Is Diversified Crop Rotation an Effective Non-Chemical Strategy for Protecting Triticale Yield and Weed Diversity?

Abstract

Diversified crop rotation (DCR) has re-gained attention worldwide as a non-chemical practice for increasing the sustainability of cereal production systems. This study focused on comparing the effects of two weed management strategies, DCR (the six-field system: potato–oat–fiber flax–winter rye–faba bean–winter triticale) without the application of a herbicide (DCR strategy) and with the application of a herbicide (DCR + H strategy) on the yield of winter triticale and on the biomass and species, taxonomic, and functional diversities of weed communities. In addition, the responses of two triticale cultivars, Trapero and Borowik, to the weed management strategies were evaluated. Data from five growing seasons (2017–2021) of a long-term experiment (Bałcyny, northeastern Poland) are presented. The DCR strategy proved less effective in protecting the triticale yield than DCR + H but provided greater weed species, taxonomic and functional diversities. Borowik had a higher yield and responded to herbicide abandonment with a lower yield loss. When the DCR strategy was used, Borowik was more competitive against weeds than Trapero without reducing weed diversity. The triticale yield correlated negatively with weed biomass and diversity, and weed diversity and weed biomass were positively correlated. The findings show that winter triticale can be grown in DCR without the application of a herbicide if a high-yield, competitive cultivar such as Borowik is used. Growing such a cultivar under DCR conditions without the application of a herbicide promotes weed diversity; however, in years with less favorable weather conditions, it may be necessary to accept a moderately lower yield compared to the yield provided with herbicide protection. The relationship between weed diversity and weather conditions is a subject for further research.

1. Introduction

Modern agriculture faces the enormous challenge of feeding a rapidly growing population without further damaging the environment [1,2]. The necessary increase in agricultural production can be effectively supported by conventional agrochemicals (although not exclusively), mainly synthetic fertilizers and pesticides; however, at the same time, these products are accused of having particularly harmful environmental and health effects [3]. In this context, all non-chemical strategies that support an increase in food production are gaining or re-gaining attention.

Cereals are the most important source of food for humans and animals all over the world. They provide dietary energy, essential proteins and micronutrients, and a diverse array of non-nutrient bioactive components [4]. Despite the raised debates over the role of cereals in the human diet, they will continue to be crucial in ensuring the food security in the world, especially in the Global South [5]. The world’s top three cereals include maize, wheat, and rice, which comprise 38.8%, 25.4%, and 25.3% of global cereal production, respectively [6]. These are followed by barley (5.24%), sorghum (1.96%), millet (1.02%), oats (0.84%), and triticale (0.51%), which recently overtook rye (0.50%).

Triticale (x Triticosecale Wittm. ex A. Camus) is a human-made species developed via crossing wheat (Triticum spp.) and rye (Secale cereale L.). It combines favorable alleles from both progenitor species (the hardiness and nutrient use efficiency of rye and the high grain yield and nutritional qualities of wheat), which not only adapts it to environments that are less favorable for wheat but also provides its higher biomass yield and forage quality [7]. It is claimed that triticale can be used as an alternative to other cereals, mostly wheat (Triticum aestivum L.), in livestock feed production and has the potential to become the preferred industrial energy crop [8]. Although triticale is a relatively new crop species, there has been growing interest in it since 1975 (when it was reported for the first time by the FAO), especially in Europe, where 93.6% of the global production of this cereal currently occurs [6]. According to data from 2020, Poland is the leading producer of triticale (6.08 million tons per year), followed by Germany (2.04), Belarus (1.54), and France (1.20) [6]. In Poland, triticale is currently the third most important cereal, following wheat and corn, and its share in the national cereal balance is 17.4% [6]. Since the beginning of triticale breeding, a large number of cultivars have been developed with differences in agronomic traits including grain and straw yield, harvest index, early vigor, tillers per plant, plant height, earliness of maturity, number of grains per spike, grain weight, grain quality, nutrient and water use efficiency, and tolerance to various stresses [9]. Breeding efforts to obtain genotypes with highly beneficial trait sets continue unabated [10]. “The Common catalogue of varieties of agricultural plant species” (with the first supplement) currently contains 418 triticale cultivars [11]. In the Polish National Register of Cultivars, there are 67 cultivars of triticale, of which 51 are cultivars of winter triticale and 16 are cultivars of spring triticale [12]. In Poland, winter triticale is more popular than spring triticale, mainly because of its higher yield potential [13]. A wide array of cultivars allows farmers to choose the right cultivar for the environmental conditions and management practices in place, increasing the chance of realizing the full yield potential of triticale.

Since the potential to increase crop acreage is limited, cereal production based on increasing yields and reducing losses due to abiotic and biotic factors will be necessary to address global food security issues [14].

Yield losses due to weeds are of critical concern in cereal-producing areas [15]. They were previously estimated at 32% (with a range of 26–40%), exceeding those due to pests (18%) and pathogens (15%) [16]. However, left uncontrolled, weeds can result in yield losses of even 100% [17]. They compete with crops for sunlight, water, nutrients, and space. In addition, they harbor insects and pathogens, which attack crop plants. Weed harmfulness depends on several factors, including weed emergence time, weed density, the species composition of the weed community, and weed and crop species competitiveness [17,18]. The demonstration of competitive abilities against weeds is a species and cultivar attribute of cereals [18,19]. Winter cereals are generally more effective at inhibiting weed growth than spring cereals [20,21]. Among winter cereals, rye is the most competitive against weeds [20], while the competitive ability of triticale falls between its progenitor species [21,22]. The literature has reported a large number of relationships between competitive ability and plant traits, including plant height, development rate, canopy architecture, and resource partitioning [23]. In general, the vigorous growth habit of triticale and its leafiness and height allow triticale to compete well with weeds [10]; however, competitiveness varies widely between cultivars [23]. It was proved that the competitive ability of tall triticale cultivars can parallel that of rye [21,22]. Moreover, the importance of traits related to cultivar competitiveness may vary between years [23]. Competitive cultivars can be used in weed management strategies as an inexpensive non-chemical option [23].

Crop rotation has been used as an effective tool for weed management since the distant past [24]. The diversity of the crop species in the rotation and their proper sequencing are critical [25]. A rotation system involving three or more crops is often referred to as a diversified crop rotation (DCR) [26]. In a well-designed DCR, an appropriate sequence of crops with varying patterns of resource competition, allelopathic interference, and soil disturbance and mechanical damage caused by crop-associated operations provides weeds with an unstable and often inhospitable environment that effectively prevents the proliferation of weed species [27]. These factors support the regulation of weed populations. However, the role of crop rotation in weed control has depreciated significantly in the 20th century due to the easy access to synthetic inputs [24]. With the introduction of selective herbicides in the late 1940s and a constant influx of new active ingredients in the succeeding decades, farmers were provided with a new tool for weed control independent of the crop production system [28]. So, over the past half-century, crop rotations have become increasingly simplified, frequently producing only one or two crops in succession [29]. The simplification of crop rotations results in the repeated exposure of weeds to the same set of ecological and agronomic conditions. This can result in increased abundances of several competitive, highly adapted weed species in crops and promote the development of herbicide resistance [28,29,30].

