extfieldsofstudy: Medicine Chemistry

text:
Alginate-Edible Coatings for Application on Wild Andean Blueberries (Vaccinium meridionale Swartz): Effect of the Addition of Nanofibrils Isolated from Cocoa By-Products

Edible coatings and films are appealing strategies for the postharvest management of blueberries. In the current work, alginate and alginate/cellulose nanofibril (CNF) edible coatings crosslinked with calcium chloride were developed for application on Andean blueberry (a promissory wild blueberry). Cocoa by-products were valorized through the isolation of their CNFs, and these were incorporated in the edible coatings. Edible coating formulations were based on blends of alginate (2% w/v), CNFs (0%, 0.1%, or 0.3%), glycerol, and water. In addition, stand-alone films were prepared, and their light and water vapor barrier properties were studied before applying the coating on the fruit surface. The results show that the addition of CNFs caused a significant decrease in the transparency and the water vapor permeability of the alginate films. After applying on the Andean blueberry fruits, the alginate and alginate/CNF coatings enhanced the appearance and the firmness of the fruits. Moreover, they significantly reduced the respiration rate and the water loss of the Andean blueberries throughout the 21 days of refrigerated storage. Alginate and alginate/CNFs coatings may be considered a useful alternative for the delay of the postharvest deterioration of Andean blueberries.

Introduction
Blueberries are widely consumed small red fruits with high economic value and several health benefits [1]. Highbush blueberries (Vaccinium corymbosum) are the most commercial blueberries. However, there are several other wild shrubs of the genus Vaccinium with similar or higher biological activity, but with few commercial exploitations, such as the Vaccinium meridionale Swartz (Andean blueberry). The latter grows in the Andean region of South America at 2300-3300 m above sea level (m.a.s.l.) [2].

Isolation of Cellulose Nanofibrils (CNF) from Cocoa Byproducts
Cocoa shell was chemically treated following a modification of the KOH-5 procedure developed by Zuluaga et al. [26] to remove non-cellulosic components. First, the cocoa shell was vigorously stirred at room temperature for 14 h with 5 wt.% KOH solution. Then, the insoluble residue was delignified with 1 wt.% NaClO 2 at pH 5.0, and adjusted with 10 wt.% acetic acid, at 70 • C for 1 h. At each step of the different treatments, the insoluble residue was extensively washed with distilled water until the pH was neutral. Finally, the cellulosic material at a concentration of 2 wt.% was passed 30 times through grinder equipment (Masuko Sangyo, Supermasscolloider), according to the G30 procedure developed by Velásquez-Cock et al. [27].

Atomic Force Microscopy
The morphology of cellulose nanofibrils from cocoa shell was studied using atomic force microscopy (AFM). Samples were imaged in tapping mode using a Flex AFM (Nanosurf) equipped with a multimode head and operated with a resonance frequency of 183 kHz. It used a cantilever of 125 mm in length and 5-10 nm in tip radius. Nanocellulose samples were diluted in distilled water and sonicated (Elma Elmansonic P) for 15 min at room temperature to achieve a good dispersion of the nanocellulose. Subsequently, a fine layer of the sample was deposited on mica by using a spin-coater for 2 min at 2000 rpm. Before morphological analysis, samples were stored in a vacuum desiccator for 3 days.

Preparation of Film/Coating Solutions
Film/coating solutions with and without cellulose nanofibrils from cocoa shell were prepared. Alginate solutions were made by dissolving sodium alginate powder (2% w/v) in distilled water and heating at 70 • C under constant stirring until the mixture became clear. Glycerol (30% w/v dry weight basis) was added as a plasticizer to the sodium alginate solution and stirred for 15 min.
For the preparation of alginate/CNF blends, alginate solutions of two different concentrations of Cocoa CNFs (0.1% and 0.3% w/v) were added and homogenized at 20,000 rpm for 3 min using an Ultra Turrax T25 (IKA ® WERKE, Germany) homogenizer. The concentrations of sodium alginate, glycerol, and cocoa shell nanofibrils were chosen according to previous works [28].
All formulations were degassed using a vacuum pump and cooled to room temperature for later application on the fruits.

