Suitability assessment of different hemp (Cannabis sativa L.) varieties to the cultivation environment

Industrial Crops & Products 143 (2020) 111860

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Suitability assessment of different hemp (Cannabis sativa L.) varieties to the cultivation environment

T

Mario Baldini, Claudio Ferfuia, Fabio Zuliani, Francesco Danuso* Department of Agricultural, Food, Environment and Animal Sciences, University of Udine, Via delle Scienze 206, 33100, Udine, Italy

A R T I C LE I N FO

A B S T R A C T

Keywords: Hemp Seed yield Oil content Phenology Modelling Scenario analysis Irrigation water requirement Crop suitability assessment

Hemp crop is nowadays spreading in many new areas and then the prediction of crop behavior and performance in different specific environments could be of interest. This study presents a methodology to assess hemp suitability to cropping environments by a combined approach of experimental trials and simulation. The experiment of this study involved six hemp varieties, evaluated for dual-purpose production (seed and stem) during two years of trials in North-East Italy. The results were exploited to develop and calibrate a simulation model able to evaluate hemp crop suitability to the trial environment. Yields obtained for hemp biomass and stems were similar to that of other European experiments, while seed production was shown to be slightly lower. Excessive temperature (daily maximum temperatures over 30 °C) during the grain-filling phase would be one of the main factors affecting seed quality, limiting the seed oil accumulation. Parameters already available in the literature and data obtained from the present experiment were used in the modelling approach to estimate phenological parameters, seed production as affected by water stress, and seed oil content as a function of temperature during the grain-filling period. In order to evaluate the hemp crop suitability to the environment, a scenario analysis using historical meteorological data was performed to predict variability - with different irrigation regimes - of seed yield, seed oil content, maturity date and required seasonal irrigation volume, for each variety in the soil and climatic conditions of the trial site.

1. Introduction Hemp (Cannabis sativa L.) is cultivated worldwide and is one of the oldest plant sources for food, textile fibers, and medicine (Clarke and Merlin, 2016). In central and southern Europe, breeding aspects and agronomic techniques of hemp cultivation have always traditionally been designed depending on textile and clothing uses. Anyway, during the 20th century, due to the competition from other increasingly profitable feedstocks such as cotton and synthetic fibers (Allegret, 2013), hemp cultivation progressively declined, with the exception of France, where production of hemp pulp and paper (mainly supplied by French producers) has allowed a certain constancy of acreage. Harvesting of the whole plant at flowering limits the potential of industrial hemp, which can provide more than 25,000 products (Schluttenhofer and Yuan, 2017) that could be utilized in new applications and emerging markets, improving the environmental and economic sustainability of hemp crop (Schluttenhofer and Yuan, 2017). Today, there is renewed interest in a hemp crop for multipurpose production, and particularly for the combination of fiber and seed, a practice that is now the norm in many European countries (Carus et al., 2013; Tang et al.,

⁎

2016). However, in this case, the harvest at seed maturity provides nonaligned short fibers usable only in non-woven applications as reinforcement in biocomposites (mainly automotive), insulation materials, papers and technical uses. In Europe, the area cultivated with hemp increased from 15,700 ha in 2013 to 33,000 ha in 2016 (Carus, 2017), with a further increase expected, mainly driven by the rising demand for hemp seed, which increased from 6,000 to 11,500 tons between 2010 and 2013 (92% growth), by the food market. In addition, new applications for hemp fibers were developed in the 1990s, such as biocomposites (about 15% of the total hemp fiber produced in the EU is used for automobile biocomposites), insulation material and other nonwoven applications, requiring nonaligned technical short fibers, today virtually the only type obtained from hemp straw processing in Europe. In addition, hemp crop is currently seen as an interesting alternative to traditional food crops used for biofuel yield (bioethanol, biogas, solid fuel, and biodiesel) (Rehman et al., 2013). The cultivation of dualpurpose varieties, with joint seed and fiber production, nowadays a requirement for the hemp farmer, has opened new challenges in many research sectors such as breeding. The number of industrial hemp cultivars registered in Europe has risen from 12 mainly dioecious varieties

Corresponding author. E-mail address: [email protected] (F. Danuso).

https://doi.org/10.1016/j.indcrop.2019.111860 Received 9 April 2019; Received in revised form 8 October 2019; Accepted 10 October 2019 0926-6690/ © 2019 Elsevier B.V. All rights reserved.

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in 1995, suitable for fiber production due to high stem yields and a better fiber quality (Amaducci et al., 2015; Tang et al., 2016) to 69 in 2018 (European Commission, 2018), most of them monoecious, considered particularly suitable for dual-proposal use (Salentijn et al., 2015). Besides traits of hemp cultivars, cropping environments and their interaction with genotypes play a crucial role in determining hemp crop production and sustainability. Information from variety trials in different years are commonly adopted to evaluate the suitability for cultivation in a specific location for a multipurpose crop and combining the variety trial results and literature data could be the best strategy for a simple simulation model approach. Modelling applications on hemp crop are not numerous: after the first implementation of a hemp crop model in the APSIM modelling system of about twenty years ago (Lisson et al., 2000a, 2000b, 2000c), the other more recent modelling applications have only regarded phenology (Amaducci et al., 2008; Cosentino et al., 2012) or specific physiological traits such as photosynthesis and light interception (Tang et al., 2018). Therefore, with the aim of predicting phenology, water requirements, seed and oil yield of hemp crop, in our specific environment, a simple modelling approach was developed, making use of the two years of experimental results for calibration. The model was also based on information from the literature regarding phenological development (Amaducci et al., 2008; Cosentino et al., 2012). This study was part of a regional project aimed to check the industrial hemp chain sustainability, mainly interested in seeds (vegetable oil and protein cake) utilization, activities already practiced at farm level using other crops. The hemp stems could also have a market as bedding, while in the near future there are prospects for the use of fiber and shives in local bio-building activities. For these reasons, this paper had the following aims a) to study the performance of some hemp varieties with different origin, sexual type and crop cycle in an environment over the years; b) to develop a simulation approach predicting crop growth stages, seed and oil yield and water requirements of different hemp varieties, depending on climate variability in North-East Italy.