At present, agricultural scientists are advocating for a return to crop rotation, stressing its productive and environmental functions [24]. The implementation of diversified crop rotation (DCR) is recommended on many pathways toward sustainable agriculture [2]. DCR is indicated as a non-chemical agricultural practice [31] that enables farmers to reduce their reliance on external inputs [32], including herbicides [29].

In addition to a new perspective on the old practice, it is worth emphasizing the change in the approach to weeds that has taken place recently. Weed species have begun to be seen as an element of biodiversity, and the important ecosystem services provided by this type of vegetation have been appreciated. Primarily, the key trophic and paratrophic functions of weeds in the agroecosystem have been noticed [30,33]. Furthermore, the importance of weed diversity in mitigating yield losses has been acknowledged. Adeux et al. [34] demonstrated that diversified weed communities can limit the negative impact of competitive and dominant species on the productivity of the crop. Opinions have emerged that weeds have an underappreciated value to biodiversity [35]. Thus, the progressive loss of weed diversity due to agricultural intensification has begun to be viewed with concern [36]. Not only has a significant decrease in the species diversity of weeds been recorded [36] but reductions in their taxonomic and functional diversities have also been recorded [37].

The species diversity of weeds is most often expressed as the number of species (species richness) or via selected diversity indexes (mostly Shannon–Wiener or Simpson) that additionally include weed species abundance (density and biomass) [38,39,40,41]. These measures of species diversity have their limitations. Species richness treats all species as if they are equally abundant, and other single indicators may be more sensitive to abundant or rare species [42]. A more comprehensive view and comparison of the species diversity of weed communities can be obtained using index families, such as Renyi diversity profiles [39,42]. According to this concept, a community can be regarded as more diverse if its diversity at all values of the scaling parameter (α) is higher than those of other communities. An important limitation of the species-based approach to weed diversity is that it treats all species found in the community equivalently, no matter their taxonomic affinity (taxonomic relationships among species) or role in the ecosystem [43]. The latter two aspects fall under the taxonomic and functional dimensions of biodiversity, respectively. Taxonomic diversity refers to the representation of lower-rank taxa within higher-rank taxa, and it can be a measure of the stress exerted on weed communities by various environmental factors since the taxonomic spectrum of organism communities is often reduced when disturbances occur [44]. At the extreme, weed communities may contain only closely related species, even species of the same genus [44]. Functional diversity is understood through the concept of ecological niche and is based on the organisms’ traits (morphological, anatomical, physiological, reproductive, or behavioral) that directly or indirectly affect the functioning of the ecosystem [45]. Species with a similar impact on a particular process (or more than one process) in an ecosystem (effect traits) or those that present similar responses to environmental factors (response traits) are grouped into functional groups [46]. Making an informed choice of traits based on ecological functions is crucial. The functional structure of the weed community governs the processes of competition for resources or complementarity in their use in space and time [34]. Competitive weeds have attributes related to rapid resource acquisition (i.e., high seed mass, high height, high specific leaf area, high leaf nitrogen content, and the same phenology as the crop). When the weed community occupies the same niche as the crop, weed–crop competition is the most intense. A higher level of functional diversity within the weed community is expected to promote complementarity in resource utilization, thereby reducing the probability of intensive niche overlap with the crop and yield losses due to dominant and competitive weeds. The shift in focus from weed taxonomy to function has yielded successful predictions of competitive outcomes; hence, a functional group approach rather than a single-species approach has been recommended in designing weed regulation [47,48].

Modern, sustainable weed management strategies are expected to minimize weed competitiveness while promoting weed diversity, i.e., keeping beneficial species at acceptable levels [48,49]. The role of DCR in weed control is evident [24,50]. However, DCR alone may not be sufficient to achieve optimal weed control, and other practices may need to be used in conjunction, such as applying herbicides or introducing more competitive crop genotypes [29].

Previous long-term studies by Zawiślak [20] proved the redundancy of herbicide use in the cultivation of winter rye cultivation under DCR. A strong root system of rye, capable of intensive water and nutrient uptake followed by a fast-growing and rapidly closing canopy, made this agrophytocenosis unfavorable for weeds. Thus, there was no yield threat from weed competition. In addition, the application of a herbicide under these conditions resulted in a reduction in rye yield, demonstrating the phytotoxic effect of the herbicide. In turn, according to a previous study by Jastrzębska et al. [51], DCR was sufficient to provide a satisfactory winter rye yield and reduce the abundance of weeds without decreases in weed species diversity or functional diversity. Moreover, when rye was grown under DCR conditions, the application of a herbicide did not increase rye yield, but it was harmful to weed biodiversity. Since the growth behavior and competitive ability of some triticale cultivars are similar to those of rye [19,21,22], it can be assumed that there is no need for herbicides when growing triticale in a DCR system, especially if the appropriate cultivar is selected for cultivation. This work focused on determining (1) whether herbicide application in triticale grown in a DCR system will result in an increase in yield due to weed biomass reduction or whether its application will no longer be necessary, (2) how the herbicide will affect the species, taxonomic, and functional diversities of weeds, and (3) how the choice of cultivar is important in the above issues. It was expected that growing a high-yield and competitive cultivar of triticale in a DCR system without herbicide support would maintain the weed biomass at a low level that would not compromise the triticale yield and weed diversity. In addition, the relationships between weed diversity, weed biomass, and triticale yield were investigated.

2. Materials and Methods

2.1. Experimental Site Description

This experiment was established on the fields of the Production and Experimental Plant “Bałcyny” Sp. z o.o. (Bałcyny, northeastern Poland, 53.60° N, 19.85° E, 136.9 m above sea level) in 1967. The site is characterized by slight undulations of post-glacial origin and Luvisols-type soil formed from silty light clay. The particle size distribution of the soil in the 0–30 cm layer was as follows: 26–39% silt (0.1–0.02 mm), 17–22% floatable particles (0.02–0.002 mm), and 2–4% clay particles (<0.002 mm) [52]. The climate is characterized by highly variable weather. The average annual total precipitation is 614.6 mm, and the precipitation is evenly distributed over the year. Short-term dry spells appear irregularly (mostly in July and August) [53] and are unfavorable for cereal plant development. The average annual air temperature is 8.1 °C; July is typically the warmest month (18.5 °C) and January the coldest (−2.2 °C). The weather conditions in the research growing periods are presented in Table S1. The estimated average length of the growing season is 215 days, and the number of frost-free days is 170 [54].

