Triticale cultivars or other cereal species with a shorter height after stem elongation may be favorable to maintain lupin yield. Selecting lupin cultivars for traits best adapted to intercropping with cereals could further increase the benefits of this cropping strategy [46].

Most farmers and experimenters managing experimental fields had no previous experience in lupin intercropping, suggesting that large room for optimization of field choice and management practices exists and that higher performances of the intercrop can be expected. In our field network, sole-cropped lupin management was not optimal since practices adapted to the intercrop were applied to both cropping strategies. Specifically, in lupin sole crops, a post-emergence herbicide treatment was typically applied in conventional fields at the time of the study and it has not been used here in eight of the eleven sites. The potential of sole-cropped lupin may have been underestimated in this study.

## **5. Conclusions**

Comparing the intercrop and the sole crop in the context of the transition to low-input crop management strategies is increasingly needed as solutions for chemical weeding are becoming scarce. In this context, we showed that the lupin-triticale intercrop is a relevant option. Because a moderate lupin yield reduction can lead to a high protein yield loss, intercropping lupin with triticale does not seem to potentially perform better than sole cropping lupin regarding protein productivity on an area basis. At a broader scale, intercropping could allow an increase in lupin cropping area via increased lupin adoption by farmers due to increased weed suppression and secured total productivity. In this case, lupin intercropped with cereals could significantly contribute to the production of protein-rich grains in Europe.

**Author Contributions:** Conceptualization, N.C., G.P., C.N. and G.C.-H.; methodology, N.C., G.P., C.N. and G.C.-H.; validation, N.C., G.P., C.N. and G.C.-H.; formal analysis, N.C.; investigation, N.C.; writing—original draft preparation, N.C., G.P., C.N. and G.C.-H.; writing—review and editing, N.C., G.P., C.N. and G.C.-H.; visualization, N.C.; supervision, G.C.-H.; project administration, G.P. and G.C.-H.; funding acquisition, G.P. and G.C.-H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by EAFRD and Regional Council (Brittany) within the project PROGRAILIVE carried out by the association Pôle Agronomique de l'Ouest, and by the French National Research Agency (ANR) within the project LEGITIMES, grant number ANR-13-AGRO-0004. The APC was funded by Ecole Supérieure d'Agricultures, Angers.

**Acknowledgments:** We gratefully acknowledge all farmers and experimenters involved and the staff members at LEVA (Légumineuses, Ecophysiologie Végétale, Agroécologie) and FNAMS (Fédération Nationale des Agriculteurs Multiplicateurs de Semences) for their excellent technical assistance. We are most grateful to François Boissinot, Céline Bourlet and Martine Mauline for coordination of the study sites and to Erik Steen Jensen for valuable comments on an earlier version of the manuscript. We thank PLATIN' (Plateau d'Isotopie de Normandie) core facility for all element and isotope analysis used in this study.

**Conflicts of Interest:** The authors declare no conflict of interest.

## **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Approach for Image-Based Semantic Segmentation of Canopy Cover in Pea–Oat Intercropping**

**Sebastian Munz 1,\* and David Reiser 2,\***

Received: 2 July 2020; Accepted: 11 August 2020; Published: 13 August 2020

**Abstract:** Intercropping systems of cereals and legumes have the potential to produce high yields in a more sustainable way compared to sole cropping systems. Their agronomic optimization remains a challenging task given the numerous management options and the complexity of interactions between the crops. Efficient methods for analyzing the influence of different management options are needed. The canopy cover of each crop in the intercropping system is a good determinant for light competition, thus influencing crop growth and weed suppression. Therefore, this study evaluated the feasibility to estimate canopy cover within an intercropping system of pea and oat based on semantic segmentation using a convolutional neural network. The network was trained with images from three datasets during early growth stages comprising canopy covers between 4% and 52%. Only images of sole crops were used for training and then applied to images of the intercropping system. The results showed that the networks trained on a single growth stage performed best for their corresponding dataset. Combining the data from all three growth stages increased the robustness of the overall detection, but decreased the accuracy of some of the single dataset result. The accuracy of the estimated canopy cover of intercropped species was similar to sole crops and satisfying to analyze light competition. Further research is needed to address different growth stages of plants to decrease the effort for retraining the networks.

**Keywords:** convolutional neural network; light competition; transfer learning; growth stages; mixed cropping

## **1. Introduction**

Intercropping systems comprise two or more crop species grown on the same field with overlapping growth periods [1]. It is widely practiced in developing countries under resource-limited conditions and recently gained more interest in European countries as well, especially in organic agriculture [2,3]. In particular, intercropping of cereals and legumes is very common and provides various advantages mainly due to the complementary use of nitrogen [4,5]. Compared to growing a sole crop, intercropping can increase resource use efficiency (e.g., nitrogen and water), total productivity, yield stability, and protein concentration in the cereal grain [6]. In addition, external inputs can be decreased such as synthetic fertilizers and herbicides due strong weed suppression by the cereal [2,6].

In addition to these numerous advantages, the agronomic optimization remains a challenging task given the large number of possible crop and cultivar combinations, spatial and temporal arrangement, and management [7,8]. The pathway to the implementation of intercropping into agricultural systems follows a combination of academic research and on-farm experimentation [3]. To cope with the complexity of intercropping systems and their adoption by farmers, efficient methods are needed that enable (i) to analyze the interactions between crop species efficiently (large area in short time) and (ii) to allow an easy implementation without the need of sophisticated equipment.

The interaction between crop species for light capture (light competition) has a large impact on both crop growth and weed suppression, especially during early growth stages. The canopy cover, i.e., the proportion of soil that is covered by the plant, is a good determinant for light interception and hence in intercropping systems for light competition.

For quantifying the canopy cover, different methods were used, e.g., visual estimation, light measurements above and below the canopy, or image analysis [9]. Visual estimation is time consuming and prone to subjectivity. Light measurements do not allow the differentiation between plant species. Whereas, image analysis with semantic segmentation could provide a precise estimation of canopy cover while being objective, able to differentiate between plant species, and efficient.