Table 1 Origin, sexual type (M = monoecious; D = dioecious) and maturity group of the six cultivar tested in two-year field trials experiment. Cultivar

Origin

Sexual type

Maturity group

Bialobrzeskie Chamaeleon CS Ermes Fedora 17 Uso-31

Poland Netherland Italy Italy France Ukraine

M D D M M M

Medium Medium Late Late Early-medium Early

Table 2 Main soil characteristics (0–0.5 m layer). Parameter

Unit

Value

Sand (> 0.05 < 2 mm) Loam (> 0.002 < 0.05 mm) Clay (< 0.002 mm) pH Total CaCO3 Active CaCO3 Organic matter Total nitrogen C/N Phosphorus (Olsen) Potassium (ammonium acetate) Cationic exchange capacity (CEC) Soil depth Gravel content Soil bulk density Wilting point Field capacity

% % % – % % % g kg−1 – ppm ppm meq 100 g−1 mm % t/m3 % w/w % w/w

43 40 17 7.35 5.5 0.2 1.8 1.85 10 34 164 18.2 800 20 1.3 11 26

reported in Table 3. Soil tillage was done in November 2015 and 2016 with 30 cm deep ploughing, and then maintained weed-free by means of disk harrow cultivation, whenever necessary. Wheat was the previous crop in both years and two weeks before sowing the soil was disked twice for seedbed preparation. Sowing was done on May 14 2015 and April 20 2016, by an experimental seeder (Wintersteiger, Austria) in rows 0.15 m apart, using 28 kg ha−1 of seed, corresponding to about 130 viable seeds per m2, with an average sowing depth of 3 cm. The wide difference is sowing time was due to the variability of environmental conditions between years. However, this did not affect results because modelling approach intrinsically takes into account meteorological and phenological variations. Nitrogen fertilization was applied at a rate of 80 kg N ha−1, as urea, 20 days after crop emergence. In order to avoid severe effects of water stress on crops, both experiments were conducted with a deficit irrigation supplement with 5 and 4 sprinkler irrigations in 2015 and 2016, respectively. Irrigation volume was 30 mm, that is the usual amount adopted by farmers in this specific soil and climate conditions. Weed control was not necessary, with the exception of a very limited area where a manual weeding was performed in May 2016, because hemp seedlings, not promptly emerged, had suffered weed competition. The experimental scheme adopted was a randomized blocks design with four replications, with each experimental unit of about 25 m2. Immediately after sowing, a 1.0 m2 area was randomly selected in each unit and the number of plants until full emergence and at harvest time were counted.

2. Materials and methods Research was conducted at two levels: field trials and modelling. A two-year field trial (2015–2016) with 6 different varieties of hemp was conducted in an environment of North-East Italy (Friuli Venezia Giulia region). Data collected in the field trial and a crop model were then used to assess the effect of environment on hemp varieties performances, thus evaluating hemp crop suitability under Friuli region soil and climate conditions. The model developed was used to simulate hemp seed oil content, date of maturity, seed yield and irrigation water requirement. 2.1. Field trial 2.1.1. Plant materials The hemp varieties used in this trial were chosen for their different origins, sexual type and cycle duration and as representative of those present in the EU database of registered hemp varieties in early 2014, and normally used in field trials in different European environments (Table 1). 2.1.2. Crop management techniques and field experimental design The field experiment was conducted at the Experimental Farm of Udine University, Udine, Italy (46° 04’ N, 13° 22’ E, 109 m a.s.l. and 0% slope). The site has a shallow soil (from 50 to 80 cm) with a loamysandy texture (average sand, silt and clay, 40, 43 and 17%, respectively) (Table 2). The soil rests on a very deep gravelly bed and the water table is very deep and out of the reach of plant roots. No capillary rise is available. The main climatic parameters of the location are

2.1.3. Sampling method and traits measured The emergence date for each genotype was set when 75% of seedlings had emerged. Emergence occurred uniformly for all cultivars in both years, about 6 and 9 days after sowing, in 2015 and 2016, respectively. Ten representative plants in the same area were selected in order to check, twice a week, the phenological stage, in order to determine full 2

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Table 3 Monthly mean maximum temperature (Tmax), minimum temperature (Tmin), and total rainfall during the growing seasons in 2015 and 2016, and the 30-year mean (1992–2016) at Udine (Italy). Month

April May June July August September October Mean Total

2015

2016

1992-2016 average

Tmin (°C)

Tmax (°C)

Rainfall (mm)

Tmin (°C)

Tmax (°C)

Rainfall (mm)

Tmin (°C)

Tmax (°C)

Rainfall (mm)

5.9 12.7 15.7 19.7 18.3 13.5 8.9 14.3

18.6 23.4 28.2 32.5 30.9 24.6 19.0 25.3

52.1 104.1 97.0 101.9 168.0 147.6 220.8

8.5 11.1 15.9 17.7 15.0 14.4 8.2 13.0

19.2 22.3 26.5 30.6 29.1 27.4 18.4 24.8

66.3 238.3 122.4 92.3 68.7 113.9 123.8

7.4 12.2 15.5 17.0 16.7 12.6 9.0 12.9

18.3 23.4 26.9 29.4 29.4 24.5 19.0 24.4

121.6 122.6 124.9 117.7 131.6 162.4 167.8

891.5

825.7

flowering and the beginning of seed maturity stages, as in Mediavilla et al. (1998). Harvesting, performed at seed maturity stage, corresponding to 50% of hard seed, code 2306 of Mediavilla et al. (1998), started in August with the earliest varieties and ended in October, in both years. All plants in a 3.0 m2 area of each plot were manually cut at 5 cm from the soil surface. Plant height was measured on ten randomly chosen plants, from the base to the top of the inflorescence and all harvested plants were divided into two fractions: stems and inflorescence, weighed separately. These were oven-dried at 70 °C until constant weight in order to evaluate the aboveground and stems total dry biomass. The seeds, separated from the inflorescence by means of a threshing machine, were cleaned and weighed for seed yield determination, after removing the empty seeds. A representative sub-sample of dried seed of each variety was finely powdered, stored at 4 °C and used for further chemical analysis.