The original and overarching aim of the experiment in Bałcyny was to explore the response of selected crops to continuous cropping (CC; growing of the same type of crops on the same piece of land every year), which was soon contrasted with growing those crops in multi-field DCRs (see [51] for more information). Currently, the experiment is observing twelve crops in CC and in two six-field DCRs (a total of twenty-four fields, two for each crop species: one for CC of the crop and one for growing in a DCR; the total area of the experiment is about 1 ha). In addition, over the more than 50 years of the experiment, other factors were also tested. Since 1983, crop protection (at three levels: no protection, herbicide, and herbicide + fungicide) and cultivars (two for each crop) have been tested as additional factors in each field of crop rotation and continuous cropping. The arrangement of plant protection levels and cultivars in a single field of crop rotation or continuous cropping was established as is shown in Figure S1, and this pattern was constant each year (since 1983). The use of herbicides and fungicides has been regularly updated in compliance with the recommendations of the Institute for Plant Protection—National Research Institute in Poznań. Within each crop species, cultivars recommended for the region and contrasted in yield potential, competitiveness against weeds, and resistance to pathogens were selected for cultivation (according to Research Centre for Cultivar Testing (COBORU) in Słupia Wielka). At the end of each rotation cycle, the cultivars were changed. Winter triticale was included in the experiment in 1993.

2.2. Experimental Design and Agronomic Management

The subject of the present study was part of the experiment described in Section 2.1, namely, the winter triticale grown since 1993 in the following six-field DCR: potato–oat–fiber flax–winter rye–faba bean–winter triticale (each year, all of the crops were present in separate fields). Data from 2017–2021 (five growing seasons: 2016/2017, 2017/2018, 2018/2019, 2019/2020, and 2020/2021), were taken for analysis. In each year (season) studied, triticale was grown in a different field according to the rotation pattern. Two experimental factors were tested: (1) a weed management strategy (DCR strategy—diversified crop rotation without any other weed control treatments; DCR + H strategy—a diversified crop rotation in which herbicide was additionally applied) and (2) the cultivar of triticale (Trapero, Borowik). The herbicide treatments used in this study period are described in

Table 1. Herbicide treatments applied to winter triticale in the growing seasons under study.

Season	Trade Name	Active Ingredient (g dm−3)	Dose, dm3 ha−1	Application Time
2016/2017	Trinity 590 SC 1	chlortoluron (250) + diflufenican (40) + pendimethalin (300)	2.0	autumn (27 October 2016), BBCH 11 [55]
2017/2018	Trinity 590 SC	chlortoluron (250) + diflufenican (40) + pendimethalin (300)	2.0	autumn (7 November 2017), BBCH 10
2018/2019	Trinity 590 SC	chlortoluron (250) + diflufenican (40) + pendimethalin (300)	2.0	autumn (10 October 2018), BBCH 21
2019/2020	Bizon 2	diflufenican (100) + florasulam (3.75) + penoxsulam (15)	1.0	autumn (14 October 2019), BBCH 11
2020/2021	Bizon	diflufenican (100) + florasulam (3.75) + penoxsulam (15)	1.0	autumn (15 October 2020), BBCH 11

¹ Manufacturer: ADAMA Polska, Warsaw, Poland; ² manufacturer: Corteva Agriscience Poland, Warsaw, Poland.

, and the major cultivar characteristics are presented in

Table 2. Major characteristics of winter triticale cultivars used in the experiment [56].

Characteristics	Unit	Trapero	Borowik
Breeder/owner		DANKO Hodowla Roślin sp. z o.o., Choryń, Poland	Hodowla Roślin Strzelce sp. z o.o. Grupa IHAR, Strzelce, Poland
Entry into The Polish National List of Agricultural Plant Varieties	year	2015	2011
Plant height	cm	114	128
From 1.01 to heading	days	141	140
From 1.01 to maturation	days	197	198
Winter hardiness	9-point scale	6	5
Resistance to lodging	9-point scale	7.1	7.7
Weight of 1000 grains	g	40.6	48.6
Yield potential 1	dt ha−1	88.9	89.8

¹ Compared to the yields of control cultivars under high levels of inputs (increased nitrogen fertilization, foliar multi-nutrient preparations, and protection against lodging and diseases) in 2017–2019.

. The cultivars selected for the study differed in traits such as the number of tillers per plant, leaf size and angle, growth rate after restarting vegetation in the spring, and plant height, upon which their competitive abilities against weeds may depend. In each growing season, the present study encompassed four treatment combinations (2 weed management strategies × 2 cultivars). Each combination was represented by three plots (replications). The arrangement of the tested factors on the winter triticale field is presented in Figure S2. The area of a single experimental plot was 27 m².

Tillage was performed using the plowing system. Farmyard manure (30 t ha⁻¹) was applied to the crop rotation every six years in autumn, before potato planting. In addition, mineral fertilizers were applied annually. Nitrogen (N), phosphorus (P), and potassium (K) fertilizers were applied to the triticale in the following amounts and forms: 70 kg ha⁻¹ of N (ammonium nitrate, 34% N, Zakłady Azotowe Anwil, Włocławek, Poland), 30.5 kg ha⁻¹ of P (Super fos dar 40, 40% P₂O₅, Grupa Azoty, Puławy, Poland), and 83 kg ha⁻¹ of K (AurePio-Kalij, 60% K₂O, Aurepio-Kalij Sp. z o.o., Warsaw, Poland). The entire P and K doses were applied prior to sowing, and the N dose was divided into two parts: 50 kg was applied at the start of the post-winter vegetation growth and 20 kg was applied at the stem elongation stage of the triticale. The triticale was sown at a depth of 3–4 cm, a spacing of 10.7 cm, and in an amount that ensured a density of 400 plants per 1 m². The sowing and harvest dates are shown in

Table 3. Sowing and harvest dates for winter triticale (both cultivars) in the growing seasons under study.

Season	Sowing Date	Harvest Date
2016/2017	19 September	4 August
2017/2018	2 October	23 July
2018/2019	13 September	26 July
2019/2020	12 September	2 August
2020/2021	14 September	30 July

. The triticale was harvested with a plot combine harvester (Wintersteiger CLASSIC, Ried im Innkreis, Austria). After the harvest, straw was removed from the field.