Formation and Characterization of Edible Films
The film-forming solutions were dispensed into polypropylene plates and dried at 50 • C for 24 h. Then, dried films were peeled from the plates, submerged in a gelling bath of calcium chloride solution (1% w/v) for 30 min, cleaned with distilled water, and air-dried at room temperature. All films were conditioned at room temperature into desiccators containing a supersaturated solution of sodium bromide (RH~57%) for 48 h prior to characterization studies.
The thicknesses of the films were measured using an electronic digital caliper. Water vapor permeability (WVP) tests were carried out at room temperature following the ASTM E96/ASTM E96M-16 method. Film samples were sealed over a circular opening of 4 × 10 −4 m 2 in a permeation cell, containing calcium chloride. Then, the cells were placed in desiccators conditioned with sodium chloride saturated solution (75% RH). Changes in the weight of the cell were recorded to the nearest 0.0001 g and plotted as a function of time, and the slope of each line was calculated by linear regression. WVP (g Pa −1 s −1 m −1 ) was calculated as follows: where WVTR is the water vapor transmission rate calculated as the ratio between the slope of the straight line (g/s) and the cell area (m 2 ); P is the saturation vapor pressure of water (Pa); RH is the relative humidity in the desiccator, and d is the film thickness (m). Film transparency was measured as reported by Piñeros-Hernandez, Medina-Jaramillo, López-Córdoba, & Goyanes, 2017 [29]. Films were cut into rectangles (50 mm × 10 mm) and placed on the inner side of a quartz spectrophotometer cell. The percent transmittance (% T) of light at 600 nm (T 600 ) was measured using a UV-visible spectrophotometer (X-ma 1200 Human Corporation, Loughborough, UK), and the transparency was calculated as the ratio between logT 600 and the thickness (mm) of each film.

Application of Edible Coatings
A total mass of 6 kg of Andean blueberries was randomly divided into four groups (control, alginate, alginate/CNFs 0.1%, alginate/CNFs 0.3%), each group containing 1.5 kg of fruit. The blueberries were dip-coated by immersion in the coating solutions for 90 s, drained of excess coating, submerged in a gelling bath of calcium chloride solution (1% w/v) for 30 min, cleaned with distilled water, and air-dried at room temperature. Control samples (without coating) were also prepared by immersion of fruits in distilled water and kept under the same storage conditions than the treated ones, for comparison.
The surface solid density (SSD) was estimated as an indicator of the coating's average thickness as follows [30,31]: where SSD is the surface solid density (g/m 2 ); M CA is the mass of coating solution adhered to the fruit surface (g); X S is the mass fraction of solid in the coating solution; and A S is the surface area of Andean blueberries (m 2 ). The average sample surface area (A s ) was estimated by considering blueberry as a sphere. Samples were weighed before and after coating to determine the mass of coating solution adhered to the fruit surface (M CA ). The non-coated sample was used as a control.

Evaluation of Quality Attributes of Andean Blueberries along Storage
The coated and uncoated Andean blueberries were packed in PET trays with perforated vents and stored for 21 days. Evaluations of quality attributes were performed at 1, 7, 14, and 21 days of refrigerated storage (4 • C and 90% RH). For every sampling time, three trays containing 125 g (~250 units) of Andean blueberries each were prepared.  [32]. Approximately 120 g of Andean blueberries were placed for 30 min at 25 • C inside hermetically sealed 2 L flasks. Then, the CO 2 concentration was determined using an infrared analyzer (LabQuest ® 2 Model LQ2-LE, Beaverton, OR, USA), and results were expressed as the rate of respiration (CO 2 ) in mg kg −1 s −1 .

Weight Loss
Weight loss of Andean blueberries during storage was determined by weighing all fruit trays at the beginning of the storage and every day of analysis. The weight loss (% W) was calculated with the following equation: where m f is the weight at each time and m 0 the initial weight of each sample.

Soluble Solids Content, pH, and Titratable Acidity (%)
The soluble solids content was measured in the fruit juice using an Atago refractometer model PR 101 (Atago CO., Tokyo, Japan) and expressed as Brix (AOAC 932.12). Fruit samples were crushed using a blender and filtered through filter paper to obtain the fruit juice.
The pH of the fruit samples was assessed using a digital pH meter (Oakton Instruments, Vernon Hills, IL, USA) (AOAC 981.12).
Titratable acidity (%) was determined by titration with 0.1 N NaOH up to pH 8.2, using 0.5 g of sample in 10 mL of distilled water (AOAC 942.15). The results were expressed in citric acid percentage.

