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Productivity and Biometric Characteristics of 11 Varieties of Willow Cultivated on Marginal Soil

: In response to the growth in the global population and climate change concerns, questions remain regarding the adaptation of production systems to meet increasing food and energy demands. The aim of the paper is to present the production potential and biometric features of 11 willow varieties bred and cultivated mainly in Europe. The experiment was set up on marginal soil. The research was conducted in 2016–2020 and concerned 11 varieties of willow harvested in a three-year cycle. The dry matter yield of the examined willow varieties ranged from 6.5 to 13.8 Mg ha − 1 year − 1 . Varieties Tur, Sven, Olof, Torhild, and Tordis were characterized by a relatively low level of yield (7.2–8.2 Mg ha − 1 year − 1 ). The highest dry matter yield was obtained for the varieties Ekotur and ˙Zubr, respectively, of 11.5 and 13.8 Mg ha − 1 year − 1 . The assessed varieties di ﬀ ered in both the level of obtained dry matter yield and biometric features. The ˙Zubr variety produced the smallest number of shoots (three), but with the greatest height (4.8 m) and diameter (29.6 mm). Varieties with high production potential develop fewer shoots, but are taller and have a larger diameter than other varieties.

Introduction
In response to the growth in the global population and climate change concerns, questions remain regarding the adaptation of production systems to meet increasing food and energy demands, and the identification of the most efficient and sustainable production schemes. Interest in the biobased economy has, therefore, been increased by aligning policies relating to central and interdependent sectors, including agriculture and forestry, food, feed, bioenergy, and bio-chemistry [1,2]. Because the EU's growing priority for renewable energy sources is driven by increased energy dependency, rising fossil fuel prices, and the desire to protect the environment, the demand for energy from biomass, mainly wood, is increasing. Waste wood from forestry and municipal energy management may in future be supplemented with wood acquired from agricultural land as fuel for power or heating plants [3][4][5][6]. Although the primary role of agriculture is food production, a portion of agriculture land has typically been devoted to non-food products, mainly within the framework of emerging technologies. Such uses include the production of bioenergy and various biomaterials [7].
Fast-growing trees for biomass production are proposed as an economical and ecological solution to meet increasing demand for energy, and also for the anticipated shortage of raw materials for wood-based industries [8][9][10][11]. One of the short rotation woody coppices (SRWCs) is willow, which has been widely accepted as a renewable energy source. Willow biomass can be converted by a wide range of technologies, such as combined heat and power hydrothermal upgrading, into a variety of energy forms and carriers [12,13] Willow has several advantages as an SRWC, including a wide range of genetic diversity, easy reproduction, tolerance to a wide range of site conditions, ability to rapidly regrow after harvests, possibility of harvests in different rotations, and resistance to disadvantageous environmental conditions, e.g., morning frost, wet snow, and strong winds [14][15][16]. Willow may be taken into account in a concept of extensive agriculture, especially on marginal soils, which are of little use or unusable for growing food crops, soils that are periodically excluded from use, and some fallow lands [6,17].
A significant novelty of this study is the provision of a comprehensive assessment of (1) dry matter yield, (2) biomass moisture, and (3) biometric characteristics of 11 willow varieties, bred and cultivated in various European countries. An additional advantage of the presented results is that the studied varieties were cultivated in commercial plantations on marginal soil.

Experiment Setup
A one-factor field experiment was conducted in 2016-2020 at the Experimental Farm of the Institute of Soil Science and Plant Cultivation-State Research Institute located in Osiny, Poland (51 • 28 21.34 N, 22 • 3 7.65 E). The experiment was set up on marginal soil. It was a clay soil comprising light clayey sands transformed into light clay at a depth of 70-80 cm, class IV on a VI scale in the Polish classification. The average pH in KCl is 5.5, i.e., it was slightly acidic. The humus content was low at 1.06%. At the time of experiment, the average nitrogen content in soil was 378 mg NO3N kg −1 , phosphorus was 224 mg P 2 O 5 kg −1 , and potassium was 124 mg K 2 O kg −1 . The content of magnesium in soil was 49 mg MgO kg −1 and calcium was 70 mg CaO kg −1 . The content of nutrients in the soil on which the experiments were conducted was at a medium or high (for phosphorus) level. Therefore, the tested plants were optimally supplied with nutrients. The annual sum of precipitation for the locations where the experiment was conducted is 568 mm on average, and the average annual air temperature is 8 • C. Willow cuttings were planted in April 2016 at a density of 18,000 per ha. The cuttings were planted in two-row strips with rows spaced every 0.75 m apart. The strips were spaced every 1.5 m. The cuttings in each row were planted every 0.50 m. In the first year of vegetation, weeds from plantations were removed mechanically. The research covered 11 varieties of willow (Table 1). The cultivation area of each variety was 270 m 2 ; moreover, for the varieties located on the edge of the field, an additional strip of plants (2 rows) was planted in order to eliminate the edge effect. The total area of the experimental field was 3150 m 2 . After the first year of vegetation, in January 2017, a maintenance cut was made. Willow biomass was harvested in January 2020 after three years of vegetation. In the year the plantation was established, mineral fertilization was applied in the dose of 30 kg ha −1 N, 60 kg ha −1 P 2 O 5 , and 90 kg ha −1 K 2 O. After maintenance, a cut of 80 kg ha −1 N, 60 kg ha −1 P 2 O 5 , and 90 kg ha −1 K 2 O was applied. No pesticides were used on the plantation.

Yield and Biometric Analysis
After three years of vegetation, in January 2020 the biomass yield and their biometric features of the tested willow varieties were determined. For this purpose, 5 whole plants of each variety were harvested in 5 replications, giving a total of 25 plants. Each of the replicates was randomized, then 5 consecutive plants in a row were harvested. No samples were taken from the edge of the experimental field to eliminate the edge effect. Sampling was performed manually using a chainsaw. Each plant was weighed immediately after harvest. Two shoots with average values of biometric features were taken from each plant and ground for determination of the share of dry matter. In the laboratory the biomass humidity was determined with the drying method at 100 • C for a period of 14 days. Dry matter yield was determined from the ratio of fresh matter yield and and dry matter content. The yield from the area of 1 ha was calculated from the product of the actual density and the unit weight of plant.
Measurements of biometric parameters of the tested plants were also performed. The following were determined: number of shoots per plant, diameter of plant shoots at a height of 15 cm, and Ø (mm) and height of shoots (m).

Statistical Analysis
The results were subjected to analysis of variance for a single factor (ANOVA) using the software Statistica v13.3 (TIBCO Software Inc., Palo Alto, CA, USA). The significance of differences between varieties for the variable, such as dry matter yield, number of shoots for plant, plant height, and shoot diameter was identified using the NIR test (p < 0.05). Cluster analysis was performed using the method of k means. The grouping variables were dry matter yield, number of shoots per plant, plant height, and shoot diameter. Initial cluster centers were established on the basis of a random selection of k observations using a standardized distance measure (Euclidean distance). A k-fold cross-validation was used to determine the number of clusters.

Results and Discussion
The dry matter yield of the tested willow varieties harvested in the three year cycle ranged from 6.5 Mg ha −1 year −1 for the Tora variety to 13.8 Mg ha −1 year −1 for theŻubr variety ( Figure 1). The highest dry matter yield was obtained for the varieties Ekotur andŻubr, respectively, of 11.5 and 13.8 Mg ha −1 year −1 , which are bred at UWM in Olsztyn. The Żubr variety obtained a significantly higher result than all other varieties. In second place in this respect was the variety Ekotur. The yield of Żubr variety was 51% higher than the average for varieties and 84-112% higher than the worst varieties. Żubr variety also yielded 20% higher than the second-best variety. Variety Ekotur yielded 26% higher than the average and 76% higher than the TheŻubr variety obtained a significantly higher result than all other varieties. In second place in this respect was the variety Ekotur. The yield ofŻubr variety was 51% higher than the average for varieties and 84-112% higher than the worst varieties.Żubr variety also yielded 20% higher than the second-best variety. Variety Ekotur yielded 26% higher than the average and 76% higher than the weakest variety (Table 2). Table 2. Matrix of differences (%) between yields of dry mass of tested varieties of willow.