948.6

scenario analysis. The model1 requires, as daily inputs, minimum and maximum air temperature (°C), rainfall (mm/d) and reference evapotranspiration (mm/d) to simulate hemp phenological development, irrigation requirement and seed yield, depending on water stress level. Irrigation event is automatically activated when all the following conditions are satisfied: 1. the model is set for automatic irrigation; 2. the daily rainfall is less than 5 mm; 3. the water stress index (SI, %) overcame that one allowed (Astress). Astress is a parameter set by the model user. Note that, if Astress≥60, only deficit irrigation is performed; if Astress = 0, crop is fully irrigated. SI considers not only the reduction in evapotranspiration but also the effect on yield trough the Ky coefficients (Doorenbos and Kassam, 1979) and is calculated as:

SI = SIa·Ky /KyF where SIa (%) is calculated as a function of soil moisture (Us), wilting point (WP) and critical moisture at water stress start (Uz) and is 0 if Us > Uz, and 100 for Us < WP, and SIa = (Uz − Us )/(Uz − WP )·100 for WP < Us < Uz. Uz is calculated as: Uz = WP + (1 − fd )·(FC − WP ) , where fd is the fraction of easily available water, set to 0.66 (Cosentino et al., 2012) and FC the field water capacity. Ky is the yield response factor to water deficit, changing during the crop cycle as a function of phenological development (GDD). KyF is the higher yield stress factor, assumed at flowering and set to 1.1. When automatic irrigation is trimmed, the irrigation volume Ivol is calculated as:

2.1.4. Seed chemical analysis Briefly, the hemp seeds were ground in a coffee mill for 30 s. Immediately after grinding, ether extracts were obtained by treating the hemp seed for 12 h with petroleum ether (b.p. 40–60 °C) using a Soxhlet apparatus. The petroleum ether was then removed by evaporation under nitrogen gas flow and the oil was dried over anhydrous sodium sulfate (Na2SO4), filtered and quantified. Each sample was extracted in triplicate. Total nitrogen contents were determined by the Dumas combustion method using an Elemental Analyzer (vario Micro cube, Hanau, Germany). Protein was calculated from the nitrogen content by multiplying by 6.25. The dried samples were combusted in a muffle oven for 2 h at 550 °C to determine ash content. Crude fiber (CF) was determined following the AOAC – Official Method 978.10.

Droot·(FC − Us ) if Droot·(FC − Us ) < IvolMax ⎫ Ivol = ⎧ ⎨ IvolMax if Droot·(FC − Us ) ≥ IvolMax ⎬ ⎩ ⎭ where Droot (mm) is the rooting depth as a function of GDD and IvolMax, the maximum irrigation volume, depending on practical issues (time to infiltrate, waiting for the rainfall, etc.), set to 30 mm, a usual value that farmers use in this specific environment. Seed yield reduction due to water stress is related to the evapotranspiration reduction by a daily adaptation of the FAO Ky method (Doorenbos and Kassam, 1979). Seed oil content (Oil) is simulated using the relationship with average daily maximum temperature of the flowering-maturity period (Atmax). This relationship, obtained from the experimental results of this work (Oil = 115 – 3.1 · Atmax), was estimated using the two-year trial data, for all varieties, with R2 = 0.62 (Fig. 1). Yield response to water deficit is calculated as depending on evapotranspiration reduction (Doorenbos and Kassam, 1979), whereas the actual relative yield (Ary; percentage of maximum yield without water deficit) is calculated as: Ary = Ky · (1 – ETa / ETm) · 100. Ky is the

2.1.5. Statistical analysis of field trial data Statistical analysis of experimental data was performed using R version 3.3.3 (R Development Core Team, 2017). Shapiro–Wilk normality test was performed to test normality condition. A two-way Analysis of Variance (ANOVA) was performed as mixed-effect model with genotypes and year as experimental factors. Genotype was treated as fixed effect, and was represented by six hemp cultivars (Table 1). Year was the random effect. Significance of each source of variation was evaluated by F-test. When the F-ratio revealed significant differences, means were compared by the least significant difference (LSD) at p < 0.05. Pearson correlation coefficients were computed to measure the strength of linear association among investigated traits. 2.2. Modelling approach 2.2.1. Model development The modelling approach used the SEMoLa simulation platform (v. 6.9.0; Danuso and Rocca, 2014) to implement the model and perform

1 The model (Hcrops) was implemented as a Windows OS software application. The installation package, including calibration algorithms, SEMoLa source code and example files is freely available from the corresponding author.