However, plant species in intercropping systems can overlap even early after emergence, which complicates this task. This is similar for close to crop weed detection, which is mandatory for autonomous site-specific weeding with robots or automated implements. However, for weeding applications, the position of the plants is sufficient. For applications like harvesting, phenotyping, plant health evaluation, and plant monitoring, semantic segmentation of image data is mandatory, like it is for canopy cover detection in intercropping systems [10].

Typical methods for the image-based canopy cover estimation are index-based [11], feature-based [12], and learning-based methods [13]. Several different learning-based methods where evaluated in the agricultural context for image identification, like random forest classifiers, support vector machines, or convolutional neural network (CNN) structures [10,14,15]. During the last years, especially CNN structures have gained great popularity and have been used for a wide range of applications in research. Most published research focused on weed and crop detection [13]. Moreover, research was conducted to segment fruits, flowers, pests, and plant diseases using CNNs [16–18]. For gaining additional information about the crop and environment, some research focused on segmentation of three-dimensional point clouds. Especially for crop detection and phenotyping, this additional dimension could supply more accurate results of plant position and shape [19].

The image analysis of intercropping systems with CNNs was hardly investigated. The only research conducted, to the best knowledge of the authors, is by Mortensen et al. using a modified version of VGG16 deep neural network to separate oil radish, barley, and weed. They reached pixel accuracies of 79% and an intersection over union (IoU) of 66% [20]. For image-based semantic segmentation of intercropping of cereals and legumes, no research is known to the authors.

Related research focusing on crop-weed detection reached different accuracies for semantic plant detection dependent on the application and setup. For instance, Lottes et al. reached a mean average precision (mAP) between 40.1% and 69.7% for segmenting sugar beet and weed pixels in artificially illuminated images of three different datasets [21]. Dutta et al. reached an mAP of 77.6% using pre-trained CNNs in close range images for weed classification [22]. Challenges arise for all networks when transferring them to other environments or when the growth stage of a plant (i.e., size and structure) changes, implying a high effort for training individual networks for each situation or retraining an existing network with new data according to the changes before good results can be expected [23]. Transfer learning between fruit flowers (apple, peach, and pear) worked quite well [17]; however, the transfer between different plant species and growth stages resulted in a large decrease in accuracy [21,24].

The mentioned related research used data augmentation to extend the input data and to limit the number of labeled images. Mostly geometric data augmentation (e.g., flipping, mirroring, and cropping) was applied, but also the additional integration of index-based features and edge detection was used [15]. It was shown by Taylor and Nitschke that data augmentation can result in an increase of accuracy of over 14% [25]. However, in the agricultural domain, we deal with unstructured objects in unstructured environments increasing the variability for CNNs to an infinite number of possibilities [26]. Therefore, data augmentation seems to be a good start to achieve a better transferability of trained networks [27].

This study presents the first step—by evaluating the feasibility of using image-based semantic segmentation for estimating the canopy cover in intercropping systems—towards an efficient, field-applicable method with the aim to optimize the agronomic management of intercropping systems. The evaluation focuses on the transferability of networks (i) trained with just one single growth stage of a crop to analyze different growth stages, and (ii) trained with images of single crops to differentiate between these crops grown in an intercropping system. For this study, we selected two important crops in intercropping systems in Europe—pea and oat. The images were taken under normal outdoor conditions assuring an easy field application.

## **2. Materials and Methods**

## *2.1. Field Experiment and Image Acquisition*

For this study, data acquisition was conducted within a field experiment on pea–oat intercropping conducted at the experimental station 'Ihinger Hof' of the University of Hohenheim (48◦44 N, 8◦55 E, 478 m above sea level) in Southwest Germany in 2019. The field size was 80 m × 36 m and had a slope of around 10 m height difference. On 28 March, pea cv. Respect and oat cv. Troll (both from IG Pflanzenzucht, Ismaning, Germany) were sown in strips of 2 m width along 80 m of length. The crops were sown both as single and mixed crops with six replicates (Figure 1). Row distance was 12 cm. The sowing density (seeds m−2) was 80 and 60 for pea and 320 and 160 for oat, in single and mixed crops, respectively.

**Figure 1.** Overview of the field experiment. The first three strips from right to left are oat, pea–oat intercrop, and pea.

Images were taken on three dates—25 April, 2 May, and 16 May—to capture the temporal dynamics in canopy cover during early growth stages. In the following, the three dates will be denoted as low, intermediate, and high cover, respectively. The canopy covers of pea and oat varied between 3.8% and 51.8% across dates and cropping systems (Table 1). The weed cover was comparably low (0.4–2.0%) across all dates, crops, and cropping systems. Pea and oat were in the growth stages (BBCH, [28]) between 12–32 and 12–21, respectively. Besides variability in plant size and structure, overlapping of plants, and weed pressure, differences in illumination conditions (sunny–cloudy) and soil reflectance (dry–wet) occurred.

**Table 1.** Mean values of the canopy cover of pea, oat, and weed in sole crops and intercrops for the three datasets.

The images were acquired with a D3100 equipped with an AF-S DX NIKKOR 18–55 mm 1:3, 5-5, 6G VR lens (Nikon Corporation, Tokio, Japan) at a distance between 0.5 m and 1 m to capture at least three crop rows in each image. These distances are applicable for moving vehicles like tractors or robots, to automate the image acquisition. The shutter speed was adapted to given illumination conditions and ranged between 1/160 and 1/1250 s with a shorter exposure time under very bright conditions. All the other settings were kept constant (ISO 400, F/8). The image size was 3072 × 4608 pixels with a resolution of 240 dpi. The images had a spatial resolution of 3–6 pixel/mm, depending on the acquisition distance. The images were taken hand-held, i.e., horizontal leveling, and therefore, distance did vary to a certain extent between images. The images were captured along the 80 m of each of the 18 strips, which resulted in a total of 300–400 single images per date. In Figure 2, three exemplary images of the sole and intercrops are shown for the three acquisition dates with different canopy cover, denoted as low, intermediate, and high cover dataset.