Variety
(1) Average yields of dry matter (9.7-10.5 Mg ha −1 year −1 ) were obtained for the varieties Gigantea, Inger, and Linnea. Varieties Tur, Sven, Olof, Torhild, and Tordis were characterized by a relatively low level of yield (7.2-8.2 Mg ha −1 year −1 ). Results obtained from Olof, Torhild, Tordis, Gigantea, Inger, and Linnea varieties were close to the average value, with a deviation of up to 15%. The results of this group of varieties were from 5% to 62% better than those of the three lowest yielding varieties. Within this group Inger yielded 25% higher than Olof. Three varieties yielded much lower than average, Tora (−28%), Tur (−20%), and Sven (−18%). These varieties yielded about half as much as theŻubr variety. Despite the wide range of results, there were no statistically significant differences between the varieties Tora, Tur, Sven, Olof, Torhild, and Tordis.
Reported biomass yields of willow harvested in a three-year cycle, depending on the location and variety, ranged from 6.4 to 20.0 Mg ha −1 year −1 , and were generally similar to those obtained in our own research [15,18,19]. Stolarski et al. [16] compared 15 willow varieties and genotypes in a field experiment. As in the current research, the authors showed that theŻubr variety produces a very high dry matter yield, which, depending on the location, ranged from 18.0 to 20.3 Mg ha −1 year −1 in a three-year rotation. This yield was higher than that obtained in our own experiment (13.8 Mg ha −1 year −1 ) because the willow was grown on better-quality soils. It should be noted that regardless of the soil conditions, theŻubr variety yield was clearly the highest. In the study of Stolarski et al. [16] in relation to theŻubr variety, the dry matter yield of the next three varieties was lower by 13%, and for the next five by 17%. The yields obtained in our own research were also similar to those obtained in other parts of Europe, which in the UK ranged from 4.9 to 15.4 Mg ha −1 year −1 . It should be noted that the highest yields were obtained from Ashton Stott and Ashton Parfitt varieties (15.4 and 13.9 Mg ha −1 year −1 , respectively) grown in the UK, which were not included in the own research [20][21][22]. In contrast, the yields obtained in Scandinavia were lower and ranged from 6.9 to 7.7 Mg ha −1 year −1 in Sweden and 3.1 to 8.6 Mg ha −1 year −1 in Denmark, depending on the variety and site [23,24]. Furthermore, studies conducted in the USA and Canada indicate that the dry matter yields of willow genotypes were significantly differentiated both in terms of genotype and site. The dry matter yield of willow in these studies ranged from 3.5 to 13.6 Mg ha −1 year −1 [25,26].
The factor that determines the quality of willow biomass and, indirectly, the level of the yield, is biomass moisture during harvest. For the studied varieties, it was very diverse and ranged from 42.3-42.9% for the varieties Olof, Torhild and Tora, to 50.1-51.4 for the varietiesŻubr, Linnea, Inger, and Tur. The varieties Tordis, Ekotur, Sven, and Gigantea were characterized by the average level of humidity (46.5-49.3%). In studies conducted in north-eastern Poland, humidity of willow biomass during harvest, depending on the variety, ranged from 47.5% to 52.0% [27].
The assessed varieties differed in both the level of the obtained dry matter yields and biometric features (Table 3). The varieties Ekotur, Tordis, Tora, and Sven developed five shoots per plant, and Tur developed six. The varieties Gigantea and Inger had, on average, eight shoots per plant, and Linnea and Olof had seven. Those four varieties had the highest number of shoots per plant. The height of the shoots of the studied varieties also varied. TheŻubr variety achieved the longest shoots, with an average length of 4.8 m. The varieties Ekotur, Tordis, Tora, and Sven grew shoots ranging from 3.5 to 4.1 m. Other varieties developed shoots 3.2 to 3.4 m long. The diameter of the shoots was significantly differentiated. The lowest value of this parameter (15.6-16.3 mm) was recorded for the Gigantea, Tur, and Torhild varieties. In addition to theŻubr variety, the Ekotur, Tora, and Tordis varieties had the largest shoot diameter (19.3-21.7mm). TheŻubr variety, which was characterized by the highest dry matter yield, produced the smallest number of shoots (three), but had the greatest height (4.8 m), and the biggest diameter (29.6 mm). The second highest yielding variety, Ekotur, was also characterized by a small number of shoots, good height, and large diameter of shoots. However, the trend changed for the next three varieties-Gigantea, Inger, and Linnea-which also had a high yield level (9.7-10.5 Mg ha −1 year −1 ). These were characterized by the highest number of shoots throughout the experiment. Linea had seven shoots on average, and Inger and Gigantea had eight. All three varieties were similar in height, but the diameter of the shoots differed. The higher yielding varieties had the thickest shoot diameter. The closest to the average values of these parameters were Tordis and Tur, however, they yielded much lower than average. Olof and Torhild varieties developed the same number of shoots as Linea, however they obtained much lower height and thickness, so the yield of these varieties was lower than the average and by almost 6 Mg ha −1 year −1 lower than the yield of the best variety. Low yielding varieties, Tora and Sven, were characterized by a small number of shoots and average values of their height and thickness, and Tur had a high average number of shoots but small height and diameter. The presented results concerning biometric features of willow varieties are similar to those obtained by other researchers. They also confirm the thesis that parameters of those features are significantly different, depending on the variety [18,[28][29][30][31]. It should be emphasized that both the varieties included in the current research and those presented by other authors are characterized by more favorable values and relationships of biometric features compared to wild forms of willow growing in natural stands [32,33].
Characteristics of the features of individual varieties allow for a reliable selection of varieties most appropriate to biomass yield. Due to the growing interest in diversification of crops to compensate for other crops' flaws, mixed crops are often used. For this purpose, clusters of varieties that have similar biometric characteristics were distinguished. Different mixtures of varieties may contain varieties with the same characteristics, or varieties with extremely different parameters, depending on the purpose of cultivation. The cluster analysis confirmed that the tested willow varieties can be categorized in terms of the level of yield and biometric features into three clusters (Table 4). The first cluster includes theŻubr and Ekotur varieties, which stand out primarily because of a high yield, obtained through specific biometric features. Both varieties were characterized by an average low number of shoots but had large height and diameter. It was also shown in other studies that the highest dry matter yields are obtained from plants that develop a small number of shoots, but were tall and had a larger diameter [16,34]. By comparison, a relatively high yield was obtained by varieties from the second cluster, which were characterized by completely different biometric characteristics. Gigantea, Inger, and Linnea varieties had an average of 26.7% shorter shoots and 28.0% smaller diameter of shoots than the varieties from the first cluster, which was compensated for by a large number of shoots. Furthermore, plants from the second cluster were a group combining the characteristics of varieties from the first and third cluster. The third group of varieties was characterized by parametric features that were highly similar to the second cluster, but with a much smaller number of shoots, resulting in the lowest yield of the three mentioned clusters. The results obtained suggest that for energy purposes, varieties from the first cluster are definitely preferred. Large biomass obtained from less numerous shoots can provide proportionally more wood in relation to bark. For purposes other than energy production, for example, as a substrate for the extraction of active substances, varieties with a higher proportion of bark in the yield could be preferred [35]. Ideal for this purpose would be varieties from the second cluster, which have a large number of shoots and provide a relatively high yield. In contrast, the lowest yielding varieties could have a higher content of active substances, which would reduce the importance of total biomass yield. The productivity of individual varieties in terms of substrates used for different purposes should be further investigated.
The biometric features of different varieties of the willow Salix viminalis L. differ due to its crossbreeding with other species of willows. In the case of the willow, several dozen hybrid varieties are available on the European market. The most frequently cultivated domestic varieties are Start, Sprint, Turbo, Tur, Bison, and Ekotur. In Poland, Swedish varieties of Torhild, Sven, Olof, Gudrun, Tordis, Tora, and Inger are also often cultivated. Due to the high variability of yields of individual varieties in following years, it is recommended to use mixtures of several different clones in cultivation [35][36][37]. Different varieties, in addition to the clusters of similar varieties distinguished in the current work, may have different yield levels depending on the geographical location of cultivation, soil type, and cultivation technology used. It cannot be precluded that climatic differences, in addition to the progression of climate warming, will result in changes in the productive potential of varieties. The key in this aspect will be the sum and distribution of precipitation and the average summer temperatures. Willow is quite predictable in this respect: the biomass yield is linearly related to the annual total precipitation. In many regions, recent years have been dominated by periods of dry years with annual perception below 600 mm, interrupted by years of very large amounts of precipitation. Therefore, regardless of location, drought-resistant varieties will be preferred. In this case, features such as strong development of the root system will be crucial, probably at the expense of the aboveground biomass in the first years of cultivation. As average temperatures increase, the pressure of diseases and pests will become more severe, so high-yielding varieties could be unreliable in some years. On the basis of previous studies, no impact of climate change on willow cultivation in Poland has been observed to date. In the conditions of north-eastern Poland, the best yielding varieties were Ekotur (17.8 Mg ha −1 year −1 ),Żubr (15.9 Mg ha −1 year −1 ), and Start (16.7 Mg ha −1 year −1 ), which coincides with the results obtained in the current experiment. It may prove that the climatic gradient of the Polish area is of little importance for the selection of varieties [35][36][37].
In the current paper we focus on the cultivation of willows on marginal land. Such areas will have some production limitations, which will directly translate into the yield and its quality. In Poland, land is marginalized for various reasons. In more than half of the cases, the reason for abandonment of land is low soil productivity potential, caused by low humus content, low fertility, and poor water holding capacity [38]. On such soils, the highest yielding varieties with favorable biometric features may be found to be weaker than on soils which are optimal for the production of willow. Furthermore, good quality soils are also often located in marginal areas. Most commonly, good soils in Poland are abandoned for organizational reasons. In this category, most of the land is set aside in the vicinity of large cities and main roads. This land was abandoned, among other reasons, as a result of industrial transformations. This often leads to extension of the distance from the farm to the field due to the construction of a highway, which eliminates local roads. It is possible to use such land for willow cultivation, however, management of these fields will be limited due to the long distance from the farm. There is also potential in degraded and polluted land, on which willow cultivation can be carried out successfully, however, use of its yield will be limited to energetic and technical purposes. In the current study, it was pointed out that the most productive varieties of willow have also obtained a decent yield on marginal soils. However, it should be remembered that the plantation was professionally cared for and carried out with the best scientific knowledge. Many of the key measures that are provided in experimental plantations are often lacking in commercial crops [36]. On marginal land, often some kind of agrotechnical negligence occurs, so it is advisable to select varieties that require as little input as possible. For example, harvesting may be delayed by organizational problems and therefore the production cycle may be extended by an additional year. It is often observed that hybrid varieties, designed for high yields, can cope less well with environmental pressure and the consequences of agrotechnical negligence. It is also unclear how the tested varieties would survive without any fertilization and chemical protection on poor soils. Rich soils can ensure the survival of plantations without fertilization and even allow a decent yield. On marginal soils, such cultivation can carry a risk of failure [35,39].
The yields of individual varieties will also be quite different depending on the cultivation technology used. Disease and insect resistance are also important. To obtain the highest yield of biomass, a three year cycle is optimal, in which the highest yields are obtained by Ekotur andŻubr varieties. In Poland the cultivation was tested in harvest cycles ranging from annual to over five years. It was indicated that some willow varieties, considered as low yielding, may yield quite well in longer cultivation cycles. The experiment was also conducted with plant density modifications. The smallest stocking density is used in the Eco-Salix system, where willow is planted from large stakes in a small stocking density of less than 10,000 per ha. In the Eco-salix system, the best yielding variety was UWM 043. A very large planting density of about a hundred thousand individuals per ha was also tested in case of seedlings from annual offshoots, planted mechanically. In the case of the cultivation of willow grown in high density (48,000-96,000 per ha), there was a significant decrease in yield in the long-term perspective for all varieties tested (Tur, Turbo, UWM 046, UWM 200, and UWM 095), however, the UWM 095 variety provided the most stable yield. Depending on the height of the harvest cycle, the highest yield was obtained from Tora and 1023 clone in a one year cycle, Tur and 1054 clone in a two year cycle,Żubr in a three year cycle, and Ekotur and 1058 clone in a four year cycle [35,36,40].
The biomass yield depends not only on the biometric features, but also on the physiological characteristics of the plant. In this case, the characteristics of resistance to diseases, e.g., rust caused by Melampsora sp., which can reduce the biomass yield by up to 40%, may be crucial. Resistance to willow rust was obtained in most varieties with high yield potential, however, other biological limitations remain. An important and often underestimated feature is the resistance of willows to pests, whose pressure may increase with climate warming. Unfortunately, pest-resistant willow species are characterized by very slow growth in comparison to the willow Salix viminalis L., whereas hybrids between species do not provide satisfactory resistance and yield [41]. Obtaining resistance features, which reduces the pressure of pests, can be supported by molecular techniques. Gene identification and gene transfer application in industrial plants is less controversial than in the case of food plants. Modification of willow varieties can have a positive impact on mitigating the effects of climate crisis, allowing substantial amounts of carbon dioxide to be sequestered in the crop biomass. By comparison, the genetic purity of wild populations of willows, which can easily cross-pollinate with modified crops, cannot be assured. It is unlikely that elaborate and expensive genetic engineering methods will be applied to the willow breeding process because willow is not considered to be an important crop for the human economy [41].
A high level of biomass yield is crucial if willow is converted to energy production. However, the use of lower yielding varieties for industrial cropping is also possible. Willow is grown for various purposes, including traditional wicker products. In this case the yield of dry matter is not as crucial as the quality of the material, so short and shrubby varieties could be preferred. However, varieties often damaged by pests will be avoided. The content of active substances is another feature of the varieties, not necessarily related to the yield. In this case, varieties with a higher proportion of bark in the biomass, i.e., varieties that are vigorously shrubby, will be preferred for medicinal and cosmetic products. An important branch of the economy in the future may be natural construction, due to its growing interest. It also uses products from willow plantations in addition to other industrial crops. In this case, high yield does not have to be a key value, which should relate to the strength and durability of the obtained material [35,41].