3

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Fig. 1. Linear regression between seed oil content (%) and maximum temperature averaged during flowering seed maturity period (Tmax) for the six hemp cultivars. (a) Udine, 2015–2016, (b) Udine, 2015.

performed:

yield response factor, varying during the crop cycle, Eta is the actual evapotranspiration and ETr the reference evapotranspiration. ETr is considered an exogenous variable, input of the model. Eta also depends on soil moisture, calculated by a cascade, one layer, soil water balance. Ky values adopted were similar to those generally used for sunflower (0.9 at emergence, 1.1 at flowering and 0.8 at ripening) and linearly interpolated during the crop cycle. Phenology is simulated considering only two stages (flowering and maturity), with a modification of the models presented by Lisson et al. (2000a, 2000b, 2000c, 2000d), Amaducci et al. (2008) and Cosentino et al. (2012). The phenological model is based on the accumulation of growing degree days (GDD, °C d), from plant emergence, till a value to reach flowering (GDDf) and another to reach maturity (GDDm). The daily accumulation (GDDday) is calculated as an average of Tmin1 and Tmax1, corrected by a daylength factor (Fx). Tmin1 equal Tmin if Tmin > Tbase, otherwise Tmin1=Tbase; Tmax1 equal Tmax if Tmax < Topt, otherwise Tmax1 = 2·Topt-Tmax. Tbase is the base temperature parameter, different for sowing-emergence and vegetative (TbaseV), and reproductive (TbaseR) phases. GDDday also depends on day-length (DL) by a factor Fx, where Fx = 1 for DL ≤ Fopt, and linearly decreasing for DL < Fopt, with Fx=0 for DL=24.

1 Estimation of the actual yield percentage (Ary), for each cultivar in 2015 and 2016 trials, by simulation. 2 Execution of simulation trials. Applying automatic irrigation with Astress values changing from 0% to 100%, a value of 60% that activates the same irrigation events performed in the field experiment management was identified. So, we concluded that the simulated value of Ary at 60% stress (Ary60) corresponds to the yield really obtained from the experiment. 3 MaxYield, for each cultivar, was estimated as: MaxYield = (Yield / Ary60) · 100. This calculation was performed for the two trial years (2015 and 2016) and then averaged. 4 The Yield value for each variety in each year was then calculated as Yieldi = (Aryi · MaxYield) / 100. 2.2.3. Estimation of phenological parameters Due to the relatively high number of parameters of the phenological model, the limited data available for each cultivar (2 stages x 2 years) and the strong correlation among the parameters, a joint calibration of all seven phenological parameters was not feasible. To overcome this problem, two strategies were adopted: i) reducing the number of parameters to be estimated and ii) using a stochastic evolutionary algorithm (genetic algorithm) instead of the deterministic methods such as Simplex or Gauss-Newton. Among phenological parameters, Fopt, GDDf and GDDm were estimated, for each variety, by calibration on experimental data; GDDe was calculated from experimental data while the other parameters (held constant for all varieties) were obtained from the literature: TbaseV = 1.9 °C (Cosentino et al., 2012), TbaseR = 11.3 °C (Cosentino et al., 2012), Topt = 26.4 (Amaducci et al., 2008). For the Fopt, GDDf and GDDm estimation, the genetic algorithm of Michalewicz (1999),

2.2.2. Estimating seed yield variability among years and cultivars The model gives the percentage of seed yield with respect to the maximum attainable yield in a specific environment without water limitation (MaxYield). So, knowing MaxYield it may be possible to rebuild the yield variability among years as Yieldi = Aryi · MaxYield / 100, where i indicates a specific year. MaxYield could have been obtained performing experiments with fully irrigated crops. In our case, as in the most frequent ones, we only had the seed yield obtained in deficit irrigation condition (see below). Thus, in order to obtain MaxYield for each cultivar in the trial environment, the following procedure was 4

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2015, with Chamaeleon recording the highest reduction (-73 cm) (Table 5). In both years CS was the tallest cultivar (290 and 279 cm, in 2015 and 2016, respectively), whereas Uso-31 was the shortest (201 and 161 cm in 2015 and 2016, respectively). Stem yield (SY) was significantly affected by cultivar and year, as average effects. It decreased in all cultivars analyzed in 2016 with respect to 2015. In particular, the highest reduction was observed in Chamaeleon and Uso-31, with about 55% decrease, while Ermes recorded the lowest reduction (about 19%). CS gave the highest SY in each year (14 and 9 t ha−1 in 2015 and 2016, respectively), Uso-31 the lowest (8 and 4 t ha−1) (Table 5). Between years, 2016 showed a lower seed yield (0.69 t ha−1, as average across varieties) than 2015 (0.92 t ha−1). Ermes (1.03 and 0.86 t ha−1) and Fedora (1.00 and 0.92 t ha−1) in 2015 and 2016, respectively, were the most productive cultivars in both years without any significant variation between years (Table 5). Conversely, CS and Chamaeleon, which in the first year showed a seed production very close to the latter, recorded a very marked reduction in 2016 (−38 and −65%, respectively). The 1000-seed weight was affected by cultivar x year interaction. All varieties showed a lower 1000-seed weight in 2016 (8.2 g, as average) than in 2015 (9.7 g), with the exception of Fedora and Uso-31, which significantly increased their 1000-seed weight in 2016 by 36% and 41%, respectively, as compared to 2015 (Table 6). The heaviest and lightest seed weights in the experiment were obtained from CS (18.1 g) and Uso-31 (5.13 g) in 2015. Seed oil content, affected by cultivar x year interaction, ranged from a minimum of 14.9% for Uso-31 in 2015 to a maximum of 31.6% for CS in 2016. All varieties showed a very similar seed oil content in both years, with the exception of CS and Ermes which significantly reduced their values in 2016 (−6 and −8%, respectively) with respect to 2015 (Table 6). Seed protein content ranged from a minimum of 13.9% for Uso-31 in 2016 to a maximum of 21.8% for CS in 2015. All varieties showed a significant reduction in protein content in 2016 with respect to the 2015, with Ermes having the highest decrease (−29%). The exceptions to this trend were CS, which showed the highest and very close values in both years and Chamaeleon, which showed the lowest but statistically not different values (14.9 and 15.3%, in 2015 and 2016, respectively) (Table 6). Hemp seed showed a crude fiber content of about 54% on average across years and varieties. The lowest values were recorded by the Italian cultivar CS in both years (41 and 44.5% in 2015 and 2016, respectively). Only Bialobrzeskie (about 53%) and Fedora (about 54%) showed a not statistically different content between years. Conversely, crude fiber content increased significantly in 2016 compared to 2015 in CS, Ermes and Uso-31, whereas Chamaeleon showed the opposite behavior with a significant decrease of about 10% in 2016 (Table 6). Lastly, seed ash content showed a trial average of 5.8%, ranging between 5.1% for Ermes in 2016 and 6.5% in Fedora in 2015. Ash content decreased in all tested cultivars in 2016 if compared to 2015, with the exception of CS (6.0 and 6.1%) and Ermes (5.3 and 5.1% in 2015 and 2016, respectively), which did not show any significant