**Figure 2.** Exemplary images of the three datasets used for the evaluation. (**a**) Low cover dataset (25 April), (**b**) intermediate cover dataset (2 May), and (**c**) high cover dataset (16 May). From top to bottom: pea, oat, and pea–oat intercrops.

## *2.2. Image Processing and CNN Architecture*

For subsequent analysis, first, the software imageJ was used to cut out a section of 2600 × 2600 pixels in the center of each image before processing [29]. Next, these images were analyzed by a semantic CNN to generate the different pixel classes. The output images were afterwards filtered with a Matlab script (Matlab R2019a, The MathWorks Inc., Natick, MA, USA) to get rid of small single objects in binary image results, which corresponded to weeds and other outliers. Therefore, all objects in the binary image with a pixel number smaller than 2400 pixels were removed from the image.

For the semantic segmentation of the acquired images, the online platform of the company Wolution GmbH & Co. KG (Planegg, Germany) was used. They supply an easy and accessible online interface, which could be used to upload, label, and train images with different machine learning algorithms. In our case, we used a semantic CNN based on a deep neural network, similar as described by Havaei et al. [30]. The CNN architecture exploits local and contextual features, and therefore, is able to model the local details and the global context at the same time. The CNN applies a series of layers, constructed from convolutions and activation functions (in this case a rectified linear unit), to the image. In a CNN, each successive layer results in a more abstract feature map, providing more global (contextual) features. The final layer applies a softmax activation function, which results in normalized class probabilities for each pixel in the image. The CNN is trained with the Adam optimizer [31] until the validation loss converges. For this purpose, the dataset is split into 80% training data and 20% validation data.

The Adam optimizer was trained with a learning rate of 10−4. The neural network training was performed with randomly cropped image patches of size 64 × 64 pixels. Each image patch was randomly mirrored and rotated to enlarge the dataset. One batch (for stochastic gradient descent) included 128 image patches, randomly selected from the training images. After each 1000th batch (1000 × 128 image patches), a validation step was performed to check the training error. For validation, 1000 batches from the validation images were randomly selected and evaluated. For the current datasets, we found that the training error converged after about 20 epochs of 1000 batches. In total, the training process took about three hours per dataset.

#### *2.3. CNN Training*

The training was done with manually pixel-wise labeled images of pea and oat. The training was performed only with images of single plants of pea and oat. Intercropping images were not used for training. For improving the results, the tendrils and the leaves of pea were labeled into two individual classes. Existing weeds in the images were not labeled, and therefore, were part of the soil/background class. This was possible without a high error rate as the datasets showed a low weed pressure (see Table 1).

In total, five different networks were trained and evaluated. The first network just used training data from the low cover dataset (LC), the second just from the intermediate cover dataset (IC), and the third just from the high cover dataset (HC). The fourth network combined training data from LC and IC (LC + IC). The fifth network combined all three datasets LC, IC, and HC (LC + IC + HC) for training. Examples of labeled images for the three different networks LC, IC, and HC are shown in Figure 3.

**Figure 3.** Examples of labeled images for training of the three networks: (**a**) low cover (LC), (**b**) intermediate cover (IC), and (**c**) high cover (HC). Three classes were labeled: pea leaves (green), pea tendrils (blue), and oat plants (yellow).

The reason behind this training procedure is that labeling at early growth stages is much less time consuming. It can be partly automated using index-based segmentation as plants overlap only to a small extent. In addition, labeling sole crops is much easier and faster than to differentiate and individually label species in a mixed canopy of an intercropping system.

Therefore, the network input data was ordered from low to high effort for creating the training input. Additionally, the evaluation of the light competition within an intercropping system has to start at an early growth stage with low cover. If we could increase the accuracy of a good working network with just a few additional training images, this could facilitate development time of highly accurate networks for different growth stages.

The idea was to first check the performance of the networks based on each dataset individually, and how the combination of training data influenced the results and robustness. In Table 2, the differently trained networks and their number of input images and plants, and the pixels per class are given in detail. The comparability between the networks individually trained on a specific dataset was assured by a similar number of pea and oat pixels contained in the training images.

**Table 2.** Description of the training input for the five networks.

#### *2.4. Evaluation*

For evaluation, 15 images (each 2600 × 2600 pixels) of each dataset, which were not part of the training data, were randomly selected. From each dataset (low, intermediate, high), five images were selected from each sole crop and the intercrop. Each individual image was divided into three different classes: soil, oat, and pea. The two classes of pea used for training (leaves and tendrils) were combined to compare to ground truth. The results of the CNNs were evaluated with the DPA-Software, which includes a pixel-wise comparison between ground truth image and result [11]. The transferability of the CNNs was evaluated for

(1) The different datasets (low, intermediate, and high cover) by analyzing all three datasets with each of the five trained networks including both sole and intercrops;

(2) The intercrops specifically, by comparing the results achieved for the sole and the intercrops. For analyzing the accuracy of the networks, the True Positives (*TP*), False Negatives (*FN*), and False Positives (*FP*) for each single class were evaluated and the Precision and Recall were calculated:

$$Precision = \frac{TP}{TP + FP'} \tag{1}$$

$$Recall = \frac{TP}{TP + FN}.\tag{2}$$

Additionally, we calculated the intersection over union (*IoU*) according to

$$IoI = \frac{|A \cap B|}{|A \cup B|},\tag{3}$$

where *A* corresponds to the quantity of ground truth pixels and *B* to the quantity of result pixels of each class.

#### **3. Results and Discussion**

First, the performance of the networks over single and intercrop images was tested and evaluated. In all three tested datasets, the classes of oat, pea, and soil pixels were detected at high rates. The networks in general detected oat more reliable than pea in the images. The individually trained networks (LC, IC, and HC) performed best for their corresponding dataset with an average precision of 91% (88–95%) and 75% (64–83%), a recall of 84% (81–89%) and 74% (65–83%), and an Intersection over Union (IoU) of 78% (73–81%) and 60% (48–68%) for oat and pea, respectively (Table 3). The network trained on all datasets (LC + IC + HC) showed almost equal performance and even slightly increased the performance when applied on the intermediate and high cover datasets.