Conclusions
This study provides an assessment of the productivity and biometric features of some of the most common willow varieties in Europe. The presented results clearly show that the key element that determines the production effects is the appropriate selection of varieties. Varieties with high production potential develop fewer shoots, but are taller and larger in diameter than other varieties. The obtained results also indicate that it is possible to effectively use marginal land for production purposes other than food production, including, for example, the cultivation of willow intended for industrial purposes.


provenance: AgriculturalAndFoodSciences,2019-2020-Agricultural And Food Sciences-2020.jsonl.gz.gz

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EXPLORATION OF THE OPTIMUM RICE HUSK BIOCHAR FOR ATRAZINE AND 2,4-D REMOVAL: DIFFERENT PYROLYSIS AND MODIFICATION CONDITIONS

In this study, the best RHB (rice husk biochar) was investigated through the effect of pyrolysis synthesis processes and modifications. Five biochars were synthesized from rice husk waste materials at different pyrolysis temperatures (400 to 600 °C) in oxygen-limited conditions. The biochars were modified by acids to remove minerals on the surface area. The characteristics of the biochars were determined including surface morphology, specific surface area, and functional groups. The herbicide adsorption was accomplished by the batch equilibration method. The result indicated that the biochar that synthesized 500 °C for 6 h had the highest maximum adsorption capacity. The optimum RHB was biochar modified with HF acid. The optimum biochar had less or no minerals and it achieved abundant functional groups on the surface areas. The pore volume distributions in pore sizes of micropores and narrow mesopores played an important role to perform the uptake of the herbicides, they were in the range of 7.90 % and 59.26 %, respectively. The high-efficiency removals of atrazine and 2,4-D by optimum biochar were 82.70 % and 95.10 %, respectively. The rice husk biochar is a suitable adsorbent to remove herbicides from the aqueous environment.

INTRODUCTION
Pesticides were used widely to raise the agriculture yield, and product value [1,2]. As a result, pesticide residues appeared in surface water where these have harmful effects [3]. Atrazine is used in agriculture as a herbicide in order to control grass and broad-leaf weed. Moreover, it is also applied in airfields, parking lots, and industrial zones [4]. Atrazine is associated with causes of cancer, including that of lymphatic system and haematopoiesis [4]. 2,4-D is a herbicide that is widely used in agriculture as an agent for weed management. Its action is selective and it is a low-priced [5]. 2,4-D is a reason to cause the hepatotoxic and nephrotoxic to animal [6]. All herbicides are harmful to humans and the environment [4,6]. The physicochemical properties of herbicides are summarized in Table 1.
Thermochemical processes that produce biochar are slow pyrolysis, fast pyrolysis, and gasification [7]. But the yield of biochar synthesized from slow pyrolysis is higher than those of the fast pyrolysis or gasification [7]. Biomass contains compounds such as cellulose, hemicellulose, and lignin, and these compounds convert to biochar Biochar is an adsorbent that is widely used in adsorption techniques. It contains a rich content of carbon after the pyrolysis process under oxygen-limited conditions. Many benefits of biochar properties are its good affinity and potential for adsorbing agrochemicals and pesticides [8,10]. Several specific characteristics of biochar consist of excellent porosity, great specific surface area, and functional groups [11]. Biomass is abundant and low-cost [12]. Hence, if they are synthesised into adsorbents, they can be valuable materials. Rice husk is a waste material occurring in large quantities, the density of rice husk is 83-125 kg.m -3 ; the amount of water that can penetrate in rice husk ranges from 5 % to 16 % of unit weights [13]. The rice husk components contain approximately 40 % cellulose, 30 % lignin group, 20 % silica, and other minerals including calcium, copper, iron, potassium, magnesium, manganese, sodium, phosphorus, and zinc [13,14]. There are many reports related to biochar (corn straw and switchgrass biochar) used in the removal of 2,4-D and atrazine [15,16], however, some of them do not report high removal efficiency, therefore, finding good biochar is necessary for application in the herbicide removal.
The aim of this study was to explore the effect of pyrolysis conditions on the mass loss of the biochar through the pyrolysis temperature. The pyrolysis process of biochar was performed under oxygen-limited conditions and was then modified by acids to remove the minerals. The methods and measurements including SEM (scanning electron microscope), BET (Brunauer-Emmett-Teller), FT-IR (Fourier-transform infrared spectroscopy), and the contact angle method were used to identify the characteristics of biochar. Comparisons are made between the atrazine adsorption capacities of the biochar modifications and to select the optimum biochar which is the highest adsorption capacity. The removal efficiencies of herbicides including atrazine, 2,4-D by optimum rice husk biochar were achieved.  [19,20] 2 MATERIAL AND METHODS

Characterization of the biochar
The scanning electron microscope (LEO 1455 VP model, Carl Zeiss Microscopy GmbH, England) was operated to examine the surface of biochar. The specific surface area of biochar was verified by the BET method (TriStar II 3020, Micromeritics Instrument Corporation, USA). The functional groups of biochar were verified by the FT-IR method (Frontier, PerkinElmer, Germany). The hydrophilic characteristic of biochar was verified by the contact angle method (OCA 20, Dataphysics OCA GmbH, Germany).

Adsorption experiment
The batch adsorption method was carried out for the adsorption of biochar and herbicides. The dosage of RHB was 1.5 g.L -1 (0.15 g of biochar was added to 100 ml of herbicide). All experiments were performed by conical flasks and Shaker (MS-NOR-30/MS-NOR-3001). The conical flasks were agitated for equilibrium time at 120 rpm in the darkness. The herbicide solutions were collected during the experiment to analyse the residual concentration by UV-Vis spectrometer (Genesys 10S UV-Vis Thermo Scientific, USA). The wavelength to analyze atrazine, 2,4-D, were 229 nm [23], 230 nm, respectively. The maximum absorbance of 2,4-D was performed in triplicate from the scanning at a range of 200-600 nm and repeated triplicate with similar results (230 nm). The stock solution of 2,4-D (1000 mg.L -1 ) was prepared from 1.25 ml (2,4-D, 80 % w/v). The stock solution of atrazine (100 mg.L -1 ) was prepared from 125 mg (atrazine (80 % w/w). Two stock solutions were performed in a 1000 ml volume flask with DI (Deionized) water. The initial concentrations of atrazine to calculate the adsorption isotherm were from 10 to 25 mg.L -1 . The concentration using in the removal efficiency of atrazine and 2,4-D were 20 mg.L -1 .
The amounts of herbicides adsorbed on biochar at time t, qt (mg.g -1 ) and at equilibrium, qe (mg.g -1 ) were followed equations as below: The percentage removal of herbicides are calculated using equation as below [5]: where Co and Ce (mg/L) are concentrations of herbicides at initial and equilibrium, respectively; W (g) is the mass of dry biochar used; V (L) is the volume of the herbicide solutions [5,15]. To evaluate the adsorption capacity of the herbicides on the RHB, Langmuir isotherm model was applied to calculate the adsorption experiment data.
Langmuir isotherms is shown as below: where Qo (mg/g) is the maximum adsorption capacity; KL (L/mg) is the Langmuir isotherm constant [15].