2.3. Data Collection

2.3.1. Grain Yield and Yield Components

The triticale grain yield was evaluated based on the amounts of grain harvested in individual plots. Triticale grains were harvested separately from each plot and then weighed. The results were converted and expressed in terms of 1 hectare and a grain moisture content of 15%. Yield components were determined as follows: spike density, i.e., the density of productive tillers per 1 m², determined before triticale harvest, using the frame method (four 0.50 m × 0.50 m quadrats were randomly designated in each plot for manual spike counting, and the values in the quadrats were summed); the grain number per spike, which was based on the measurements of 20 plants sampled from each plot shortly before harvest; and the weight of 1000 grains (TGW), which was based on grain samples (one sample of about 1 kg from each plot) obtained during combine harvesting (1000 grains were randomly picked from each sample and weighed).

2.3.2. Weed Species and Biomass

Weeds were sampled from randomly determined areas of 1 m² from each plot before the winter triticale was harvested. In each sample, all weed species were identified and separated. Then, the weed roots were separated from the above-ground parts and removed. Finally, the above-ground biomass of each weed species was dried at room temperature for several days and weighed separately.

2.3.3. Weed Diversity

Weed diversity was assessed based on the weed samples taken from individual plots. The assessment included species, taxonomic, and functional dimensions of biodiversity.

The weeds’ species diversity was expressed using Renyi diversity profiles ($H_{\propto }$) [39], which summarize several diversity indexes (including species richness, Shannon–Wiener index, Simpson index, and the Berger–Parker index) by an order of the parameter alpha (α) ranging from zero (0) to infinity (+$\infty$) and determining the sensitivity of the index to the relative abundance of species. By plotting the diversity profiles for two or more communities, it is possible to determine whether one community is consistently more diverse than another, i.e., at all parameter levels. Diversity profiles were calculated for treatment combinations after compiling weed data from three (for individual years) or fifteen (for the 5-year period) replication plots. In addition, species richness (the number of species, S) and the Shannon–Wiener index (H′) [40] were also determined for individual replication plots.

To illustrate the taxonomic and functional diversity of weeds, taxonomic distinctness indexes (Δ⁺) by Warwick and Clarke [44] and Rao’s quadratic entropy (Q) [57], respectively, were calculated. For the taxonomic distinctness index, six different “distinctness weights” (1, 2, 3, 4, 5, and 6) were adopted when comparing each pair of species and their affiliation to the same genus, family, order, class, division, and kingdom, respectively. The basic taxonomic categories in the plant kingdom and the systematic positions of individual species were provided according to Mirek et al. [58] (see also Table S2). To determine Rao’s quadratic entropy (Q), the dissimilarity between each pair of species was measured by the Euclidean distance based on six functional traits that relate to competitiveness: maximum plant height [59,60], growth habit [59], specific leaf area (SLA) [59], season of emergence [60], seed number [59], and ecological trophic indicator value (Tr) [61,62] (see Table S3 for trait characteristics and rationale, and see Table S4 for the list of weed species and their functional trait values adopted to calculate the functional diversity index). It should be noted that Tr is not a real plant trait but a numerical indicator that expresses plant preferences for soil nutrient abundance and can represent the trophic requirements of a species. The Δ⁺ and Q indexes were computed for individual replication plots.

The calculations of the Euclidean distance based on chosen functional traits were supported by Statistica software [63], and other calculations were performed using Microsoft Excel [64]. Formulas for diversity indexes and their explanations are provided in the Supporting Information.

2.4. Statistical Analysis

The data were submitted to a three-way variance analysis (ANOVA) for the experiment in a completely randomized design. The effects of the weed management strategy, cultivar, year, and their interactions on triticale yield, yield components, weed biomass, and weed diversity indexes (S, H′, Δ⁺, Q) were analyzed. The Shapiro–Wilk W-test was used to assess variable distribution normality, and Levene’s test was used to verify variance homogeneity. To homogenize variances, weed biomass, H′, Δ⁺, and Q values were transformed using natural logarithms (ln(x + 1)). Other variables were not transformed. The differences between means were evaluated using Duncan’s test. The relationships between the variables were expressed using Pearson simple correlation coefficients. When variables were transformed, the transformed data were used for correlation. The calculations were supported by Statistica software [63].

3. Results

3.1. Triticale Yield and Yield Components

The triticale yield was significantly influenced by the weed management strategy (WMS) used, cultivar (CV), and year (YR), as well as by the interactions between WMS × CV and WMS × YR (

Table 4. Analysis of variance (F values and degrees of freedom, df) for triticale yield and yield components.

Source of Variation	df	Yield	Spike Density (SD)	Grain Number per Spike (GN)	1000-Grain Weight (TGW)
Weed management strategy (WMS)	1	54.37 ***	11.41 **	10.59 **	19.28 ***
Cultivar (CV)	1	65.88 ***	42.74 ***	158.78 ***	697.67 ***
Year (YR)	4	45.10 ***	16.22 ***	31.04 ***	43.26 ***
WMS × CV	1	7.96 **	4.42 *	0.17	0.00
WMS × YR	4	8.04 ***	1.50	1.97	1.34
CV × YR	4	2.12	5.77 ***	2.07	17.19 ***
WMS × CV × YR	4	1.51	0.77	2.82 *	3.48 *

* p < 0.05, ** p < 0.01, *** p < 0.001.

). The main sources of variation tested (WMS, CV, and YR) also affected all yield components, i.e., spike density (SD), grain number per spike (GN), and 1000-grain weight (TGW), with CV being the strongest factor, followed by YR and WMS. In addition, SD was differentiated by the interactions WMS × CV and CV × YR, GN by the interaction WMS × CV × YR, and TGW by the interactions CV × YR and WMS × CV × YR.

The DCR + H strategy resulted in a higher triticale yield in comparison to the DCR strategy regardless of the cultivar and year (

Table 5. Effects of weed management strategy, cultivar, and year on triticale yield and yield components (means and standard errors).