The transfer of the networks to other datasets, especially for the HC-trained network, showed a strong decrease in the mean Intersection over Union (mIoU). The transfer of LC onto the intermediate cover dataset and IC onto the low cover dataset yielded in a mIoU higher than 69%. Whereas, the transfer of LC and IC onto the high cover dataset and the HC onto the other two datasets showed a strong decrease of the mIoU with values between 40% and 50%.

An example for the performance of the three networks individually trained with sole crop images from the three datasets on the intermediate cover dataset is shown in the Figure 4.

**Table 3.** Intersection over union (IoU, %), precision (%), and recall (%) for pea, oat, and soil for the three evaluation datasets (low cover—LC, intermediate cover—IC, and high cover—HC) each analyzed by the five different networks. Individually trained networks in bold.

**Figure 4.** Examples for images from the intermediate cover dataset analyzed by the three individually trained networks. From top to bottom: pea, oat, and pea–oat intercrops (yellow: oat, green: pea, blue: pea tendrils, white: pea combined).

These results indicate that the transferability across different growth stages (respectively, degree of canopy cover) is challenging. Therefore, the need for retraining the network for a new dataset seems the only option to optimize the performance. The difference between the accuracy of oat and pea pixels could be a result of the increased complexity of the plant and the overall cover of the two species in the images. Especially in intercropping, the cover of pea was less than half of oat (Table 1). Additionally, the tendrils of pea where hard to detect, especially when their share in cover increased during growth and more tendrils with a small diameter were present. Interestingly, the mIoU dropped considerably for the later growth stage, where overlapping of plants increased and the canopy cover was higher. The main reason for this was the lower quality in detection of the intercrops as shown in Table 4.

**Table 4.** Intersection over union (IoU, %) of pea and oat in sole crops and intercrops for the three evaluation datasets (low, intermediate, and high cover) each analyzed by the individually trained network (LC, IC, HC) and the network trained on all datasets (LC + IC + HC).

The sole crops were detected well across all datasets with an IoU between 72% and 90%. The transfer of the networks trained on sole crop images onto the intercrops showed a good performance for intercropped oat for the first two datasets (IoU: 70–80%). However, the IoU of intercropped pea decreased considerably and for the high cover dataset, the network performed poorly for the intercrops and especially pea.

The largest error was associated with the small tips of the oat plants, which were detected as pea tendrils. The center of the plants resulted in another typical zone of errors. A reason might be shading, which created a different coloration for the center compared with the rest of the plant.

Reasons for this behavior could be the comparable low training input, the change of color, and the different shape of the plants in the high cover dataset as both species reached the next main growth stage (pea: stem elongation, oat: tillering). This leads to the point that future CNN architectures for applications in agriculture should address different growth stages in the networks. To gain a network with the given method that fits all growth stages seems challenging. However, future architectures could address the special needs for extracting invariant features for agricultural plants under different growth stages. The better performance of the LC + IC + HC network on intercropped pea for the high cover dataset indicated that better results might be obtained with more training images of pea. A few example images of this network are shown in the following Figure 5.

**Figure 5.** Examples for images from the three datasets: (**a**) low, (**b**) intermediate, and (**c**) high cover analyzed by the network trained on all datasets (LC + IC + HC). From top to bottom: pea, oat, and pea–oat intercrops (yellow: oat, green: pea, blue: pea tendrils).

For the general use in agriculture, the absolute precision of plant pixels is not mandatory. As todays machines for fertilization and weeding are not plant-specific, mean estimates over the field or a specific site are sufficient. Therefore, the mean crop cover over an area of interest is enough to estimate the light competition between the intercrops for a given agronomic practice (e.g., sowing density of each intercrop) or site. With this in mind, the two best performing networks for each dataset were selected and the mean average cover (*m*) evaluated by the CNN was compared to the ground truth on an absolute (Δa) and relative (Δr) scale (Table 5).

**Table 5.** Mean estimated canopy cover (*m* ) of pea and oat in sole crops and intercrops and absolute

The results showed that the absolute cover was estimated quite accurately. The maximum absolute difference to ground truth was −1.1% for pea and −5.3% for oat with a relative difference not exceeding 12.1%. The estimated canopy cover of the intercrops were on absolute scale in the same magnitude as the sole crops. However, given their lower canopy cover, relative differences were higher reaching a maximum of 25%. Interestingly, a lower IoU as shown for the high cover dataset (Tables 3 and 4) does not necessarily result in a lower accuracy of estimated canopy cover.

Compared to the existing state of the art work in the field, the trained networks did perform quite well. The reached mIoU between 66% and 81% for the networks is in the range of published results as mentioned in the introduction. This study confirmed that transferring a CNN to another dataset resulted in a considerable decrease in IoU. This corresponds to the results of Lottes et al. and Bosilj et al. [21,23].

Shorten and Khoshgoftaar highlighted in their review that there are no existing augmentation techniques that can correct a dataset that has a very poor diversity with respect to the test data [27]. However, especially in an agricultural domain, this is the most challenging point, as diversity of the possible real test environment is huge. Therefore, future research has to address the generalization and transferability of networks in the agricultural domain, as we deal with unstructured objects in unstructured environments [25]. Data augmentation techniques like geometric transformations, color space transformations, kernel filters, mixing images, random erasing, feature space augmentation, adversarial training, generative adversarial network-based augmentation, neural style transfer, and meta-learning schemes could help to gain better transferability of networks in the future.

## **4. Conclusions**

The results of this study showed that it is feasible to estimate the canopy cover in intercropping systems with a satisfying accuracy based on sole crop training data. However, the transferability of trained networks onto other datasets—than the one used for training—has to be improved in future research to reduce the effort for retraining the networks for new situations. In a next step, the network will also be trained with another dataset having a higher weed pressure to estimate

weed cover separately (not as a part of the soil/background class). Besides the use of the estimated canopy cover to analyze light competition between intercrops and identify promising management practices, a combination of the results with site-specific management would open new possibilities to dynamically influence the interactions between crop species to maximize yield and weed suppression.