Effect of pyrolysis conditions on the mass loss of the biochar
The influence of the pyrolysis temperatures on the mass loss of the rice husk biochar are shown in Figure 1. In the pyrolysis process, the heating temperature is a factor that has a strong effect on the biochar yield. When increasing the temperature, the mass of the biochar decreased. At 100 to 200 °C , the mass of the biochar slightly decreased from 98.53 to 86.67 %. This was likely to be due to the loss of moisture content [24], this result agrees with previous experimental results [24]. At 300 °C , the percentage of the mass of the biochar decreased to 53.67 %, this temperature significantly influenced the loss of biochar. When the temperature was from 400 to 550 °C , the mass of the biochar decreased from 45.51 to 37.68 %. The mass of the biochar stabilized at a temperature of 600 °C (34.72 %). According to Demirbas, biomass consists of cellulose, hemicelluloses, water, lignin, simple sugars, extracts, lipids, proteins, starches, ash, hydrocarbons, and other compounds (2009) [25]. The decrease in mass is explained through the pyrolysis process, the biomass transformed into biochar, bio-oil, and syngas [8]. At high pyrolysis temperatures, the volatile material was transferred and released significantly, which changed the yield of the biochar [26]. Therefore, when the biomass is burned at high temperatures, the yield of the biochar decreased significantly.

Figure 1. Effect of the yield (%) of the mass loss of RHB by the pyrolysis temperatures in 6 h
GeoScience

Surface morphology of rice husk biochars
The SEM pictures of different RHB (modification with HCl acid) are shown in Figure 2. When the pyrolysis temperature increased from 400 to 600 °C , the pore sizes of the biochar width tended to be narrow. The width range of RHB 400 °C was approximately 8.15-12.34 µm. This was considered in [27], when biochar formed pore openings. Whereas the pores in RHB 500 °C are various sizes. The small amount of volatile matter in the biochar was slowly lost, this formed the various pore sizes at 500 °C [27]. When biochar was burned at 600 °C , the total dissolution of small parts of the volatile matter that remained in the biochar was achieved. The RHB showed that the pores on the surface of the biochar are longitudinal in shape, which indicates that RHB can take up pollutants by a pore-filling mechanism.

Comparison of the RHB by the Qo (maximum adsorption capacity)
The results of the pyrolysis temperature affected on the Qo of RHB are shown in Figure 3. The Qo increased from 10.10 to 14.00 mg.g -1 , when biochar was synthesized from 400 to 500 °C for 6 h (Figure 3). This explains that the increasing of the pyrolysis temperature due to enhancement of the specific surface area (SSA) [26]. The adsorption ability of biochar depended on the total pore volume (TPV) and SSA significantly [28]. However, the pyrolysis process was performed from 550 to 600 °C in 6 h, the Qo of biochar decreased from 11.20 to 9.50 mg.g -1 . Probably, at 550 to 600 °C, the SSA of biochar could decrease due to the high temperature leading to the change in form of the pores of the biochar [8]. This caused the reduction in the Qo of RHB that synthesized at 550 to 600 °C in 6 h. High temperatures and prolonged holding time could cause a decrease in the SSA and Qo [8].

Comparison of the adsorption capacity of RHB 500 °C -6 h -modified with many types of acid
The adsorption capacity of non-modified and modified RHB (acid modification) is shown in Figure 4. The results indicate that the capacities of RHB to absorb atrazine were higher if the biochars were modified with acid. The minerals blocked the pores of the biochar, impeding the transfer of atrazine into the pores. The adsorption capacity of RHB modified by HF acid was observed to be the highest. This is clarified by the ash composite of the biochar. The Si element was 87.00 % and a main component of ash in rice husk biochar, the residual components were Ca (2.0 %), P (0.9 %) and K (9.0 %) [29]. The Si ash of RHB was significantly removed by HF modification, this reports that the percentages of Si ash of RHB that synthesized at 450 °C and 600 °C were 80.00 % and 85.64 %, respectively [30]. The removal of Si from RHB by HF acid was due to HF reacting to Si creating SiF4. HF acid can also react with K on the surface of the biochar [31]. HF acid is able to corrode glass, as the fluoride anions can react with silicon molecules which exist in glass thus damaging glass [32]. Therefore, the main ash of RHB including Si and K was removed by HF acid, which brought about the highest adsorption capacity for biochar.

Characterization of the optimum RHB
The SEM (scanning electron microscope) picture of acid-modified biochar is shown in Figure 5. The biochar with acid modifications presented fewer or no minerals, allowing higher adsorption capacity. The specific surface area (SSA) and total pore volume (TPV) of the RHB were 153.27 m 2 .g -1 and 0.055 cm 3 .g -1 , respectively. If the large specific surface area exists on the surface of the biochar, it has more interfaces for pollutant adsorption [15]. The pore size of the biochar played an important role in pore-filling adsorption. The pore volume distributions (%) in pore sizes of micropores (< 2 nm) and narrow mesopores (2-20 nm) were in the range of 7.90 % and 59.26 %, respectively. The pore size of the biochar is very important in performing the uptake Figure 6 shows the FT-IR analysis of the optimum RHB. The main functional groups of the RHB comprised the ketone, carbonyl, and aromatic organic molecules and are unsaturated hydrocarbons. The hydroxyl group is an abundant molecule on the surface of RHB and can bring about H-bonding interaction between RHB and pollutants [34]. In addition, the aromatic group of the RHB can generate the - EDA interaction [34] and the alkyl group can engender the hydrophobic bonding [35].

The removal efficiency of optimum RHB
The removal efficiency of herbicides by the optimum RHB are shown in Figure 7. The removal of atrazine, 2,4-D were removed at a high percentage and were 82.70 and 95.10 %, respectively. The removal efficiency of atrazine (82.70 %) in our study is higher than those (12.00 %) of Tan et al.'s study, 2016 (the atrazine concentration: 10 mg.L -1 , the dose of corn straw biochar: 33.3 g.L -1 ) [15]. For the removal efficiency of 2,4-D, this is similar to the study results of Essandoh et al., 2017 (90 % removal, the 2,4-D: 40 mg L -1 , the dose of switchgrass biochar: 1.0 g.L -1 ) [16]. The removals of herbicides are related to the adsorption mechanism between biochar and herbicides. Based on the SEM, BET, and FT-IR of the optimum RHB and molecular structure of atrazine and 2,4-D, the suggestion of the adsorption mechanism is the pore-filling, electrostatic, hydrophobic bonding, H-bonding, π-π EDA interactions [12].

CONCLUSION
This study explored the best rice husk biochar from different pyrolysis conditions and modifications. When the biomass is burned at high temperatures, the yields of the biochar decreased significantly. The optimum biochar from different pyrolysis conditions is biochar that was synthesized at 500 °C for 6 h. The optimum biochar was observed when it was modified with HF acid. The optimum biochar had less or no minerals, and it achieved the abundant function groups on the surface areas. The pore volume distributions (%) in pore sizes of micropores and narrow mesopores were in the range of 7.90 % and 59.26 %, respectively, and were an essential factor to adsorb the herbicides. The removal efficiency of the optimum biochar for atrazine and 2,4-D is very high in the range of 82.70 to 95.10 %. From the results of this study, the rice husk biochar is an advantageous adsorbent to remove herbicides from the aqueous environment.


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Determining Soil Hydraulic Properties Using Infiltrometer Techniques: An Assessment of Temporal Variability in a Long-Term Experiment under Minimum- and No-Tillage Soil Management

Conservation agriculture is increasingly accepted by farmers, but the modeling studies on agro-environmental processes that characterize these agricultural systems require accurate information on the temporal variability of the soil’s main physical and hydraulic properties. Therefore, specific investigations carried out in long-term experiments can increase our knowledge on the pros and cons of different measurement techniques. In this work, the simplified falling head (SFH) technique and the Beerkan Estimation of Soil Transfer (BEST) procedure were applied to investigate the temporal variability of some main soil physical and hydraulic properties, including bulk density (BD), field saturated hydraulic conductivity (Kfs), macroporosity (Pmac), air capacity (AC), plant available water capacity (PAWC), and relative field capacity (RFC). For this purpose, a long-term experiment was selected, and the experimental information obtained was used to verify the long-term impact (fifteen years) determined by two alternative forms of soil management, minimum tillage (MT) and no tillage (NT), for the cultivation of durum wheat. The main results of the comparison between MT and NT showed: (i) A comparable temporal variability in Kfs values when SFH was considered, given that in 75% of considered cases, the same result (i.e., significant or not significant) was obtained; (ii) a comparable temporal variability in Kfs values under MT (but not under NT) when both SFH and BEST were used, as a result of a possible Kfs,SFH overestimation under NT; (iii) differences in Kfs estimations by a factor of 6 or 7 (Kfs,SFH > Kfs,BEST) when comparable soil conditions (moisture and soil density) were considered; (iv) a comparable temporal variability when other soil indicators (BD, Pmac, AC, PAWC, and RFC) were simultaneously considered. After about fifteen years of field experiments characterized by continuous soil management and conducted with the methodological rigor typical of experimental farms, the SFHand BEST-derived experimental information showed a substantial equivalence between MT and NT for the fine-textured soil investigated.