implemented in each SEMoLa model, was adopted. 2.2.4. Scenario analysis Sowing was set to the 110th day of the year (April, 20). The factors of the simulation experiment were: varieties (6), meteorological years (24 from 1995 to 2018), and three levels of irrigation (rainfed crops, deficit irrigation triggered at 60% of stress, fully irrigated crops). The simulated values of the studied variables (Yield, seed yield t/ha; MatDate, maturation date, DOY; Oil, seed oil content, %; Sirri, seasonal irrigation volume, mm) were obtained for the 24 years and for each variety, in each irrigation regime. The variability generated by meteorological conditions in interaction with soil and cultivar traits was used to calculate the empirical cumulative distributions, giving the notexceeding probabilities. Meteorological data to run simulations were obtained from the Regional meteorological service (Osmer-Arpa, Omnia database). Reference evapotranspiration was calculated using the Hargreaves method (Hargreaves and Samani, 1985) and daylength was obtained, from the day of the year and latitude, with the method of Keisling (1982). 3. Results 3.1. Weather conditions Table 3 reports air temperature and precipitation recorded during the hemp crop cycle in 2015 and 2016, compared to 25-year mean (1992–2016). Total precipitation was 671 mm in 2015 and 702 mm in 2016, lower than the average of multi-year mean (781 mm). In particular, the summer period June-August 2016, in which the main phenological stages (flowering and grain-filling) of the cultivars tested occurred, had about 24% and 40% less rainfall with respect to the same multi-year period and 2015, respectively, proving 2016 to be a year characterized by a dry summer. On the contrary, the same period in 2015 was significantly warmer by about 2 and 1.5 °C, in maximum and minimum average temperatures respectively, than the same multi-year period. 3.2. Hemp traits Cultivar and year, as main treatments, showed significant effects for all variables with the ANOVA analysis, with the exception of year for seed oil content (Table 4), while the two-way year x genotype interaction was highly significant for all the characters analyzed (Table 4). Regarding the duration of emergence to flowering in days between cultivars, CS was the latest in each year, with 97 and 107 days in 2015 and 2016, respectively (Table 5). Conversely, Uso-31 was the earliest, with the same duration (about 55 days) in both years. With the exception of Uso-31, vegetative growth was 13 days longer on average in 2016 than in 2015 in all cultivars, with Ermes showing the longest duration in days (+22). Plant height was significantly affected by cultivar x year interaction and generally decreased in all tested cultivars in 2016 with respect to

Table 4 Analysis of Variance (F-values) for days from emergence to flowering, plant height, yield components and seed composition. Values in brackets in the body of the table are in percent of total SS after removal of residuals. Source of Variation

d.f.

Emergence-flowering

Plant height

Stem yield

Seed yield

Seed weight

Seed oil content

Seed protein content

Seed crude fiber

Seed ash

Cultivar (C)

5

Year (Y)

1

CxY

5

421.9*** (78.8) 436.7*** (16.3) 26.0*** (4.8)

29.4*** (66.2) 60.3*** (27.2) 2.9* (6.6)

15.4*** (41.0) 96.2*** (51.2) 0.9 ns (7.7)

2.7* (21.7) 15.4*** (24.5) 6.8*** (53.8)

39.0*** (67.7) 15.8*** (5.5) 15.5*** (26.8)

470.75*** (78.9) 7.04 ns (1.2) 118.94*** (19.9)

110.3*** (63.5) 76.4*** (8.8) 48.1*** (27.7)

112.1*** (78.3) 23.6*** (3.3) 26.3*** (18.4)

16.4*** (38.3) 78.7*** (36.9) 10.6*** (24.8)

*, ** and *** Significant at the p < 0.05, 0.01 and 0.001 levels, respectively. ns = not significant. 5

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Table 5 Vegetative Growth, plant height and yields in six hemp cultivar in the two-year field trial. Cultivar

Bialobrzeskie Chamaeleon Cs Ermes Fedora Uso-31 Overall mean

Vegetative growth (dd)

Plant height (cm)

Stem yield (t dm ha−1)

Seed yield (t dm ha−1)

2015

2016

mean

2015

2016

mean

2015

2016

mean

2015

2016

mean

60de 70cd 97a 66d 60de 55e 68

78c 83b 107a 88b 76c 54e 81

69 76 102 77 68 54 74

250cd 271bc 290a 255cd 239d 201f 251

208ef 198f 279ab 235de 195f 161g 212

229 235 284 245 217 181 232

10 12 14 10 9 8 11a

7 5 9 8 7 4 6b

8b 8b 12a 9b 8b 6c 8

0.61ef 1.15a 0.97ab 1.03ab 1.00ab 0.75cd 0.92

0.65ef 0.40f 0.60ef 0.86bc 0.92ab 0.68de 0.69

0.63 0.78 0.79 0.95 0.96 0.71 0.80

Values within 2015 and 2016 columns followed by the same letter are not significantly different (LSD at the 5% level). For Stem yield, values within “mean” column followed by the same letter are not significantly different (LSD at the 5% level). Values in row “Overall Mean” followed by the same letter are not significantly different (LSD at the 5% level).

variation between years (Table 6).