Source of Variation	Yield, t ha−1	Spike Density, No. m−2	Grain Number per Spike, No.	1000-Grain Weight, g
Weed management strategy (WMS)
DCR	5.85 ± 0.23 b 1	415 ± 16 b	34.4 ± 1.31 b	45.6 ± 0.89 a
DCR + H	6.67 ± 0.14 a	459 ± 17 a	36.9 ± 1.39 a	44.3 ± 0.90 b
Cultivar (CV)
Trapero	5.81 ± 0.20 b	480 ± 20 a	30.7 ± 1.02 b	40.9 ± 0.46 b
Borowik	6.71 ± 0.17 a	394 ± 8 b	40.5 ± 1.03 a	49.0 ± 0.55 a
Year (YR)
2017	6.21 ± 0.20 b	449 ± 19 b	32.5 ± 1.23 c	46.9 ± 1.38 a
2018	6.19 ± 0.33 b	393 ± 15 c	39.2 ± 2.12 b	47.2 ± 1.68 a
2019	7.45 ± 0.13 a	514 ± 32 a	31.3 ± 1.62 c	41.9 ± 1.22 d
2020	6.36 ± 0.21 b	465 ± 27 b	32.8 ± 2.15 c	45.0 ± 0.64 b
2021	5.09 ± 0.30 c	365 ± 15 c	42.3 ± 1.74 a	43.8 ± 1.51 c

¹ Different letters indicate significant differences at p < 0.05.

). In general, this was due to higher SD and GN values. However, the DCR + H strategy reduced the TGW. During the five years of the study, twice, i.e., in 2017 and 2019, the triticale yield was not affected by the WMS (Figure 1). The Borowik cultivar produced a higher yield than the Trapero cultivar (Table 5). Borowik showed lower SD values than Trapero but higher GN and TGW values.

The higher-yielding cultivar Borowik responded to the application of a herbicide (DCR + H vs. DCR strategy) with a smaller yield increase (+0.50 t ha⁻¹) than the cultivar Trapero (+1.13 t ha⁻¹) (Figure 2a). Under the DCR strategy, the Borowik cultivar provided the same statistical yield as the Trapero cultivar under the DCR + H strategy. When the DCR + H strategy was used, the Trapero cultivar developed more productive tillers (spikes) than under the DCR strategy, while the WMS did not affect the SD of the Borowik cultivar (Figure 2b).

Of the five years of the study, the years 2019 and 2021 proved to be the most and least favorable, respectively, for the productivity of both cultivars, regardless of the WMS (Table 5). In 2019, the highest SD and the lowest GN and TGW values were observed. In turn, in 2021, the lowest SD and GN per spike values were recorded.

The spike density (SD) of the Trapero cultivar showed significant variation over the years, unlike the Borowik cultivar, for which this trait was stable over time (Figure 3a). Notwithstanding the higher TGW of the Borowik cultivar, the differences between cultivars in this trait varied over the years, with the largest in 2018 and the smallest in 2020 (Figure 3b). Details of the effects of the interaction WMS × CV × YR on the GN and TGW are shown in Figure S3.

There was a strong positive correlation of yield with GN and TGW in 2017 and 2019 and with GN in 2018 (

Table 6. Relationship between grain yield and yield components—Pearson simple correlation coefficients.

Yield Component	2017	2018	2019	2020	2021	2017–2021
Spike density	−0.660 1	ns	−0.827	ns	0.641	0.304
Grain number per spike	0.863	0.754	0.640	ns	ns	ns
1000-grain weight	0.858	ns	0.879	ns	ns	ns

¹ Values significant at p < 0.05, ns—no significance at p < 0.05.

). Furthermore, a negative correlation between yield and SD was confirmed in 2017 and 2019, and a positive correlation was confirmed in 2021. However, for the entire study period (2017–2021), only a positive correlation between yield and SD was significant.

3.2. Weed Biomass

The weed biomass was strongly influenced by the WMS, YR, and the interactions WMS × CV and WMS × YR (

Table 7. Analysis of variance (F values and degrees of freedom, df) for weed biomass, species richness (S), and indexes of species diversity (H′), taxonomic distinctness (Δ⁺), and functional diversity (Q) of weed communities in winter triticale fields.

Source of Variation	df	Biomass 1	S	H′ 1	Δ+ 1	Q 1
Weed management strategy (WMS)	1	119.208 ***	155.042 ***	87.567 ***	35.254 ***	39.069 ***
Cultivar (CV)	1	1.904	3.375	0.864	1.030	0.010
Year (YR)	4	5.422 **	13.681 ***	2.998 *	5.198 **	3.092 *
WMS × CV	1	5.815 *	1.671	0.001	0.122	1.521
WMS × YR	4	5.959 ***	7.310 ***	2.365	5.022 **	0.625
CV × YR	4	0.193	0.111	0.344	0.827	0.220
WMS × CV × YR	4	0.164	0.167	0.467	0.663	0.989

¹ Transformed variable; * p < 0.05, ** p < 0.01, *** p < 0.001.

). There was no effect of CV on weed biomass.

The DCR + H strategy was more effective in reducing the weed biomass than the DCR strategy (

Table 8. Effects of weed management strategy, cultivar, and year on weed biomass, species richness (S), and indexes of species diversity (H′), taxonomic distinctness (Δ⁺), and functional diversity (Q) of weed communities in winter triticale fields (means and standard errors).

Source of Variation	Biomass 1	S	H′  1	Δ+ 1	Q 1
Weed management strategy (WMS)
DCR	4.07 ± 0.26 a 2	7.97 ± 0.69 a	0.75 ± 0.04 a	1.70 ± 0.02 a	1.31 ± 0.09 a
DCR + H	1.10 ± 0.23 b	1.87 ± 0.25 b	0.22 ± 0.05 b	0.92 ± 0.16 b	0.47 ± 0.10 b
Cultivar (CV)
Trapero	2.77 ± 0.41 a	5.37 ± 0.80 a	0.51 ± 0.06 a	1.38 ± 0.13 a	0.90 ± 0.11 a
Borowik	2.40 ± 0.32 a	4.47 ± 0.72 a	0.46 ± 0.07 a	1.24 ± 0.14 a	0.88 ± 0.13 a
Year (YR)
2017	2.88 ± 0.40 ab	3.83 ± 0.71 cd	0.47 ± 0.10 ab	1.45 ± 0.20 ab	0.90 ± 0.19 ab
2018	2.70 ± 0.72 ab	2.67 ± 0.67 d	0.36 ± 0.11 b	0.84 ± 0.25 c	0.47 ± 0.16 b
2019	2.23 ± 0.52 bc	4.33 ± 0.94 bc	0.45 ± 0.08 b	1.46 ± 0.20 ab	1.05 ± 0.19 a
2020	1.62 ± 0.49 c	5.83 ± 1.50 b	0.50 ± 0.12 ab	1.11 ± 0.24 bc	0.86 ± 0.20 ab
2021	3.51 ± 0.65 a	7.92 ± 1.48 a	0.66 ± 0.10 a	1.69 ± 0.05 a	1.16 ± 0.21 a

¹ Transformed data; ² different letters indicate significant differences at p < 0.05.