Introduction
The soil hydraulic properties (i.e., water retention and hydraulic conductivity) are key information in many fields of agro-environmental research works, since they are necessary, for example, for simulating a few agro-environmental processes [1][2][3], for establishing agronomic adaptation strategies under climate change [4][5][6], and for quantifying economic and environmental impacts of alternative soil management [7][8][9][10].
Many techniques are available in the literature for obtaining the hydraulic soil properties that differ in accuracy, specific skills for their application, and experimental efforts (costs and execution times). Some techniques provide discrete values of soil properties, e.g., soil water retention at field capacity or saturated hydraulic conductivity, while other techniques can generate continuous hydraulic property data. For instance, the evaporation method is widely used because it provides accurate estimations of soil hydraulic properties [11], but requires sampling of undisturbed soil cores and relatively laborious and time-consuming lab procedures. Consequently, the use of simplified and expeditious methods is increasingly frequent, and the choice of the most appropriate one depends on specific experimental needs and economic availability.
The simplified falling head (SFH) technique by Bagarello et al. [12] allows the estimation of the field saturated hydraulic conductivity (K fs ) in a sufficiently accurate and repeatable way with relatively limited experimental effort. For these potentialities, it has been widely used to obtain K fs in specific agro-environmental investigations on both herbaceous [13] and arboreal [14] agronomic crops. A literature review also revealed that the SFH technique was successfully applied to investigate the influence of soil compaction on water infiltration rate due to cattle hooves or tractors [15], to assess soil hydraulic quality factors and develop a comprehensive soil quality index under different cropping systems [16], to study the soil physical quality response to sugarcane expansion in Brazil [17], and to investigate the impact of herbicide glyphosate on compost-amended soils [18].
The Beerkan Estimation of Soil Transfer (BEST) parameters procedure by Lassabatère et al. [19] is an attractive, easy, robust, and inexpensive way for a "complete" soil hydraulic characterization, since it allows the simultaneous determination of the water retention curve, h(θ), and the hydraulic conductivity function, K(θ) [20]. The BEST method has shown reliability and robustness based on several theoretical and experimental evaluations; consequently, in recent years, it was applied for soil property estimation in agro-environmental sustainability investigations [21][22][23][24][25]. For instance, BEST was applied by Yang et al. [21] in a karst landscape of southern China to investigate the effects of Napier grass management on soil hydrological functions and to identify reasonable strategies for maintaining optimal soil hydraulic functions. Yang et al. [22] used this method to establish the impact of different vegetation restoration types on K fs within a large global ecological restoration engineering project. Likewise, the impact of native, spontaneous, or exotic vegetation on soil hydraulic properties was assessed by BEST in areas of France [23] and Brazil [24]. Di Prima et al. [25] applied BEST to investigate the soil physical quality of a Spanish orchard under three different soil management types, i.e., no-tillage using herbicides, conventional tillage under chemical farming, and no-tillage under organic farming. The results showed that independent and BEST-derived indicators (i.e., soil bulk density, total organic carbon, and structure stability index, in conjunction with plant available water, relative field capacity, and saturated hydraulic conductivity) yielded similar information, suggesting their ability to distinguish soil qualities among contrasting soil management types.
Soil hydraulic properties are highly variable in space and time [26][27][28], and several sampling times are needed to investigate the seasonal, or temporal, variability during a crop cycle, or between sampling dates of different cropping years [29]. Overall, there is a wide literature on the temporal variability of soil physical and hydraulic properties, as well as on the effects induced by the practices applied in agriculture. For instance, Castellini et al. [29] investigated the seasonal variability of the water retention curve and the relative field capacity of the soil under different soil management types (i.e., no-tillage, minimum tillage, and incorporation into the soil of wheat straw). The results showed that there was no seasonal variability from about February until June, suggesting that four or five months Sustainability 2020, 12, 5019 3 of 18 are the minimum times required after the last soil disturbance to reach near-optimal physical quality of the soil. Kreiselmeier et al. [28] studied the temporal changes of soil hydraulic conductivity (both saturated and unsaturated) under conventional and conservation tillage. The findings showed a high temporal variability of K fs in tilled soils (lower under unsaturated soil condition) and a greater stability of K fs in untilled soils, confirming the susceptibility of tilled soils to variations, also in the short term. However, to the best of our knowledge, although the literature on this topic is very extensive, specific investigations aimed at comparing the temporal variability estimated by two different infiltrometric techniques have not yet been carried out. Therefore, the two cited techniques (SFH and BEST) could be applied simultaneously to investigate the seasonal (or temporal) variability of specific soil management and to evaluate the respective pros and cons.
The no-tillage of the soil, as compared with traditional plowing or various options of minimum tillage, has received growing interest in cereal cropping systems in southern Italy. This is true especially for wheat cultivation, since no tillage is increasingly considered as a real alternative to soil tillage [30]. However, according to a hydrological perspective aimed at assessing the impact of these two soil management systems on soil hydrology and water conservation, appropriate case studies should be shared in order to generalize, to a certain extent, the information obtained [28,31,32].
Long-term field experiments, carried out in the experimental farms of the research institutes or universities, represent important research tools to assess, for example, the temporal variability of selected soil properties given that (i) relatively stable conditions can be expected in these soils and (ii) the cultivation operations are carried out with scientific rigor [31,33,34]. Consequently, these factors provide the basis to obtain robust experimental information.
The general objective of this investigation was to assess the temporal variability of physical and hydraulic properties of a fine-textured soil in a long-term experiment (about fifteen years) for durum wheat cultivation under minimum tillage (MT) and no tillage (NT). The specific objectives were to: (i) Apply the SFH and BEST methods to obtain the main physical and hydraulic properties of the soil (i.e., bulk density, saturated hydraulic conductivity, macroporosity, air capacity, plant available water capacity, relative field capacity); (ii) investigate the temporal variability obtained by the selected methods; (iii) compare the estimations obtained by SFH and BEST; (iv) use the acquired experimental information to assess the agro-environmental sustainability of MT and NT from the perspective of soil physical and hydraulic properties.

Experimental Site
The study was carried out in the two-year period of 2016-2017 at the experimental farm of the Council for Agricultural Research and Economics, CREA-AA, Foggia (41 • 27 03" N, 15 • 30 06" E), in a long-term field experiment performed on a monoculture of durum wheat (Triticum durum, Desf.). The study, started in 2002, aims at comparing the effects of tillage (minimum tillage, MT) and sod-seeding (no tillage, NT) on grain yield and quality. The experimental design is a completely randomized block design with three replicates and unit plots of 500 m 2 size (Figure 1a). Straw was chopped into 10-15 cm lengths and spread back on the plot in September (first week); this resulted in about 40-50 q/ha of organic matter returned to the soil (Figure 1b). A two-layer tillage (field cultivator with rotary tiller) was carried out in the MT plots (late October-early November). For the NT plot, chemical weed control and direct sowing were carried out. Sowing was performed in early November. According to the USDA classification, the soil texture is that of clay, with 42.7% and 27.7% of clay and silt, respectively [32]. As an example, an image of the experimental site is shown in Figure 1.

Simplified Falling Head (SFH) Technique
The SFH technique [12] consists of quickly pouring a known volume of water, V (L 3 ), onto the soil confined inside a ring inserted at a fixed depth into the soil, d (L), and measuring the time, ta (T), necessary for the poured volume, Δθ (equal to the volume of water obtained by difference between the soil water content at saturation, θs, and that at the time of the experiment, θi), to fully seep through to the surface area, A (L 2 ). In other words, given a value of the water content at the beginning of the experiment, Δθ represents the amount of water needed to saturate the soil porosity estima ted at the time of sampling. The Kfs values were determined by a one-dimensional, transient, falling-head infiltration process, based on the analysis by Philip [35]: where D = V/A (L) is the depth of water corresponding to V, and α* (L −1 ) is a soil texture/structure parameter. According to the procedure reported by Bagarello et al. [12], two undisturbed soil cores (5 cm in height by 5 cm in diameter) were collected near the ring at the 0 to 5 and 5 to 10 cm depth about 24 h before the measurements to determine the dry soil bulk density, BD (g cm 3 ), and the soil water content at the time of sampling, θi (cm 3 cm −3 ), in the laboratory. The saturated soil water content, θs, was estimated using the measured BD and considering a mean value of soil particle density of 2.65 g cm −3 , as is usual for this procedure [12,36]. A choice of the most suitable α* parameter can be made following the guidelines of Elrick and Reynolds [37]. However, according to previous experimental investigations carried out on investigated soil [38], a value of the α* equal to 0.012 cm −1 was used to calculate Kfs.
The SFH technique was applied in two consecutive years (growing season 2015-2016 and 2016-2017) to determine the field saturated soil hydraulic conductivity, Kfs,SFH. In particular, the SFH tests started about five months after pipe insertion; this made it possible to exclude (or attenuate) the possible compaction during cylinder insertion. Five sampling dates were considered in 2016 and another three in 2017 ( Figure 2). For each considered agronomic treatment (MT and NT), ten PVC