**Author Contributions:** Conceptualization, S.M. and D.R.; formal analysis, S.M. and D.R.; funding acquisition, S.M.; methodology, S.M. and D.R.; project administration, S.M.; software, D.R.; validation, S.M. and D.R.; visualization, S.M. and D.R.; writing—original draft, S.M. and D.R.; writing—review & editing, S.M. and D.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the European Union's Horizon 2020 Programme for Research & Innovation under grant agreement n◦727217 (ReMIX: Redesigning European cropping systems based on species MIXtures).

**Acknowledgments:** We thank Jonas Boysen for helping with preparing the ground truth data, Alexander Stana and Nils Lüling for proofreading, and Julian Zachmann and the technical staff of the research station for managing the field experiment. Additionally, we thank Dominik Wuttke and Moritz Münchmeyer for their help and support while preparing this manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest.

## **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

*Article*

## **Water-Soluble Carbohydrate Recovery in Pastures of Perennial Ryegrass (***Lolium perenne* **L.) and Pasture Brome (***Bromus valdivianus* **Phil.) Under Two Defoliation Frequencies Determined by Thermal Time**

## **Iván Calvache 1,2, Oscar Balocchi 1,\*, Máximo Alonso 1, Juan Pablo Keim 1 and Ignacio López 3**

Received: 18 October 2020; Accepted: 18 November 2020; Published: 20 November 2020

**Abstract:** The objectives of the experiment were to (i) examine the dynamics of WSC use and the recovery of leaf sheaths and blades of *Bromus valdivianus* Phil. and *Lolium perenne* L. subjected to two defoliation frequencies (DFs) determined by thermal time (TT); (ii) evaluate how DF influenced regrowth and accumulated herbage mass (AHM) during fall. Defoliation was carried out at frequencies of 135 and 270 accumulated growing degree days (AGDDs) for both species. Twelve plots were arranged in a three-block design. All plots had a conditioning period to establish the assigned DF prior to sampling. From the start of the experiment, "cores" were collected from each plot every three days until the DF was reached. Every core was separated into leaf and sheath material before measuring the WSC concentration. *Lolium perenne* had concentrated more WSCs than *B. valdivianus*. Both species adapted their WSC recovery according to the DF. The recovery of WSC was faster under a DF of 135 AGDDs than that of 270 AGDDs. Leaf sheaths contained more WSCs than leaf blades and were identified as WSC storage organs. This period can be used as the optimal defoliation interval in *B. valdivianus* and *L. perenne* grazing systems.

**Keywords:** accumulate growing degree days; phyllochron; grass regrowth; leaf sheaths; blades

## **1. Introduction**

Along with soil fertility and environmental conditions, defoliation frequency (DF) is one of the main factors related to pasture management that influences the rate of regrowth and accumulated herbage mass (AHM). However, when days are used to assign the interval between defoliations, changes in plant phenology are not considered. Temperate forage grows differently depending on the season. Therefore, using days as a criterion for DF can reduce pasture growth and longevity. In some perennial crops, such as sorghum and oats growing under field conditions, the thermal time, expressed as accumulated growing degree days (AGDDs), has been widely used to determine sowing data [1,2] because there is a positive relation between plant development and temperature [3]. The thermal time (TT) was used as a tool to determine the best DF in *Lolium perenne* L. and *Bromus valdivianus* Phil. grass [4], because they are two species with different growth habits growing under the same environmental conditions.

Water-soluble carbohydrates are also important for DF because leaf growth is related to a decrease in WSC concentration [5,6]. When carbohydrate synthesis is greater than its utilization, WSCs are temporarily stored in the base of leaf sheaths with a lower proportion stored in leaf blades [7]. Water-soluble carbohydrates are an immediate energy source for plant growth after defoliation; therefore, the quantity of stored WSC pre-defoliation is associated directly with the speed of pasture regrowth [8].

In pastures, the amount of stored WSCs is associated with the plant's phenological stage [9]. *Lolium perenne* uses WSCs as an energy source immediately following defoliation until approximately one leaf has expanded completely and can generate enough energy via photosynthesis to cover maintenance requirements and tissue development [10,11]. Once the third leaf has emerged, it can be assumed that the plant has accumulated enough WSC reserves to tolerate subsequent defoliation.

Defoliation frequency influences the mobilization and storage of WSCs in grass pastures. Short intervals between defoliation do not allow plants to recover enough WSCs, reducing pasture persistence, whereas longer intervals permit plants to synthesize enough carbohydrates to support post-defoliation regrowth [9,12,13]. The season also influences WSC recovery. Most studies have been conducted during spring or in controlled environments because it is known that the concentration of WSCs in grass increases during daylight as a result of the positive balance between photosynthesis and respiration [10,14]. In C3 species, the accumulation and mobilization of stored WSCs, predominantly fructans, are of great importance in the synthesis of new tissues during regrowth [15]. Hence, the concentration of WSCs in plants varies through the day and from season to season [16,17], playing an important role in the balance between photosynthesis, carbon contributions, and C requirements for plant growth and development [18].

Some studies have concluded that *L. perenne* has a higher concentration of carbohydrates than *B. valdivianus.* The concentration of WSCs in species such as *L. perenne* and *B. valdivianus* increases in the afternoon because of increased photosynthetic activity in the presence of full light [19,20]. In fall, post-defoliation regrowth is slow compared to spring because the temperature drops and luminosity is lower [9]. However, fall is an important season because, along with spring, it provides the most forage during the year. Therefore, the two objectives of this study were (1) to examine the dynamics of WSC use and the recovery of leaf sheaths and blades of *L. perenne* and *B. valdivianus* subjected to two DFs; (2) to evaluate how DF influences regrowth and accumulated herbage mass (AHM) during fall.