Table 7 Yields of the six hemp cultivars measured in the two-year field trials (ActualYield) and the actual relative yield (Ary60), simulated for each cultivar in same two years. Maximum yields (MaxYield) are obtained from ActualYield and Ary60 with the procedure described in the text.

3.3. Maximum seed yield Following the method described in the previous section, maximum yield was obtained for each cultivar in not limiting water conditions (MaxYield) and for the trial environment. Table 7 reports seed yields measured for each cv in the two-year experiment and those estimated by simulation (as percentage of maximum yield). MaxYield estimated differed among cultivars, depending on their different yield potential but also between years, so showing a certain interaction cultivar x year. The two-year averaged values ranged from 0.73 t/ha of Bialobrzeskie to 1.11 t/ha of Fedora.

Cultivar

Bialobrzeskie Chamaeleon Cs Ermes Fedora Uso-31

ActualYield(t/ha)

Ary60 (%)

MaxYield (t/ha)

2015

2016

2015

2016

2015

2016

mean

0.61 1.15 0.97 1.03 1.00 0.75

0.65 0.40 0.60 0.86 0.92 0.68

88.6 88.8 88.7 88.9 89.0 86.8

84.7 83.7 86.4 83.7 84.6 87.2

0.69 1.30 1.09 1.15 1.12 0.86

0.77 0.48 0.69 1.03 1.09 0.78

0.73 0.89 0.89 1.09 1.11 0.82

3.4. Phenological parameters 3.5. Scenario simulation results The phenological parameters of the model, obtained from the literature or calibrated for each cultivar, are reported in Table 8. The joint estimation of day-length parameter (Fopt) and those indicating the amount of temperature summation required to reach the end of the phase (GDDf, GDDm), highlights the different sensitivity of cultivars to photoperiod and temperature. In particular, Uso-31 resulted a very early variety due to a short requirement of GDD for both pre- and postflowering periods; CS was the latest cultivar with more GDD required in both phases. Ermes, Fedora, CS and Chamaeleon had a similar GDD requirement for flowering but whereas CS had a high GDD requirement for flowering-maturity period (GDDm - GDDf equal to about 1750), the other varieties required less (about 1100–1300); among these, Fedora had the shortest requirement for ripening. CS (the selected Carmagnola variety), which although of Italian origin, already showed a strong sensitivity to photoperiod in experiments in similar locations (Amaducci et al., 2008), having the highest Fopt (15.33 h). Uso-31 and Chamaeleon showed the lowest Fopt (14.15 h).

Scenario analysis was performed for each tested variety, with three irrigation regimes (no irrigation, deficit irrigation at 60% stress and full irrigation) for 24 years of meteorological data of the trial site. Of course, due to the year by year meteorological variability, a distribution of values was obtained for seed yield without irrigation (Yield), seasonal irrigation volume to maintain yield at the maximum value (Sirri), seed oil content (Oil) and maturity date (MatDate). Fig. 2 reports the cumulative distribution of seed yield (t/ha) obtained from scenario analysis, for all varieties. It can be noted, f.i., that in our environment, with deficit irrigation there is a 45% probability of obtaining seed yields over 0.8 t/ha. Instead, in rainfed conditions, more than 0.8 t/ha are obtained in only 30% of cases. Simulated seed oil content (%) ranged from 12.2 to 38.8%, with an average of 26.8%. The differences among cultivars were remarkable and varied from an average of 36.4% in CS to 23.4% in Uso-31 (Table 9). The overall cumulated distribution of oil content (Fig. 3a) shows the effect of the years in interaction with cultivar maturity date. Maturity date also showed a wide variation due to the different

Table 6 Seed weight and seed composition in six hemp cultivar in the two-year field trials. Cultivar

Bialobrzeskie Chamaeleon Cs Ermes Fedora Uso-31 Overall mean

Seed weight (g)

Oil (%)

Crude fiber (%)

Crude protein (%)

Ash (%)

2015

2016

mean

2015

2016

mean

2015

2016

mean

2015

2016

mean

2015

2016

mean

7.13f 9.63cd 18.10a 11.70b 6.70fg 5.13g 9.73

6.47fg 7.40ef 10.70bc 8.30de 9.10cd 7.26ef 8.20

6.80 8.52 14.40 10.00 7.90 6.19 8.97

22.2cd 17.8fg 31.6a 26.8b 19.0ef 14.9h 22.1

22.5cd 18.6fg 25.5bc 18.9ef 20.4ef 16.0gh 20.3

22.4 18.2 28.5 22.9 19.7 15.5 21.2

19.0b 14.9e 21.8a 21.1a 17.6c 17.6c 18.7

16.6cd 15.3e 20.8a 15.1e 15.9de 13.9f 16.3

17.8 15.1 21.3 18.1 16.8 15.7 17.5

52.2d 66.1a 41.5f 43.8ef 55.1d 59.1c 53.0

54.4d 59.9c 44.5e 58.2c 53.6d 63.0b 55.6

53.3 63.0 43.0 51.0 54.3 61.1 54.3

6.4ab 6.3ab 6.0bc 5.3e 6.5a 5.9cd 6.1

5.3e 5.8d 6.1bc 5.1e 5.2e 5.2e 5.5

5.9 6.1 6.1 5.2 5.9 5.6 5.8

Values within 2015 and 2016 columns followed by the same letter are not significantly different (LSD at the 5% level). 6

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Table 8 Phenological parameters (obtained or calibrated) for each cultivar. Parameter

Unit

TbaseV (1) TbaseR (1) Topt (2) Fopt GDDe (3) GDDf GDDm R2 RRMSE

Cultivar

°C °C °C h °C·d °C·d °C·d – %

Ermes

Bialobrzeskie

CS

Fedora

Uso-31

Chamaeleon

1.9 11.3 26.4 14.71 102 1585 2873 0.992 4.2

1.9 11.3 26.4 14.75 102 1511 2672 0.998 2.1

1.9 11.3 26.4 15.33 102 1583 3339 0.936 11.9

1.9 11.3 26.4 15.05 102 1584 2642 0.996 7.3

1.9 11.3 26.4 14.15 102 1326 2415 0.924 5.7

1.9 11.3 26.4 14.15 102 1576 2887 0.949 6.1

(1) Not calibrated. Values obtained from Cosentino et al. (2012). (2) Not calibrated. Value obtained from Amaducci et al. (2008). (3) Estimated from experimental data obtained in 2015–2016 at Udine.