Simplified Falling Head (SFH) Technique
The SFH technique [12] consists of quickly pouring a known volume of water, V (L 3 ), onto the soil confined inside a ring inserted at a fixed depth into the soil, d (L), and measuring the time, t a (T), necessary for the poured volume, ∆θ (equal to the volume of water obtained by difference between the soil water content at saturation, θ s , and that at the time of the experiment, θ i ), to fully seep through to the surface area, A (L 2 ). In other words, given a value of the water content at the beginning of the experiment, ∆θ represents the amount of water needed to saturate the soil porosity estimated at the time of sampling. The K fs values were determined by a one-dimensional, transient, falling-head infiltration process, based on the analysis by Philip [35]: where D = V/A (L) is the depth of water corresponding to V, and α* (L −1 ) is a soil texture/structure parameter. According to the procedure reported by Bagarello et al. [12], two undisturbed soil cores (5 cm in height by 5 cm in diameter) were collected near the ring at the 0 to 5 and 5 to 10 cm depth about 24 h before the measurements to determine the dry soil bulk density, BD (g cm 3 ), and the soil water content at the time of sampling, θ i (cm 3 cm −3 ), in the laboratory. The saturated soil water content, θ s , was estimated using the measured BD and considering a mean value of soil particle density of 2.65 g cm −3 , as is usual for this procedure [12,36]. A choice of the most suitable α* parameter can be made following the guidelines of Elrick and Reynolds [37]. However, according to previous experimental investigations carried out on investigated soil [38], a value of the α* equal to 0.012 cm −1 was used to calculate K fs . The SFH technique was applied in two consecutive years (growing season 2015-2016 and 2016-2017) to determine the field saturated soil hydraulic conductivity, K fs,SFH . In particular, the SFH tests started about five months after pipe insertion; this made it possible to exclude (or attenuate) Sustainability 2020, 12, 5019 5 of 18 the possible compaction during cylinder insertion. Five sampling dates were considered in 2016 and another three in 2017 ( Figure 2). For each considered agronomic treatment (MT and NT), ten PVC pipes of 30 cm in inner diameter and 30 cm in height (with relatively sharp edges) were inserted into the soil (in mid-November) at a depth d = 15 cm. Using the bucket loader of a tractor, the insertion of the PVC pipe was conducted step by step, ensuring that the upper rim of the pipe remained horizontal. A spirit level was used to check the horizontality of the cylinder. To obtain a flat infiltration surface, the small depressions of the cylinder's inner area were filled with soil collected in situ. The PVC pipes were always removed immediately before the start of the next crop cycle (at the beginning of November).
5 pipes of 30 cm in inner diameter and 30 cm in height (with relatively sharp edges) were inserted into the soil (in mid-November) at a depth d = 15 cm. Using the bucket loader of a tractor, the insertion of the PVC pipe was conducted step by step, ensuring that the upper rim of the pipe remained horizontal. A spirit level was used to check the horizontality of the cylinder. To obtain a flat infiltration surface, the small depressions of the cylinder's inner area were filled with soil collected in situ. The PVC pipes were always removed immediately before the start of the next crop cycle (at the beginning of November).

Beerkan Estimation of Soil Transfer (BEST) Parameters Procedure
The BEST procedure [19] was applied to simultaneously determine the water retention curve, h(θ), and the soil hydraulic conductivity function, K(θ), of the soil. BEST focuses specifically on the van Genuchten [39] relationship with the Burdine [40] condition for the water retention curve (Equation (2a)) and the Brook and Corey [41] relationship for hydraulic conductivity (Equation (3a)): where θ (L 3 L −3 ) is the volumetric soil water content, h (L) is the soil water pressure head, K (L T −1 ) is the unsaturated soil hydraulic conductivity, n (>2), m, and η are shape parameters, p is a tortuosity parameter set equal to 1 following Burdine's [40] condition, and hg (L), θs (field saturated soil water content; L 3 L −3 ), θr (residual soil water content; L 3 L −3 ), and Kfs,BEST (field saturated hydraulic conductivity; LT −1 ) are scale parameters. In BEST, θr is assumed to be equal to zero. Shape parameters, which are texture-dependent, are estimated from particle size analysis and soil bulk density Apr-16

Beerkan Estimation of Soil Transfer (BEST) Parameters Procedure
The BEST procedure [19] was applied to simultaneously determine the water retention curve, h(θ), and the soil hydraulic conductivity function, K(θ), of the soil. BEST focuses specifically on the van Genuchten [39] relationship with the Burdine [40] condition for the water retention curve (Equation (2a)) and the Brook and Corey [41] relationship for hydraulic conductivity (Equation (3a)): is the volumetric soil water content, h (L) is the soil water pressure head, K (L T −1 ) is the unsaturated soil hydraulic conductivity, n (>2), m, and η are shape parameters, p is a tortuosity parameter set equal to 1 following Burdine's [40] condition, and h g (L), θ s (field saturated soil water content; L 3 L −3 ), θ r (residual soil water content; L 3 L −3 ), and K fs,BEST (field saturated hydraulic conductivity; LT −1 ) are scale parameters. In BEST, θ r is assumed to be equal to zero. Shape parameters, which are Sustainability 2020, 12, 5019 6 of 18 texture-dependent, are estimated from particle size analysis and soil bulk density measurement by assuming a shape similarity between the particle size distribution and the water retention curve [42]. To obtain the shape parameters of soil hydraulic functions, the particle size distribution (PSD) of the soil is fitted to the following equations: where F(D) is the fraction by mass of particles passing a particular diameter, D (L), M and N are two shape parameters, and D g (L) is a scale parameter. The fitting of Equation (4a) provides an estimation of parameters M, N, and D g . The shape index of the medium, p m , can be obtained from M and N: where the coefficient κ is equal to: in which s is the fractal dimension of the medium, defined as the root of the following equation: ( The m parameter is derived from the values of p m considering the positive root of Equation (5b): To estimate the field saturated hydraulic conductivity, K fs,BEST , experimental data from Beerkan infiltrations and the approximations of the quasi-exact implicit infiltration model by Haverkamp et al. [26,43] were used. Specifically, when an infiltration experiment with a zero-water-pressure head on a circular surface of radius r (L) is performed under homogenous conditions of θ i (L 3 L −3 ), the 3D cumulative infiltration, I (L), and the infiltration rate, I (LT −1 ), can be approached by the following explicit transient (Equation (9a,b)) and steady-state (Equation (9c,d)) relationships [26,43]: where I s and i s are respectively the asymptotic models for I and i for large times, t is the time (T), and A (L −1 ), B, and C are constants that, for the Brooks and Corey model [41] model, may be obtained as: where β and γ are coefficients that are usually set equal to 0.6 and 0.75, respectively, when θ i < 0.25 θ s [26,43]. The scale parameter for the van Genuchten water retention curve, h g [L], is estimated by the following relationship: where S (LT -1/2 ) is the soil sorptivity and c p is a coefficient dependent on n, m, and θ according to Equation (6b) by Lassabatère et al. [19]. Three alternative algorithms were developed to analyze field infiltration data, i.e., BEST-slope [19], BEST-intercept [44], and BEST-steady [26]. The BEST-slope by Lassabatere et al. [19] makes use of the following relationships for both K fs,BEST and cumulative infiltration, I(t): where i s is estimated by linear regression of the last data points describing steady-state conditions on the I vs. t plot and corresponds to the slope of the regression line. Equation (12b) is fitted to the experimental data to estimate soil sorptivity, S. According to Yilmaz et al. [44], the BEST-slope may lead to erroneous saturated hydraulic conductivity values when i s ≈ AS 2 . Therefore, these authors [44] proposed the BEST-intercept and suggested using the asymptotic expansion I + ∞(t) [26,44] to estimate the saturated hydraulic conductivity and the cumulative infiltration by the following relationship: where b s [L] is the intercept of the asymptotic expansion in Equation (9c). Both the slope and the intercept values are required by BEST-steady [26], which does not need data fitting for the transient stage of the run, but relies solely on the steady-state condition. Consequently, for the purpose of comparing BEST and SFH, only the BEST-slope and BEST-intercept were considered, while BEST-steady was not used in this investigation. BEST was applied on three sampling dates ( Figure 2). For each soil management (MT, NT) and sampling date (1, 6, 7), twelve BEST experiments were carried out to obtain a complete hydraulic characterization of the soil, with the exception of the sampling date at the end of March (MT6-NT6, Figure 2), for which the sample size was five. Therefore, a total of fifty-eight BEST experiments were carried out in this investigation. Following a procedure commonly used for BEST experiments, two undisturbed soil cores (0.05 m in height by 0.05 m in diameter) were collected at the 0 to 5 and 5 to 10 cm depth at each randomly selected sampling point. These soil cores were used to obtain the Sustainability 2020, 12, 5019 8 of 18 dry soil bulk density, BD, as well as to obtain the soil water content at the time of sampling, θ i . The field saturated soil water content, θ s , was assumed to coincide with soil porosity, ϕ [26], which was calculated from the BD data, assuming a soil particle density of 2650 kg m −3 . Moreover, the default parameters of the infiltration constants, i.e., β = 0.6 and γ = 0.75, were used to estimate soil hydraulic functions [19,20]. An updated version of the workbook by Di Prima [45], which considers all the available algorithms, was used to analyze the cumulative experimental infiltrations, I(t), using the two considered BEST-algorithms (slope and intercept).
Finally, the water retention curve estimated by BEST was used to calculate (i) macroporosity (P mac ; cm 3 cm −3 ), (ii) air capacity (AC; cm 3 cm −3 ), (iii) plant available water capacity (PAWC; cm 3 cm −3 ), and (iv) relative field capacity (RFC; dimensionless) of the soil [29]. Specifically, for each estimated soil water retention curve, the above capacitive indicators were calculated as follows: where θ s , θ m , θ FC , and θ PWP are the volumetric water contents corresponding to a pressure head of 0, 10, 100, and 15,300 cm, respectively [29]. According to Reynolds et al. [46], the aforementioned water pressure head value (i.e., 100 cm) was considered to obtain the field capacity of several clay soils in agricultural [47,48] and forest [49] environments.

Data Analysis and Comparisons
For each main variable considered in this investigation (BD, θ i , K fs , P mac , AC, PAWC, RFC), a given dataset was summarized by calculating the mean and the associated coefficient of variation. Arithmetic means were always calculated with the exception of K fs because a log-normal distribution and, therefore, a geometric mean and associated coefficient of variation are generally suggested for these soil properties [50].
Temporal variability was evaluated for each variable (i.e., BD, K fs , P mac , AC, PAWC, RFC) by comparing the mean values obtained from Tukey's Honestly Significant Difference (THSD) test [51], whereas the statistical significance between NT and MT was evaluated according to a two-tailed t-test. A probability level of p = 0.05 was assumed.