## **2. Materials and Methods**

## *2.1. Site Description*

This study was carried out at the Austral University of Chile (UACh) in the Austral Agriculture Research Station (39◦46 S, 73◦13 W, 12 m a.s.l.) in Valdivia, southern Chile, during the fall of 2017, from 20 March to 23 June. The precipitation during the study period (March to June) was 677 mm with a mean temperature of 11 ◦C (Figure 1), and the annual precipitation and mean temperature at the experimental site was 2210 mm and 11.7 ◦C, respectively. Climate information for the experiment was collected daily via a meteorological station (Agromet-Inia) located 5 m from the study site. The topography was flat with a slope of less than 2%. The soil corresponded to a Duric Hapludand Andisol, in the Valdivia series [21], with a pH of 5.4, organic matter level of 14.6%, 17.4 mg kg−1 Polsen, and 4.9% aluminum saturation (Soil Lab, Institute of Agricultural Engineering and Soils, Faculty of Agriculture, Universidad Austral de Chile, Valdivia, Chile). The fertilizer application was 150 kg ha−1 year−1 of nitrogen, 44 kg ha−1 year−1 of phosphorous, and 50 kg ha−1 year−1 of potassium. The soil was suitable for perennial species.

**Figure 1.** Daily maximum (solid line) and minimum (dashed line) temperature and rainfall (bars) for Valdivia, Chile, during the fall of 2017.

## *2.2. Experimental Design*

The experiment included 12 plots of 15 m2 (3 m wide × 5 m long) randomly distributed into three blocks. In March 2015, six plots were sown with 30 kg ha−1 *L. perenne* cv. Alto and six with 45 kg ha−1 *B. valdivianus* Phil. Each plot was defoliated with one of the following fixed DFs based on AGDDs: DF1 = 135 AGDDs that corresponded to the 1.5 leaf stage (LS) for *L. perenne* and 1.7 for *B. valdivianus*; DF2 = 270 AGDDs that corresponded to the 3 and 3.4 LS for *L. perenne* and *B. valdivianus*, respectively, using a rotary mower equipped with a collection bag and with a residual height of 5 cm left.

Each block comprised four plots, and each plot corresponded to the interaction between species (*L. perenne* and *B. valdivianus*) and DFs (135 and 270 AGDDs). The interaction between these two factors was considered the treatment (TTm). All plots were defoliated according to the assigned DF during the plot conditioning period from October 2016 to March 2017. Sampling was conducted from 21 March to 21 June 2017.

The following equation was used to calculate accumulated thermal time:

$$\text{AGDD} = \sum \left[ (T\_{\text{max}} + T\_{\text{min}})/2 \right] - T\_{\text{base}} \tag{1}$$

where *T*max is the daily maximum air temperature, *T*min is the daily minimum air temperature, and *T*base is the temperature below which the observed process does not occur. A base temperature of 5 ◦C was used for both species, and the temperature accumulated from day 1 to day *n* [22,23]. The air temperature was measured at 150 cm high above the ground level, and it was taken from the meteorological station close to the field experiment.

## *2.3. Evaluated Variables and Sampling*

Three cores (90 cm in diameter and 10 cm deep) were collected from each plot at 08:00 every third day, until each plot reached the assigned AGDDs. Each core corresponded to a sample composed of soil with root, leaf blades, and leaf sheaths which were stored on ice to avoid WSC losses until they were transported to the Animal Nutrition Laboratory of the UACh, where they were washed to remove soil residue. The root was discarded, and the grass blades were separated from the sheaths.

The blade and sheath materials were measured separately for green weight and then dried in a forced-air oven at 65 ◦C for 72 h to measure dry matter (DM) content and dry weight. Dried samples were ground to 1 mm using a Wiley mill (Model Digital ED-5, Thomas Scientific, Swedesboro, NJ, USA) and stored in plastic containers at ambient temperature for subsequent WSC analysis.

Water-soluble carbohydrate concentration was determined in the blade and sheath by near infrared reflectance spectroscopy (NIRS) with a FOSS-NIRSystems Model 6500 and Version 4.4 FOSS-ISIScam software and equations developed in the UACh Animal Nutrition Laboratory. The equation for WSC concentration was calibrated using the anthrone method described by Yemm and Willis [24], where WSCs are expressed as glucose and determined using spectroscopy based on the blue–green color created when carbohydrates are heated with anthrone in sulfuric acid. The standard errors of cross-validation and *R*2 for WSCs were 6.99 and 0.96, respectively.

The AHM was estimated every six days using a rising plate meter (Ashgrove Plate Meter, Hamilton, New Zealand). Each value was calculated using the average height of 10 samples per plot. To estimate production per hectare (kg DM ha−1), an equation specific to fall pastures in southern Chile was used [25]:

$$\text{Y} = 120\text{X} + 350 \text{ (}r^2 = 0.74\text{)},\tag{2}$$

where Y = AHM in kg DM ha−1, and X = compressed average height.

When each plot accumulated the assigned AGDDs, it was defoliated again for further sampling. Therefore, during the fall, plots with 135 AGDDs were defoliated twice, and plots with 270 AGDDs were defoliated once. The growth was calculated from AHM at the defoliation time minus the residue that was measured in the past defoliation and calculated at 1200 kg DM ha−1, and the growth of 135 AGDDs was adding up the growth for both periods.

## *2.4. Statistical Analysis*

Response variables included WSC concentration (g kg−1) in blades and sheaths and AHM production (kg DM ha−1). Data were analyzed using the MIXED procedure of SAS (SAS Institute, V9.0, 2008) in a complete randomized block design with a factorial arrangement of treatments (two species and two DF), where AGDDs and species were fixed effects, and the field block and their interaction were random effects. Sampling time was included as a repeated measure in the model. The covariance structure [26] was based on the probability test and the Akaike information criterion, test according to (a) no structure, (b) composite symmetry (CS), (c) heterogeneity of compound symmetry (HCS), (d) Toeplitz (TOEP), and (e) Toeplitz heterogeneity (HTOEP). Prior to analysis, all data were checked for normality and homogeneity of variances. When there were significant differences (*p* < 0.05), a multiple mean comparison test (LSMEANS) was performed with the PDIFF command.