Fig. 2. Not exceeding probability of seed yield (t/ha) obtained from scenario analysis with deficit irrigation at 60% stress (Yield 60) and in rainfed (Yield R) conditions. The distributions include simulation results of all six varieties in 24 years. Table 9 Seed oil content (%) and maturity date (doy) for each cultivar (the same for all irrigation regimes). Statistics are obtained from simulations, performed on 24 years of meteorological data (from 1995 to 2018). Cultivar

Ermes Bialobrzeskie CS Fedora Uso-31 Chamaeleon

Seed oil content (%)

Maturity date (doy)

Mean

Standard dev.

Mean

Standard dev.

27.4 25.1 36.4 24.7 23.4 27.5

4.7 4.5 3.9 4.8 4.9 4.7

265 254 292 252 240 265

3.2 2.7 3.9 2.6 2.5 3.0

Fig. 3. Not exceeding probability of seed oil content (%) (a) and maturity date (DOY) (b), obtained from scenario analysis. The distributions include simulation results of all six varieties in 24 years.

earliness of varieties and climate (Fig. 3b). Overall the maturity was obtained at DOY 261 (September, 18) on average, but varied from DOY 235 (August, 23) to DOY 299 (October, 26). The differences among cultivars were significant (Table 9) and the climate variation among years generated a standard deviation of 2 to 4 days. The distribution of irrigation water requirement, for different varieties and years, can be appreciated in Fig. 4, where a comparison between deficit irrigation (irrigation applied when a stress of 60% was reached) and fully irrigated crop (without any stress). On the average among cultivars and years, the seasonal irrigation applied was 266 mm with full irrigation and 119 mm with deficit irrigation. The irrigation volume only slightly varied among cultivars, and depended on the different maturity date.

4. Discussion 4.1. Yield potential Hemp has a great potential as a high yielding multipurpose crop in Europe (Amaducci et al., 2015; Salentijn et al., 2015; Tang et al., 2016). This paper presents and discusses the results from six commercial hemp cultivars during two years of trials for the above dual-purpose cultivation in an Italian environment. The stem yield (t ha−1 of d.m.) obtained in this trial, as mean across years and cultivars (8 t ha-1), is quite similar to the average stem yield (7.3 t ha-1) reported by Carrus et al. (2013) in Europe in 2010 and to the 7.1 t ha-1obtained by Tang et al. 7

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partially, to explain the incomplete accumulation of oil in the seed. In fact, a negative and significant relationship was found (R2 = 0.62**) between seed oil content and the average of the maximum temperatures recorded during the grain-filling period in both years (Fig. 1a). In particular, this relationship shows an even higher correlation coefficient (R2 = 0.81**) if limited to 2015 (Fig. 1b), when the maximum average temperatures during that period, were very high (30–31.5 °C) for all cultivars, with the exception of CS (27.1 °C) and Ermes (29 °C), due to the lateness of their reproductive phase. During 2016, the range of maximum mean temperature was slightly lower (29.3–29.9 °C) and CS variety reached this phenological phase in a period with milder temperatures (T max below 28 °C). These temperatures are in any case higher than those reported by Anwar et al. (2006) who considered hemp suitable to grow in a mild, humid climate with a temperature range of 16–27 °C. This confirms that the oil content and composition of hemp seeds is largely affected by environmental factors (Kriese et al., 2004) and the wide variation in seasonal temperature and soil texture might have been the two major factors contributing to the reduced oil content (Anwar et al., 2006). High temperature also altered grain characteristics in other field crops. For instance, in sunflower brief periods of heat stress during grain-filling altered seed weight, increased pericarp/kernel ratio and diminished seed oil content, lowering the seed commercial quality (Rondanini et al., 2006, 2003). Generally, seed oil content was negatively related with temperature during grain-filling (Connor and Hall, 1997; Harris et al., 1978).

Fig. 4. Not exceeding probability of seasonal irrigation volume (mm) obtained from scenario analysis with deficit irrigation at 60% stress (Sirri 60) and in fully irrigation (Sirri 0) conditions. The distributions include simulation results of all six varieties in 24 years.

(2016) in Italy in 2013. The significant cultivar effect on stem yield obtained in this experiment is confirmed by several authors (Cosentino et al., 2013; Höppner and Menge-Hartmann, 2007; Pahkala et al., 2008; Struik et al., 2000). The stem yield obtained at seed maturity in this trial is proportional to the GDD accumulation during the vegetative phase (Faux et al., 2013), confirming the positive relationship between cycle length and stem yield already highlighted by other authors (Cosentino et al., 2013; Struik et al., 2000; Tang et al., 2016). 2016 had very limited rainfall during July-August (Table 3) which, together with the specific soil characteristics (shallow and gravelly soil), significantly affected biomass yield (about 6 t ha-1). This was lower than the 7.1 t ha1 obtained by Tang et al. (2016) in an experiment conducted in Italy in 2013, in which stem yield was limited by a combination of late sowing and early flowering. The seed yield was comparable to the range of 0.3 to 1.10 t ha−1 reported in many studies conducted in different environments of southern, central or northern Europe (Deleuran and Flengmark, 2005; Gorchs et al., 2017; Mediavilla et al., 1999; Meijer et al., 1995; Vogl et al., 2004). The early-medium and medium monoecious cultivars, Ermes and Fedora, showed a better stability in grain production across years than dioecious cultivars. In particular, seed yield of both dioecious cultivars, comparable with the best monoecious cultivars in 2015, fell significantly in 2016, in which both dioecious cultivars had significantly prolonged the flowering-seed maturity period (data not shown). This reduction in seed production could likely be a consequence of a rapid change of several environmental factors such as temperature, water availability and day–length during the grain-filling period (Faux et al., 2013; Höppner and Menge-Hartmann, 2007; Tang et al., 2016).