Temporal Variability of Soil Properties Using the SFH Technique
The results of soil bulk density (BD) and volumetric soil water content at the time of SFH experiments (θ i ) under MT and NT are reported in Figures 3 and 4, respectively. Overall, higher BD values were observed under NT than MT, as, on average, they varied in the range (min-max) of 0.98-1.19 g cm −3 under MT and 1.19-1.32 g cm −3 under NT. On five of the eight sampling dates, BD values under NT were significantly higher than under MT (Figure 3). A lack of temporal variability was observed for BD because significant differences were identified only under MT between sampling dates 2 and 3 (i.e., between the beginning of May and July). Overall, detecting some discrepancies of BD values at the beginning of the crop cycle is quite expected as a result of the residual effects of the last soil tillage; however, a comparison with bulk densities of April 2016 suggests possible underestimation in May under MT due to unknown factors that make them difficult to assess. Soil water contents were higher under NT (0.28-0.41 cm 3 cm −3 ) than under MT (0.19-0.37 cm 3 cm −3 ) (Figure 4). Specifically, θ i values showed significant differences between MT and NT on four dates (i.e., 3, 5, 7, and 8), namely between the end of spring and the beginning of autumn (Figure 4). Moreover, compared to BD, a Sustainability 2020, 12, 5019 9 of 18 relatively higher temporal variability was identified because the θ i value of sampling date 7 was significantly lower than those of other dates, i.e., 1, 2, 4 and 5. This suggests that under MT/NT alters θ i more than BD over time due to the different soil management. 9 4). Specifically, θi values showed significant differences between MT and NT on four dates (i.e., 3, 5, 7, and 8), namely between the end of spring and the beginning of autumn (Figure 4). Moreover, compared to BD, a relatively higher temporal variability was identified because the θi value of sampling date 7 was significantly lower than those of other dates, i.e., 1, 2, 4 and 5. This suggests that under MT/NT alters θi more than BD over time due to the different soil management.  Field saturated hydraulic conductivity (Kfs,SFH) changed over time within two or three orders of magnitude when the minimum-maximum interval or the geometric mean was considered, respectively (Table 1). Kfs,SFH was generally higher under MT than under NT (by a factor of 1.2-3.0); however, NT was higher on two occasions (sampling dates 1 and 5) by a factor of 1.2-3.8 (Table 1). According to a two-tailed t-test, the differences between MT and NT were significant in the first two and the last two sampling dates (Table 1). According to the THSD-test, which provides comparisons across all sampling dates (i.e., 28   9 4). Specifically, θi values showed significant differences between MT and NT on four dates (i.e., 3, 5, 7, and 8), namely between the end of spring and the beginning of autumn ( Figure 4). Moreover, compared to BD, a relatively higher temporal variability was identified because the θi value of sampling date 7 was significantly lower than those of other dates, i.e., 1, 2, 4 and 5. This suggests that under MT/NT alters θi more than BD over time due to the different soil management.  Field saturated hydraulic conductivity (Kfs,SFH) changed over time within two or three orders of magnitude when the minimum-maximum interval or the geometric mean was considered, respectively (Table 1). Kfs,SFH was generally higher under MT than under NT (by a factor of 1.2-3.0); however, NT was higher on two occasions (sampling dates 1 and 5) by a factor of 1.2-3.8 (Table 1). According to a two-tailed t-test, the differences between MT and NT were significant in the first two and the last two sampling dates (Table 1). According to the THSD-test, which provides comparisons across all sampling dates (i.e., 28   Field saturated hydraulic conductivity (K fs,SFH ) changed over time within two or three orders of magnitude when the minimum-maximum interval or the geometric mean was considered, respectively (Table 1). K fs,SFH was generally higher under MT than under NT (by a factor of 1.2-3.0); however, NT was higher on two occasions (sampling dates 1 and 5) by a factor of 1.2-3.8 (Table 1). According to a two-tailed t-test, the differences between MT and NT were significant in the first two and the last two sampling dates (Table 1). According to the THSD-test, which provides comparisons across all sampling dates (i.e., 28 possible combinations), the results of the temporal variability showed that K fs,SFH was significantly different according to the different sampling dates in 60% of the considered cases (i.e., 17 of the 28 cases) under MT and 64% (18/28) under NT (Table 2). This confirms a relatively high temporal variability of field saturated hydraulic conductivity. The temporal dynamics of K fs,SFH between MT and NT were quite similar because the results of the THSD-test were simultaneously significant or not significant in 75% of the cases (i.e., 21 out of 28 cases) ( Table 2). However, although θ i values were generally comparable under MT and NT and their ratio was close to one (i.e., 0.9), discrepancies in K fs values were detected according to the soil use, as only 3-5 sampling dates (2, 3, 6, 7, and 8) showed a ratio of K fs,SFH values close to 1. A further deepening about the similarities among the aforementioned sampling dates (i.e., applying a cluster analysis) suggests three main groups (namely 4, 1 and 5, and all the remaining groups), but no link to the month of the year was found.

Temporal Variability of Soil Properties Using the BEST Procedure
Between the selected algorithms, only the BEST-intercept provided an adequate number of positive outcomes (i.e., 100% of the reps were analyzable and provided a complete soil hydraulic characterization), as the BEST-slope returned a K fs,BEST estimation only in less than 20% (12/58) of the cases. The failures were due to the inability to correctly estimate the soil sorptivity, and negative estimations of K fs,BEST were obtained. With the exception of two infiltration tests for which the two calculation algorithms provided discrepancies greater than one order of magnitude (i.e., a factor of 16 or 22), for ten of the twelve estimates obtained, K fs,BEST differed at most by a factor of 3 or 4. Therefore, since only the BEST-intercept provided an adequate sample size, this algorithm was used to investigate the temporal variability of estimated soil properties and to establish the comparison in terms of K fs between the BEST and SFH techniques.
The results of K fs,BEST are reported in Table 3. Overall, relatively higher discrepancies between minimum and maximum values were detected under NT (within a factor of 4-34) than under MT (factor of 5-13); consequently, the associated variability was about double under NT (Table 3). Similarly to the results obtained by SFH, higher K fs,BEST values were always detected, on average, under MT than NT, with differences equal to a factor of 15.8, 3.5, or 1.2 in the sampling dates 1, 6, and 7, respectively. According to a two-tailed t-test, the differences were significant only for sampling date 1. Moreover, due to the relatively high K fs,BEST value for MT-1, temporal changes were detected only under MT (Table 3). Table 3. Minimum (Min), maximum (Max), geometric mean (GM), and associated geometric coefficient of variation (CV) of field saturated hydraulic conductivity (mm h −1 ) carried out under MT and NT during each sampling date with the BEST-intercept. * For a given sampling date, mean values of alternative soil management types (i.e., MT and NT) followed by the same lowercase letter are not statistically different according to a two-tailed t-test (p = 0.05). For a given soil management type, mean values of different sampling dates (i.e., 1, 6, and 7) followed by the same capital letter are not statistically different according to a THSD-test (p = 0.05).

Minimum Tillage (MT)
The BEST-derived soil water retentions are depicted in Figure 5a. Differences between MT and NT or between sampling dates were mainly located near to soil saturation, while they were relatively smaller in the unsaturated zone. This was because the BEST procedure considers a starting point of the water retention curve (i.e., the saturated soil water content) that depends on bulk density measured at the time of the experiment; also, in this area, the effect of soil structure estimated from Beerkan infiltration is higher. Consequently, differences in water retention between MT and NT decreased as soil pressure head increased, changing in the range of about 0.02-0.10 cm 3 cm −3 at saturation and not exceeding 0.02 cm 3 cm −3 between field capacity and wilting point (Figure 5b). For a given sampling date, a sensitivity analysis performed by running the BEST-intercept again with the same mean BD value for MT and NT (i.e., they differed only in the Beerkan infiltration) suggested that the effects of soil structure, as compared to soil texture, should be relevant (structure >> texture) until the field capacity; beyond this threshold, the effect of soil texture should become proportionally increasingly relevant (structure << texture). Furthermore, this suggests that the temporal variation in plant available water may have been negligible (Figure 5b).
1 Figure 5. Soil water retention curves under minimum tillage and no tillage (MT and NT, respectively) in the three sampling dates considered (first, sixth, and seventh) (a), and corresponding differences in soil water retention (Δθ) as a function of soil pressure head (h) (b). Dotted lines represent θ values at field capacity and the permanent wilting point, and the dashed line represents an estimated mean threshold value corresponding to a mean value of bulk density between different soil management types. Figure 7. Differences of field saturated hydraulic conductivity, Kfs, as a function of corresponding differences in gravimetric soil water content, Ui (a), and soil bulk density, BD (b), carried out with SFH and BEST (i.e., SFH/BEST). Units are given in: mm h -1 , g g -1 , and g cm -3 , respectively for Kfs, Ui, and BD. R²  The temporal variabilities of macroporosity (P mac ), air capacity (AC), plant available water capacity (PAWC), and relative field capacity (RFC) under MT and NT are shown in Figure 6. Overall, with the exception of PAWC, which was quite constant between sampling dates (about 0.25 cm 3 cm −3 ), mean values of P mac and AC varied in the ranges of 0.01-0.07 and 0.15-0.28 cm 3 cm −3 , respectively, whereas RFC was in the range of 0.59-0.72 ( Figure 6). In addition, MT always showed the highest average soil porosity (i.e., lower RFC values indicate greater air capacity), whereas NT always showed the lowest one (similarly, higher RFC values indicate higher soil compaction). Among indicators and management of the soil considered in this investigation, relatively greater discrepancies between dates were generally detected for AC, whereas the smaller discrepancies were for PAWC. Moreover, depending on the considered soil management type, the results showed a temporal variability that was (i) equal between P mac and RFC and (ii) opposite between AC and PAWC ( Figure 6). Specifically, MT has always shown significant differences between sampling dates (7/12 or 58% cases), with the exception of PAWC (were never significant) or for P mac and RFC between MT6 and MT7; NT showed significant differences in fewer cases (4/12 or 33% cases), always involving the first sampling date ( Figure 6). Finally, according to the reference values of the literature [29,46], the mean values of soil indicators suggest a worsening of soil physical quality moving from the sampling dates 1 to 6 to 7. Regardless of statistical significance, however, this general trend over time (i.e., decreasing for P mac and AC or increasing for RFC) was more evident under NT, since MT showed some inconsistencies.