### **3. Results**

## *3.1. E*ff*ect of Defoliation Frequency on WSC Concentration in Leaf Sheaths and Blades*

*Lolium perenne* L. and *B. valdivianus* defoliated after 135 AGDDs had similar WSC levels (*p* > 0.05, Figure 2a,b) in both the blades and sheaths. When the DF was extended to 270 AGDDs, significant differences (*p* < 0.05, Figure 3a,b) were found between the two species. This change became apparent after 195 AGDDs, at which point *L. perenne* regrowth was able to accumulate approximately 33% more WSCs than *B. valdivianus*. This difference remained constant between the two species from 195 to 270 AGDDs.

**Figure 2.** Mean of two growth cycles for water-soluble carbohydrate (WSC) concentration (g kg−1 dry matter (DM)) in leaf blades (**a**) and leaf sheaths (**b**) for *L. perenne* (–-–) and *B. valdivianus* Phil. (--) plots defoliated at 135 accumulated growing degree days (AGDDs). Bars indicate standard error.

**Figure 3.** Mean water-soluble carbohydrate (WSC) concentration (g kg−1 DM) post-defoliation in leaf blades (**a**) and leaf sheaths (**b**) for *L. perenne* (–-–) and *B. valdivianus* Phil. (--) plots defoliated at 270 accumulated growing degree days (AGDDs). \* Indicates significant differences (*p* < 0.05) between species. Bars indicate standard error.

It was observed that in both *L. perenne* and *B. valdivianus*, the concentration of WSCs in the blades and sheaths significantly decreased following defoliation until 30 AGDDs (*p* < 0.05, Figures 2 and 3), (**a**) and (**b**) when plants began to accumulate WSCs. This process was similar for both blades and sheaths.

After both DFs, leaf blade WSC concentration was reduced by approximately 80% in *L. perenne* and 74% in *B. valdivianus*. For both species, plants began to recover WSCs after 65 to 90 AGDDs post-defoliation when defoliated every 135 AGDDs. For plants defoliated every 270 AGDDs, the recovery of WSCs was initiated after 90 to 120 AGDDs had been reached. On the other hand, the recovery rate of WSCs in leaf sheath was different between species defoliated at 270 AGDDs. The *L. perenne* recovered faster than the *B. valdivianus* using 1.5 AGDDs per 1 g of WSC, whereas *B. valdivianus* needed more TT (2.3 AGDDs) to accumulate the same amount of WSCs. When the species were defoliated at 135 AGDDs, the recovery rate was the same for *L. perenne* and *B. valdivianus* with 2.5 AGDDs per 1 g of WSCs.

## *3.2. Water-soluble Carbohydrates and Pasture Regrowth*

After the initial defoliation (day 0) and during the subsequent growth cycle of 135 AGDDs DF, there were no significant differences in WSC levels between species for both leaf blades and sheaths

(*p* > 0.05, Figure 4), except at the end of the experiment, when the leaf blade WSC concentration of both species had not fully recovered. In contrast, leaf sheaths recovered WSCs to the pre-defoliation levels measured on day 0 (*p* < 0.05, Figure 4). Mean concentrations of WSC were 25% and 28% greater in leaf sheaths than in leaf blades for *L. perenne* and *B. valdivianus*, respectively, when defoliation occurred every 135 AGDDs (Figure 4a; *p* < 0.05).

**Figure 4.** Mean of two growth cycle for water soluble carbohydrates (WSCs, g kg−1 DM) and accumulated herbage mass (AHM, kg DM ha−1) in *Bromus valdivianus* Phil. (**a**) and *Lolium perenne* L. (**b**) in: leaf blades (--) and leaf sheath (–-–) defoliated at 135 accumulated growing degree days (AGDDs) and AHM (–o–). \* Indicates significant differences (*p* < 0.05) between leaf sheaths and blades for the same species. Bars indicate the standard error.

When plants were defoliated after 270 AGDDs (Figure 5), the WSC concentration was significantly greater for leaf sheaths than for leaf blades at day 0 and after 135 AGDDs (*p* < 0.05, Figure 5). Between 30 and 120 AGDDs, the WSC concentration was similar for both leaf portions (Figure 5). The same tendency was observed in pasture plots defoliated every 135 AGDDs, where the WSC concentration reduced from day 0 to 30 AGDDs (Figures 2–4).

**Figure 5.** Mean water soluble carbohydrates (WSCs, g kg−1 DM) and accumulated herbage mass (AHM, kg DM ha−1) in *Bromus valdivianus* Phil. (**a**) and *Lolium perenne* L. (**b**) in: leaf blade (--) and leaf sheath (–-–) defoliated at 270 accumulated growing degree days (AGDDs) and AHM (–o–). \* Indicates significant differences (*p* < 0.05) between leaf sheaths and blades for the same species. Bars indicate the standard error.

In *L. perenne* plots defoliated every 270 AGDDs, the WSC concentration was reduced by 85% compared with the pre-defoliation levels (day 0) and did not recover until approximately 215 AGDDs, when plants reached the WSC concentration used for regrowth. In *B. valdivianus* plots defoliated with

the same DF, the WSC concentration levels decreased by 75% post-defoliation and required 205 AGDDs to completely recover the WSC reserves used during regrowth (Figure 5a,b).

Similar patterns in the movement of WSCs throughout the regrowth cycle were observed in both *L. perenne* and *B. valdivianus* plots defoliated after 135 and 270 AGDD. Plants in both DFs utilized WSC immediately after defoliation, but the replenishment of WSC levels before the next defoliation was slower at 270 AGDDs than at 135 AGDDs (Figures 4 and 5).

Plots defoliated at 270 AGDDs had time to accumulate greater concentrations of WSC compared to those defoliated at 135 AGDDs. In both forage species and at both DFs, the concentration of WSC was higher in leaf sheaths than in leaf blades during the entire regrowth cycle. When considering the combined effect of DF and species, the highest concentration of WSCs corresponded to leaf sheaths of *L. perenne* plots defoliated at 270 AGDDs, followed by sheaths of *B. valdivianus* defoliated at the same frequency.