). Under the DCR strategy, the Borowik cultivar proved to be more competitive with weeds (less weed biomass) than the Trapero cultivar (Figure 4). In contrast, no differences between cultivars were observed under the DCR + H strategy when the weed biomass was strongly reduced by the herbicide. Differences between the DCR and DCR + H strategies were consistent from 2018 to 2021 with the exception of 2017, when the weed biomass was not differentiated by the WMS (Figure 5a).

3.3. Weed Diversity

A total of 32 weed species were identified throughout the study period. They were hierarchically classified into 30 genera, 16 families, 14 orders, 3 classes and 2 divisions (Table S2). Between 0 and 15 species appeared in individual plots, while between 2 and 21 species were recorded for the experimental combinations. It should be added that there was a great differentiation in the composition and structure of the weed species between years and combinations (Table S5). Weed species communities that form in cereals grown in DCRs tend to be less stable over time and less related to the biology of the crop species and the associated agronomic treatments but more driven by other factors (weather conditions, cultivar competitiveness, weed sensitivity to herbicides, and others) [41,50,65].

Compared with the DCR strategy, the DCR + H strategy typically reduced weed species diversity at all α levels in the fields of both varieties (their curves do not intersect) (Figure 6). Exceptions to this pattern were recorded in 2017 for the Borowik cultivar and in 2019 for the Trapero cultivar, when the biodiversity profiles under the DCR and DCR + H strategies intersected. In both aforementioned cases, the weed communities under the DCR strategy had more species but a more unbalanced distribution of biomass among species. Therefore, diversity under the conditions of the DCR strategy was higher only for rare species (0 < α < 0.6 for Borowik and 0 < α < 0.8 for Trapero), but for more abundant species, it was higher under the DCR + H strategy.

Under the DCR strategy, only in 2018 were the weed communities in the Trapero fields consistently more diverse than in the Borowik fields. In other years, the communities in the fields of the two cultivars were non-separable, with different arrangements of diversity profiles in individual seasons. Under the DCR + H strategy, weed communities were consistently more diverse in the Borowik cultivar fields in 2017 and in the Trapero cultivar fields in 2019, 2020, and 2021.

From the perspective of the 5-year study period, the use of DCR + H strategies in weed control resulted in a consistent reduction in the weed species diversity in the fields of both triticale cultivars when compared to the DCR strategy. Under both weed control strategies with the same (DCR) or higher species richness (DCR + H), the weed communities in the Trapero fields showed a more uneven distribution of biomass between species.

The weed management strategy (WMS) and year (YR) had a significant effect on all measures of weed diversity subjected to a variance analysis (S, H′, Δ⁺, Q), while the cultivar (CV) did not differentiate any of them (Table 7). In addition, S and Δ⁺ were affected by the interaction WMS × YR.

Compared to the DCR strategy, the DCR + H strategy decreased the S, H′, Δ⁺, and Q indexes (Table 8). The year 2021 turned out to be the most favorable for weed diversity, while the lowest values of all the indexes were recorded in the year 2018. For the DCR strategy, higher S values were observed in 2020 and 2021 compared to earlier years, while for the DCR + H strategy, only the difference between 2018 and 2021 was confirmed (Figure 5b). The differences in S between the DCR and DRC + H strategies gradually increased from 2017 to 2020. Reductions in Δ⁺ under the DCR + H strategy were confirmed in 2018 and 2020 but proved to be insignificant in 2017, 2019, and 2021 (Figure 5c).

3.4. Relationship between Yield, Weed Biomass, and Weed Diversity

A negative correlation between triticale yield and the weed biomass and weed diversity indexes was found in 2018 (except for Q), 2020, and 2021 (except for Δ⁺ and Q) and for the period 2017–2021, while no relationship between these variables was observed in 2017 and 2019 (

Table 9. Relationship winter triticale yield and weed biomass and diversity—Pearson simple correlation coefficients.

Weed Community Trait	2017	2018	2019	2020	2021	2017–2021	DCR	DCR + H
Biomass 1	ns	−0.582 2	ns	−0.800	−0.594	−0.512	−0.557	ns
S	ns	−0.700	ns	−0.758	−0.729	−0.547	−0.484	ns
H′ 1	ns	−0.594	ns	−0.780	−0.584	−0.466	−0.480	ns
Δ+ 1	ns	−0.593	ns	−0.633	ns	−0.296	ns	ns
Q 1	ns	ns	ns	−0.605	ns	−0.260	ns	ns

¹ Transformed variable; ² values significant at p < 0.05; ns—no significance at p < 0.05.

). Considering the two strategies separately, only under the DCR strategy was a negative correlation between the triticale yield and weed biomass and weed species diversity (S and H′) confirmed. Under the DCR + H strategy, no correlation was found.

In 2018, 2020, and for the whole period of 2017–2021, a positive correlation was confirmed between weed biomass and all weed diversity indexes studied (

Table 10. Relationship between weed biomass and weed diversity—Pearson simple correlation coefficients.

Biodiversity Index	2017	2018	2019	2020	2021	2017–2021	DCR	DCR + H
S	0.593 2	0.927	0.915	0.950	0.915	0.742	0.384	0.698
H′ 1	ns	0.973	0.789	0.827	0.649	0.682	ns	0.365
Δ+ 1	ns	0.973	ns	0.722	ns	0.606	ns	0.530
Q 1	ns	0.880	ns	0.699	ns	0.498	ns	ns

¹ Transformed variable; ² values significant at p < 0.05; ns—no significance at p < 0.05.

). In contrast, in 2017, 2019, and 2021, only the relationship between weed biomass and weed species diversity (S and/or H′) was significant. When considering the two strategies separately, there was only a positive relationship between weed biomass and S under the DCR strategy, while under the DCR + H strategy, weed biomass was also positively correlated with H′ and Δ⁺.

4. Discussion

Regardless of the cultivar and year, the DCR + H strategy resulted in a higher triticale yield compared to the DCR strategy, a difference that was attributed to its effective weed biomass reduction. At the same time, however, the DCR + H strategy reduced weed diversity.

The superior efficacy of the DCR + H strategy over DCR in reducing weed biomass was expected, given the results of previous studies [51,66]. Although it was assumed that there would be no increase in triticale yield due to herbicide application, the observed increase is also not surprising considering the studies by other authors [67,68,69]. The higher yield achieved under the DCR + H strategy was associated with increases in the spike density and grain number per spike, indicating an increase in resource availability to the triticale crop once weed competition was reduced by the herbicide [48]. Other authors have also found an increase in 1000-grain weight under such conditions [67]. However, in the present study, the opposite effect was observed, which may be explained by compensation mechanisms between the different yield components. Often, yield components are negatively correlated, with a higher spike density resulting in fewer and/or smaller grains per individual spike [70]. This is the response of plants to light conditions modified by the spike density.