4.3. Cultivars suitability for multipurpose use This experimentation aimed to identify the best responses from cultivars mainly used for the production of seed, while the production of stems, although important, was postponed to the time of mature seed harvesting. The dioecious varieties, known for their biomass utilization, in the environment under study, showed a significant contraction of GDD accumulation to complete the emergence-flowering phase in 2016, which negatively affected stem yield and determined a strong elongation (in days) of the flowering-maturation phase, which negatively affected the seed yield, due to a limitation of favorable environmental conditions. For multipurpose use, in which seed production is a priority, the monoecious varieties should be preferred as they are more stable under different environmental conditions than the dioecious varieties, with the exception of Uso-31, which, given its excessive precocity, does not seem suitable for the studied environment, especially with the sowing time used. Conversely, the performance of the same earlier variety with a delayed sowing period, i.e. after the barley or wheat harvest in this environment, could be more interesting. For hemp crop grown mainly for human consumption, the quality characteristics of the seeds are also essential. In this experiment the seeds obtained were, in the majority of cases, of inadequate weight, with a low oil and protein content and excessively rich in fiber, therefore with a predictable low yield in the technological processing: poor vegetable oil yield and a seed cake extraction excessively rich in fiber and low in protein. In particular, it would be appropriate for the grain-filling phase to take place in conditions of not excessive temperature (< 30 °C as T max) and not limiting conditions of day-length and global radiation that would affect the quality and yield potential of seed, respectively.

4.2. Seed characteristics In addition to seed yield, the seed characteristics are very important as they influence, in the technological processing, the vegetable oil and seed extruded yield and quality destined for human consumption. The seeds obtained in our experiments, generally showed light weight, low oil and protein content and a high fiber content if compared to seeds obtained in similar experiments (Anwar et al., 2006; Callaway, 2004; Galasso et al., 2016; House et al., 2010; Kriese et al., 2004; Vonapartis et al., 2015). The exceptions were CS and, limited to 2015, Ermes. Both provided seed oil and protein content within the range obtained by various authors (Anwar et al., 2006; Callaway, 2004; House et al., 2010). The effects of environmental stress during cell division and differentiation (histodifferentiation), in which the cell expands gaining in dry weight as a result of the synthesis and deposition of stored reserves to accommodate these reserves, is generally deleterious and results in a decline in seed quality (Bewley and Black, 1994). In our experiment, the high temperatures seemed, at least

4.4. Evaluating hemp crop suitability to the environments The optimal photoperiod parameter (Fopt) obtained by the modelling approach (Table 9) was very similar to those obtained by Amaducci et al. (2012) and Cosentino et al. (2012) and the GDDf’s were within the range of values obtained by Amaducci et al. (2012). In particular, our values were slightly lower than the maximum values of the above range, referring to early sowing in a locality with a similar latitude (Cadriano, 44.33 °North) to that of our experiment (Udine, 46.04 °N) 8

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References

(Table 8). On the contrary, the GDDf obtained were significantly higher than the minimum values of the range by Amaducci et al. (2012) which referred to very late sowing, and the values obtained by Cosentino et al. (2012), in an environment of southern Italy at a much lower latitude (37.24 °N) than ours. Regarding the seed oil content (Table 8) the modeling approach confirms the ranking of the varieties (Table 6), although slightly overestimating the oil content value in the seed. This is because high temperatures in the post-flowering period, which the model considers as a possible and important reason for the limited seed oil accumulation, are probably not the only cause. The methodology, combining field experiments with model simulation, overcame the limitation of the single approaches, by fully exploiting the available information. In particular, the field trial results of a few years were expanded to give information about the effect of climate uncertainty. At present, this aspect is of extreme importance, given that the cumulated probability distribution of possible yields, maturation date or irrigation water requirement are at the base of economic risk assessment.

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5. Conclusions At the end of these experiments, the environment under study has demonstrated the capability to give hemp biomass and stem yield similar to those obtained in other European sites, while the seed production has been shown to be slightly lower. If seeds with their relative extraction products were the priority objectives, the monoecious varieties and, among these Fedora, seem to be the most productive and constant for the environment. Instead, the dioecious cultivars and CS in particular would be preferable if biomass and stem production are the main objective. The seeds obtained, in the majority of cases, showed low quality; i.e. low oil and protein content and excessively rich in fiber, therefore with a predictable low yield in the technological processing. In particular, excessive temperature (daily maximum temperatures higher than 30 °C) during the grain-filling phase would limit seed oil accumulation. Moreover, the adaptation of hemp crop to North-East Italy has been evaluated through a modelling approach, easily customizable for other environments, by changing or recalibrating some model parameters. Although this preliminary modelling approach would require further validations using data from different environments, the results obtained from the scenario analysis give useful information to plan hemp crop, by predicting the most important phenological phases and evaluating the best irrigation management related to the seed yield expected, both in terms of absolute values and probability of the different yield levels. In particular, the efficiency of irrigation (seed yield increase/irrigation water), obtained by simulation, was 0.66 and 0.61 kg/mm in deficit and full irrigation, respectively. The proposed approach could also be applied to optimize irrigation in terms of costs and revenues, for any specific pedo-climatic conditions.

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements This experimentation was possible thanks to the contribution and technical support of the Regional Agency for Rural Development (ERSA) of the Friuli Venezia Giulia region. The authors would like to thank Gaia Dorigo for the laboratory analysis. 9

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10