The water-soluble carbohydrate concentration in leaf sheaths did not significantly differ between species when defoliated at 135 AGDDs (*p* > 0.05, Table 1). The highest leaf blade WSC concentration levels were found in the interaction among *L. perenne* plots defoliated at 270 AGDDs, but there were no other significant differences in leaf blade WSC concentration level interactions (*p* > 0.05, Table 1). A significant interaction between species and DF was observed for both blades and sheaths. In *B. valdivianus*, WSC in blades was similar in both DF, whereas for *L. perenne*, a greater WSC concentration was observed for DF 270 compared with 135. In the case of sheaths, the interaction showed a greater increase from DF 135 to DF 270 for *L. perenne* (+58 g kg−1 DM) compared with *B. valdivianus* (+31 g kg−1 DM). The same trend was obtained for WSCs per hectare, where *L. perenne* produced 110 kg more than *B. valdivianus*, both defoliated at 270 AGDDs, while *L. perenne* and *B. valdivianus*, which were defoliated at 135 AGDDs, did not show a significant difference.

**Table 1.** Mean water-soluble carbohydrate (WSC) content (kg kg−1 DM) in leaf sheaths and leaf blades and production per hectare (kg WSC ha−1) for *Bromus valdivianus* Phil. and *Lolium perenne* L. defoliated at 135 and 270 accumulated growing degree days (AGDDs).

Means within a column with different superscripts differ (*p* < 0.05); SEM = standard error of the mean.

## *3.3. Defoliation Frequency and Growth*

The increase in growth followed the same trend as WSC replenishment levels. As shown in Figures 4 and 5, growth began to increase after 30 AGDDs in both DF treatments. In contrast, WSCs did not begin to recover until 90 AGDDs in plots defoliated at 135 AGDDs (Figure 4) and 45 AGDDs in plots defoliated at 270 AGDDs (Figure 5). Table 2 shows that the average value of the interaction between DF and species for growth was not significant (*p* > 0.05, Table 2), illustrating that similar growth occurs in fall grazing with intervals of 135 and 270 AGDDs. However, *B. valdivianus* defoliated at 135 AGDDs produced 52 (6%) kg DM ha−1 more than *L. perenne* defoliated at 270 AGDDs; that in one hectare is not significant but in several hectares it could be significative. In addition, pastures defoliated to 135 AGDDs have a better nutritive value in terms of crude protein and metabolizable energy than defoliated pastures at 270 AGDDs.

**Table 2.** Mean growth (kg DM ha−1) of *Bromus valdivianus* Phil. and *Lolium perenne* L. defoliated after 135 and 270 accumulated growing degree days (AGDDs).

x Accumulated value of two growth cycles each one at 135 AGDDs; SEM = standard error of the mean.

## **4. Discussion**

The present field study highlights the effect of DF, determined by AGDDs, on the utilization, concentration levels, and recovery of WSC concentrations in *L. perenne* and *B. valdivianus* pastures during fall. More frequent defoliation (135 AGDDs) reduced the levels of WSCs in both leaf sheaths and blades, but reserves were replaced faster. When defoliated with a lower frequency (270 AGDDs), WSC levels were higher but recovery was slower.

## *4.1. Defoliation Frequency and WSCs*

During most of the growth cycle, the WSC level and recovery rate werer greater in leaf sheaths than in leaf blades, supporting previous studies that report the sheath as the main WSC storage component in grass species [7]. Moraes et al. [27] evaluated 24 *poa* species and reported that the WSC concentration was greater in sheaths than in blades, with the exception of *Echinolaena inflexa*. The storage of WSCs in leaf sheaths is probably a survival mechanism given that the reserves are located close to the growth points and allow for efficient use. If WSCs were predominately stored in the leaf blades, they would be removed by grazing activity and the plant would lose this resource for regrowth in the subsequent cycle [28].

In most of terrestrial plants, the use of WSCs begins immediately after a plant is defoliated and after recovery process the reserves are restored to the original concentration [29]. In the present study, plants defoliated with the lower DF of 270 AGDDs recovered the original concentration (between 100 to 120 g kg−1 DM) after approximately 195 AGDDs. Similar results were reported by Donaghy and Fulkerson [5], who measured 125 g kg−1 DM of WSC for *L. perenne* pastures defoliated at the three-leaf stage with 5 cm of residue. Turner et al. [6] reported that *L. perenne, B. willdenowwii* Kunth., and *Dactylis glomerata* L. recovered WSCs after 2.5 to 3 leaves had appeared. Many studies have shown that AGDDs are directly related to the phyllochron and the leaf stage [30]. The new leaf appearance in *L. perenne* takes 88 AGDDs, while for *B. valdivianus* it is only 77 AGDDs [4]. After 195 AGDDs, pasture grass has between two and three leaves per tiller and some 80% of WSC reserves are restored, which supports recovery after the next grazing cycle [31].

Berone et al. [32] evaluated the impact of DF on WSC concentrations in winter pastures of *L. perenne* and *B. stamineus* in Argentina using leaf stage as a grazing criterion. They reported that the WSC concentration was greater in plants defoliated with less frequency (5 leaf stage) than in plants defoliated more frequently (3 leaf stage), and the WSC mean values were 25 g kg−1 DM for *B. stamineus* and 15 g kg−1 DM for *L. perenne*. The response to different DFs is consistent with the trend measured in the present study; however, they reported lower WSC values. This may be due to the fact that the two studies were conducted under different seasons and climate conditions.

The measurements of WSC concentration following defoliation at intervals of 135 and 270 AGDDs in *L*. *perenne* and *B. valdivianus* indicated that WSC recovery was initiated at the same time in both leaf sheaths and blades, with a consistently greater concentration in the sheaths. This supports the conclusion that, regardless of the DF or species, leaf sheaths are the main organ for WSC reserves [10].