Comparison between the BEST and SFH Techniques
The results of saturated hydraulic conductivity obtained by SFH and BEST were described by the ratio K fs,SFH /K fs,BEST , which showed differences within a factor of 0.1-12.8 ( Figure 7). Specifically, the aforementioned ratio was higher than one (K fs,SFH > K fs,BEST ) for four out of six considered cases, while it was substantially lower than one or equal to one (K fs,SFH ≤ K fs,BEST ) in the remaining cases. According to the latter, however, a ratio K fs,SFH /K fs,BEST of less than one was also found in a recent study [52], where clayey soils were also considered. Soil disturbance due to ring insertion or anisotropy of K fs was suggested as the main cause for the underestimation obtained with SFH. For each sampling date, Figure 7 shows the ratio K fs,SFH /K fs,BEST as a function of the corresponding differences in gravimetric water content (Figure 7a), or as a function of the corresponding differences in soil bulk density (Figure 7b). Regardless of the moderate or strong correlation highlighted when U i and BD were considered, findings from the linearized exponential function suggest that discrepancies in K fs by a factor of 6 or 7 may be hypothesized between SFH and BEST when comparable BD or U i values (i.e., relative difference equal to 1) were detected in the field (Figure 7). According to a t-test (p = 0.05), K fs,SFH estimations were significantly higher, with the exception of NT1-MT1. This result could be expected because, although using stable cylinders can avoid possible underestimations due to the lack of soil compaction, K fs,SFH overestimations cannot be excluded during the experiments due to possible preferential water flow (i.e., subsurface cracks, or along the edge of the cylinder). On the contrary, these measurement errors can be avoided by BEST because the ring is superficially inserted into the soil. Therefore, establishing comparisons between methods for field saturated hydraulic conductivity estimation is crucial for quantifying the accuracy of simplified methodologies. However, both methods provided the same information on the temporal variability of K fs under MT, although discrepancies in the temporal variability evaluation were obtained under NT (i.e., a different result was obtained for two sampling dates, 1 vs. 6 or 7) ( Table 3).

Comparison between the BEST and SFH Techniques
The results of saturated hydraulic conductivity obtained by SFH and BEST were described by the ratio Kfs,SFH/Kfs,BEST, which showed differences within a factor of 0.1-12.8 (Figure 7). Specifically, 1 Figure 5. Soil water retention curves under minimum tillage and no tillage (MT and NT, respectively) in the three sampling dates considered (first, sixth, and seventh) (a), and corresponding differences in soil water retention (Δθ) as a function of soil pressure head (h) (b). Dotted lines represent θ values at field capacity and the permanent wilting point, and the dashed line represents an estimated mean threshold value corresponding to a mean value of bulk density between different soil management types. Figure 7. Differences of field saturated hydraulic conductivity, Kfs, as a function of corresponding differences in gravimetric soil water content, Ui (a), and soil bulk density, BD (b), carried out with SFH and BEST (i.e., SFH/BEST). Units are given in: mm h -1 , g g -1 , and g cm -3 , respectively for Kfs, Ui, and BD. R² Figure 7. Differences of field saturated hydraulic conductivity, K fs , as a function of corresponding differences in gravimetric soil water content, U i (a), and soil bulk density, BD (b), carried out with SFH and BEST (i.e., SFH/BEST). Units are given in: mm h −1 , g g −1 , and g cm −3 , respectively for K fs , U i , and BD.

Discussion
Evaluating the temporal variability of the physical and hydraulic properties of the soil is crucial for crop modeling and, therefore, to assess the economic and environmental sustainability of specific soil management practices. In this investigation, the temporal variability of some physical and hydraulic properties was investigated over two years in a long-term agronomic experiment mainly aimed at comparing the effects of soil management practices, namely minimum tillage (MT) and no tillage (NT), by applying the SFH and BEST methods.
Temporal variability of K fs detected by the SFH technique was comparable between MT and NT given that in 75% of the considered cases (i.e., 21/28 sampling dates), the same result (i.e., significant or not significant) was obtained ( Table 2). On the other hand, when the temporal variability was investigated by BEST, a lack of significant differences was found (Table 3); this was partly due to a difference in the number of comparisons made (i.e., eight and three sampling dates, respectively, for SFH and BEST). However, when a homogeneous comparison between methods was made (considering the same sampling dates, i.e., 1, 6, and 7; Figure 2), similar results were always obtained for MT (i.e., same result in three out of three cases) while only in one out of three cases under NT, suggesting a higher temporal stability or a more difficult variability to capture under MT and NT, respectively. Therefore, the two methods could be not considered equivalent to study the temporal variability of K fs when very heterogeneous soil conditions are sampled. However, a deeper analysis to better interpret the observed discrepancies under NT suggests a possible SFH overestimation on sampling dates 6 and 7 because: (i) The mean value of K fs,SFH of sampling date 1 was significantly lower by a factor of 6 or 7; (ii) the ratio K fs,SFH /K fs,BEST was equal to a factor of 1, 13, and 4, respectively, on the three dates compared.
Although the compared methods (SFH and BEST) were very different for geometry of the water flow (1D or 3D, respectively) and therefore for the corresponding hydraulic modeling (transient or quasi-steady-state condition of the infiltration process), K fs discrepancies between methods by a factor of 6 or 7 may be hypothesized when comparable BD or U i values (i.e., a ratio approximately equal to one) were detected in the field. It is well known that transient methods could lead to higher K fs estimates than stationary ones [26], and following a restrictive criterion, differences of K fs obtained with alternative methods within a factor of 2 or 3 are considered negligible for practical purposes [37,53]. However, since slightly higher discrepancies were obtained in the present study, other factors should be taken into consideration to better understand SFH overestimation, including the (i) differences in the run duration, (ii) sampled soil volume, and (iii) possible preferential flow within the soil macropores or the soil water content at the time of experiment. In fact, although infiltration measurements were simultaneous, spatial variability should be considered as different according to the soil management type (i.e., due to probable local accumulations of organic matter), and Figure 7 confirms this hypothesis.
Studying the temporal variability of soil properties may also imply the accurate evaluation of some ancillary variables. Under MT, for example, a greater temporal variability of θ i was detected, as compared with BD, only due to the low soil moisture values in the summer of 2017 (Figure 4).
Compared with SFH, the application of BEST could be considered more demanding in terms of experimental effort and specific skills for soil properties' determination and data analysis. However, it allows the estimation of some main capacity-based soil indicators that are relevant for agronomic and environmental purposes and, therefore, the evaluation of their temporal changes over time. The comparison between the months of April 2016 and 2017 showed that there were no significant differences of P mac , PAWC, and RFC under either MT or NT because they differed only in the air capacity of the soil ( Figure 5). Moreover, since discrepancies in temporal variability were detected in only 3 out of 12 considered cases when all dates (and indicators) were considered, we may conclude that the two investigated soil management practices had a similar temporal variability, as shown by the capacity-based soil indicators estimated by BEST. This finding is important and not trivial, since it confirms several literature results emphasizing that long-term NT may show stable soil properties as compared with MT (among others, [31,32,54]). Results are also plausible because, in the three aforementioned cases (sampling date 1, P mac and RFC; sampling date 3, AC), only MT changed over time, whereas NT was stable.

Conclusions
In this study, the temporal variability of some hydraulic soil properties was investigated over two years by applying the SFH and BEST techniques in a long-term field experiment for wheat cultivation under minimum tillage and no-tillage soil management.
Overall, the two soil management systems showed a relatively similar temporal variability of soil hydraulic properties (42% and 37%, respectively, under MT and NT), including bulk density, saturated hydraulic conductivity, macroporosity, air capacity, plant available water capacity, and relative field capacity. Although the two applied methods for K fs estimation were basically different (among those to be highlighted, confined-unconfined or 1D-3D water flux, sampling volume), they provided similar information on the temporal variability of K fs under MT, but not under NT, showing possible K fs overestimations when SFH was used under no tillage. Therefore, although the two techniques appear to be equivalent in the MT system, further investigations are desirable to check these findings, and a greater number of dates compared would increase the reliability of the estimates.
However, when comparable soil conditions were sampled (i.e., water content and bulk density of the soil), the two methods differed by less than an order of magnitude (a factor of six or seven) with K fs,SFH > K fs,BEST . Therefore, if the main aim of the research is to intensively monitor the temporal variability of K fs , the use of stable cylinders can be considered an inexpensive and quite accurate approach. Conversely, if hydraulic soil properties (water retention curve and hydraulic conductivity functions) are required, applying BEST is a way to estimate useful indicators to investigate, for example, the sustainability of soil management systems from a hydrological perspective. Although the latter technique is more complex than SFH, requiring certain extra measurements to run the available BEST calculation algorithms, their simultaneous use in the field can increase our confidence to quantify the impact of agronomic practices on soil physical properties.
In this long-term field experiment, characterized by 15 years of continuous soil management conducted with the methodological rigor typical of experimental farms, the BEST procedure allowed us to derive useful capacity-based indicators and confirmed a substantial equivalence between MT and NT for the fine-textured soil investigated.