Over the years, the Borowik cultivar consistently showed a higher yield than the Trapero cultivar, regardless of the weed management strategy used. The generally lower number of productive tillers of the former was effectively compensated for by the higher grain number per spike and the greater 1000-grain weight. Genetic differences among triticale cultivars with respect to yield components are well documented in the literature [18,69,70,71,72,73,74], and the Borowik cultivar has been noted for its particularly high 1000-grain weight [18].

The yield response of triticale to the weed management strategy was cultivar-dependent. This was related to the different competitiveness of the cultivars grown against weeds observed under the conditions of the DCR strategy. The competitive ability of winter triticale against weeds is a cultivar attribute. Tall cultivars with better winter hardiness and higher tillering coefficients are considered to have competitive abilities comparable to those of rye and compete well with both monocotyledonous and dicotyledonous weeds [21,75]. In the present study, the greater competitiveness of the Borowik cultivar against weeds in a DCR system strategy was explained by its faster growth rate after the spring vegetation restart than that of the Trapero cultivar, as well as by its larger leaves and longer tillers, although they are slightly less numerous. Early season crop ground cover confers later competitiveness against weeds [76]. The high competitive ability of the Borowik cultivar under organic cropping conditions was confirmed by the study of Feledyn-Szewczyk et al. [18]. When weeds are controlled with herbicides, differences in weed biomass between cultivars or years tend to blur [77]. Such an effect was noted in the present study. The less-competitive and lower-yielding cultivar, Trapero, responded to the reduction in weed biomass caused by the herbicide (DCR + H strategy) with a greater increase in its yield. Most of all, the reduction in weed competition allowed this cultivar to produce more spikes, which was not observed in the case of the Borowik cultivar. However, the yield of Trapero in a DCR + H system barely matched that of Borowik in a DCR system.

The inter-annual variability peculiarly determined the triticale yield and weed biomass and interacted with weed management strategy in shaping these variables.

The reduction in weed biomass under the conditions of the DCR + H strategy was consistent across years with the exception of 2017, when there were no differences between the DCR and DCR + H strategies. It was the first year of a new crop rotation cycle and the growth of the Trapero and Borowik cultivars. Cultivar rotation has been previously suggested as a tool for weed control strategies in crop fields [78]. Moreover, in 2017, the herbicide proved to be less effective in controlling Agropyron repens, and by limiting other weed species, it provided more space for the latter’s biomass to develop. The lack of a difference in triticale yield between the DCR and DCR + H strategies this year seems to be a natural consequence of the statistically equal weed biomass.

Regardless of the weed management strategy, 2019 proved to be the most favorable year for productivity for both cultivars over the five-year study period. This year, after a February and March that were much warmer than usual (Table S1), the vegetation started unusually early, and triticale took advantage of these conditions for intensive tillering [79]. A high spike density was the most important yield-determining component in 2019. The grain number per spike and 1000-grain weight, although they demonstrated the lowest values of the five years, were high enough to not contribute to a reduction in the yield of triticale. An early spring restart and increased tillering allowed the triticale to escape weed competition under the DCR strategy. Although the higher spike density in 2019 was followed by a significant reduction in grain numbers per spike and the 1000-grain weight, the triticale yield under the DCR strategy was not lower than the triticale yield under the DCR + H strategy that year. On the other hand, Brzozowska and Brzozowski [67] claim that in years characterized by an even distribution of precipitation, the competitiveness of triticale against weeds is enhanced, and the effect of herbicide application becomes insignificant. It is noteworthy that in 2020, after a warm winter and an early spring, the yield of triticale under the DCR strategy decreased significantly, although the weed biomass was not higher than in 2019. This can be explained by a more complementary resource use by crops and weeds in 2019 but a more intense niche overlap and competition in 2020 [34]. The Trapero cultivar made better use of the early spring in 2019 and 2020 for tillering than the Borowik cultivar, but due to its generally lower grain number per spike and 1000-grain weight, it did not match the productivity of the Borowik cultivar, regardless of the weed management strategy. A late spring in 2021 worsened triticale tillering, especially in the Trapero cultivar, which weeds took advantage of by building up a large biomass under the conditions of the DCR strategy. A rather wet July was not favorable for grain filling by triticale. A low spike density and relatively low 1000-grain weight in 2021 contributed to the low yield of the triticale (the lowest of the study period), while the yield increase due to the use of a herbicide (DCR + H) in that year was the highest of the study period.

The contribution of the individual yield components to the yield volume varied across years; however, for the entire study period (2017–2021), only a positive correlation between yield and spike density was confirmed. Other authors have also pointed to spike density as the most important component of triticale yield [67]. However, the present study and other studies show that it is difficult to establish a strict ranking of the individual components of triticale yield. This is because they are determined by both genetic factors (cultivar) and environmental conditions [67].

The reduction in weed species diversity under the DCR + H strategy is an obvious result of the long-term use of herbicides not only in triticale but also in any crop previously grown on a given plot according to the rotation cycle pattern. Each year, species sensitive to a particular herbicide have been eliminated or prevented from producing seeds. At the same time, the soil seed bank has been depleted [80]. This has promoted a reduction in species composition and changes in weed community structures in herbicide-treated fields. The decline in weed species diversity as a result of herbicide use in conventional crop rotations has been confirmed by many other studies, which measured diversity using species richness or the Shannon–Wiener index [41,51,81,82]. Renyi profiles are considered to provide a more comprehensive picture of species diversity than individual measures (indexes) [42]; however, they are not yet used very often in comparing weed communities [38,83,84]. In the present study, Renyi profiles show that abandoning chemical weed control in triticale (DCR strategy) results in a consistently higher weed species diversity (at all values of the scaling parameter) or at least a higher number of species among which functionally valuable species can be found. In an earlier study involving this family of indexes, Jastrzębska et al. [38] also proved a consistently higher weed diversity was found in triticale grown in a Norfolk-type rotation without herbicides (organic farming) than in the same rotation with herbicide application (integrated farming).

The declines observed in the taxonomic and functional diversities of weed communities were the results of either the complete eradication of weeds in individual plots by the herbicide or a reduction in the number of species to one or a small group of taxonomically or functionally close species. Such a direction of weed structure simplification under chemical control has been noted in the literature [50,85]. The simplification of weed community structure has often been linked to emerging herbicide resistance in weeds, and a decline in weed diversity has been reported from many cropping systems, while weed biomass has not decreased [30]. Such a phenomenon was not observed in the present study as the herbicides were exchanged quite frequently during the experiment.

The number of reports on the impacts of agricultural practices on the taxonomic and functional diversities of weeds has been increasing in recent years [37,48,86,87,88,89]. However, there are still few studies comparable to the present study in terms of the research factors and methods used (e.g., formulas of indexes, weed abundance measures, and selection of functional traits) as various methodological approaches were used by other authors [37,88,89]. A general opinion has been established that agricultural intensification reduces the taxonomic and functional diversities of weed communities [37,90], and herbicide use is among the indicators of this intensification at the individual field scale [37]. The findings from the present study are broadly in line with this opinion, although in some years, the negative effects of herbicides (DCR + H strategy) on taxonomic distinctness were not proven. In a methodologically similar study with winter rye, Jastrzębska et al. [51] found similar differential effects.

In the present study, there was no effect of cultivar on weed species richness (S) and Shannon–Wiener species diversity (H′), and Renyi profiles also show an inconclusive and inconsistent effect of this factor on weed species diversity across the study years. Such findings are not very surprising given the reports of other authors. Wesołowski et al. [91] and Urban et al. [92] showed that the H′ values of weed communities varied depending on the cereal species and cultivar, but the differences were small. In other studies, small differences between cereal cultivars were recorded in weed species richness [91,93]. In the available literature, we failed to find articles on the taxonomic and functional diversities of weeds depending on crop cultivar, leaving room for further study. Currently, it is recommended that the assessment of plant community diversity should be based on a set of indicators that simultaneously capture different components of the multidimensional concept of biodiversity [42]. The present study, which used Renyi profiles and taxonomic and functional diversity indexes to assess weed diversity, addresses this postulate.

Inter-annual variations in triticale yield and weed biomass and diversity and the interaction of these variations with the chosen weed management strategy and/or cultivar may be explained by the fact that many factors play a role in the competitive interactions between crops and weeds, which may vary from year to year [75]. This is also why the observations carried out in long-term field experiments are of great importance. Weather conditions appear to be of primary importance. The amount and pattern of rainfall and temperatures over time not only determine crop development and yield [67] but also strongly influence seed germination and weed plant development [94]. The latter is due to the specific requirements of different weed species and can explain differences in both weed biomass and weed diversity [41,65,94,95].

Weed biomass is the qualitative measure of weed competitiveness, and most of the previous studies relating crop yield and weed biomass showed a negative linear function [96]. In the present study, the negative correlation between triticale yield and weed biomass was also statistically confirmed except in 2017 and 2019. In these two years, competition from weeds proved to be a weak yield-limiting factor. It was particularly surprising in the case of 2019, in which the weed biomass in the Trapero field under the DCR strategy was even higher than in 2020. In addition, Apera spica-venti, a species that is strongly competitive with winter cereals, accounted for 69.4% of the total weed community (Table S5). This demonstrates that understanding the ecological processes that drive weed abundance (density and biomass) and how to influence these factors is still an open challenge in sustainable weed management [48].

In addition to the negative correlation with weed biomass, triticale yield showed a negative correlation with at least one of the weed diversity dimensions. This relationship was particularly evident under the DCR strategy conditions, in which weather conditions played a greater role in determining weed biomass and diversity. The strong reduction in weed biomass by the DCR + H strategy to almost the same level in all years made the relationship between weed biomass and yield insignificant.

The relationship between weed community diversity and crop performance has yet to be fully elucidated [97]. More diverse weed communities are thought to buffer agroecosystems against dominance by one or a few aggressive, resistance-prone species [98]. On the other hand, in diverse communities, there is a greater chance that one or more species would be able to survive a given control strategy and gain a competitive advantage over the crop, lowering its productivity [99]. The present study supports the opinion that high weed abundance provides great opportunities for weed species diversity [100]. Moreover, it seems that it was the link between weed diversity and weed biomass that directed the relationship between weed diversity and triticale yield. Evidence of both a positive [34,101] and negative [102] relationship between crop productivity and weed diversity, as well as a lack of a relationship between these variables [51,103], can be found in the literature. Some authors [97,103] claim that the relationship between weed diversity and crop productivity is a complex phenomenon and cannot be reflected adequately by the common weed crop interference models.

Finally, it should be considered optimistic that under the DCR strategy (without herbicide), the Borowik cultivar limited the weed biomass without reducing weed diversity. Although the yield of the Borowik cultivar under the DCR strategy was lower than under the DCR + H strategy on average for the 5 years of the study, the difference was only 0.5 t ha⁻¹, which represents a loss of 7.2% compared to the yield under the DCR + H strategy. The analogous difference for the Trapero cultivar was 1.13 t/ha, i.e., 17.7%, and the yield of Trapero under the DCR + H strategy did not exceed the yield of Borowik under the DCR strategy. Weed competition yield losses of 2.5–10% are accepted in integrated weed management programs [104]; thus, the loss of 7.2% for Borowik can be considered acceptable. On the positive side, there is less pollution from herbicides and increased biodiversity. These are still not economically quantifiable for the farmer [105]. However, governmental subsidies and other support programs can compensate for the decreases in yields and provide additional incentives to abandon pesticide use [106]. Previous studies have also shown that choosing the right cultivar is one of the key factors in determining triticale productivity [18,68,71,72,107]. The findings from the present and other studies [18,108] prove that the Borowik cultivar demonstrates high yield potential. This potential, in addition to its high level of competitiveness against weeds, predestines this cultivar for organic production in which DCR plays a central role and synthetic herbicides are prohibited [18], or for other crop production systems that rely on a non-chemical approach [109].

5. Conclusions

In general, the DCR strategy proved less effective in protecting triticale yield than DCR + H but provided greater species, taxonomic, and functional diversities of weed communities. The Borowik cultivar produced a higher yield than the Trapero cultivar and responded to herbicide abandonment with a lower yield loss. Under the conditions of the DCR strategy, Borowik was more competitive against weeds without reducing weed diversity. Moreover, the Borowik cultivar provided conditions for a more even distribution of biomass between weed species. The triticale yield correlated negatively with weed biomass and weed diversity, and weed diversity and weed biomass were positively correlated. The heterogeneity in treatment effects on triticale yield, weed biomass, and weed diversity across years, as well as in the relationships between these variables, provides room for further research. In particular, research on the relationship between weed community diversity and weather conditions is suggested.

The obtained findings show that winter triticale can be grown in a diversified crop rotation without herbicide application if a cultivar characterized by a high yield potential and a high level of competitiveness against weeds is used. Thus, the diversity of weed communities can be maintained; however, in years with less favorable weather conditions, it may be necessary to accept a moderately lower yield compared to the yield provided by herbicide protection. It is worth considering frequent cultivar replacement (cultivar rotation) to support the yield-forming and yield-protecting potential of crop rotation.