Firmness Analysis
Firmness was determined using a digital Force Gauge PCE-FM200 (Southampton, UK) equipped with a 6 mm diameter stainless steel probe. Firmness was defined as the maximum force to disrupt the tissue at the penetration time used (5 s) [33]. The results were expressed as an average of at least five measurements.

Statistical Analysis
The statistical analysis was performed using Minitab v. 16 statistical software (State College, PA, USA). Analysis of variance (ANOVA) and Tukey's pairwise comparisons were carried out using a level of 95% confidence. The experiments were performed at least in triplicate, and the data were reported as mean ± standard deviation. Figure 1a shows some bundles with diameters around 300 nm and some individual CNFs. The morphology of individual CNFs is evidenced more clearly in Figure 1b, where nanofibers with diameters between 15 and 50 nm and several microns in length are observed. Therefore, AFM images presented in Figure 1a,b reveal that the cellulosic material isolated from cocoa shell has a fibrillar morphology and forms an entangled network of high-aspect materials, which makes it a suitable material to develop edible coatings and films with barrier properties and prevent the gaseous exchange.  Table 1 shows the thickness, transparency, and water vapor permeability of the alginate films with and without cellulose nanofibrils from cocoa shell. All films were clear enough to be used as see-through packaging. However, alginate/CNF 0.1% and alginate/CNF 0.3% films showed a decrease in the transparency of 27% and 40%, respectively, in comparison to the alginate ones. Martins et al. [34] also reported a higher opacity in starch/CNF films as more CNFs were added to the filmogenic matrix, and it was attributed to the dispersion of light by nano-sized fibrils.

Film Characterization
In addition, the addition of cocoa shell nanofibrils caused a decrease in the water vapor permeability of the films of 65%, compared to the films without nanofibrils, regardless of the concentration used (0.1% or 0.3%). This behavior could be attributed to the nanofibrils being able to act as a filler, causing a tortuous pathway that limits water vapor transmission through the alginate matrix. These results are in agreement with those reported in the literature [34]. Table 1. Thickness, water vapor permeability, and transparency of the alginate films with and without cellulose nanofibrils from cocoa shell.

Sample
Thickness (mm) Transparency (%) Different letters within the same column indicate statistically significant differences (p < 0.05). Figure 2 shows the appearance of Andean blueberries with and without coatings. It can be seen that the coated fruits were brighter than the uncoated ones. This behavior was attributed to the smoother surface of the coated fruit, compared to the fruit skin, and it caused a greater reflection of visible light [35]. The brightness is an important quality perceived attributed to blueberries because consumers commonly associate it with a fresh-like appearance [36].  Table 1 shows the thickness, transparency, and water vapor permeability of the alginate films with and without cellulose nanofibrils from cocoa shell. All films were clear enough to be used as see-through packaging. However, alginate/CNF 0.1% and alginate/CNF 0.3% films showed a decrease in the transparency of 27% and 40%, respectively, in comparison to the alginate ones. Martins et al. [34] also reported a higher opacity in starch/CNF films as more CNFs were added to the filmogenic matrix, and it was attributed to the dispersion of light by nano-sized fibrils. In addition, the addition of cocoa shell nanofibrils caused a decrease in the water vapor permeability of the films of 65%, compared to the films without nanofibrils, regardless of the concentration used (0.1% or 0.3%). This behavior could be attributed to the nanofibrils being able to act as a filler, causing a tortuous pathway that limits water vapor transmission through the alginate matrix. These results are in agreement with those reported in the literature [34]. Figure 2 shows the appearance of Andean blueberries with and without coatings. It can be seen that the coated fruits were brighter than the uncoated ones. This behavior was attributed to the smoother surface of the coated fruit, compared to the fruit skin, and it caused a greater reflection of visible light [35]. The brightness is an important quality perceived attributed to blueberries because consumers commonly associate it with a fresh-like appearance [36]. Besides, the surface solid density (SSD) was estimated as an indicator of the applied coating's thickness. It was observed that the addition of CNFs did not cause important changes in the SSD of the fruits, obtaining values of 0.52, 0.54, and 0.60 g.m −2 for the alginate, alginate/CNFs 0.1%, and alginate/CNFs 0.3% systems, respectively. SSD values between 1.0 and 1.85 g.m −2 were reported by Falcó et al. [31] for blueberries (Vaccinium corymbosum) coated with edible coatings based on carrageenan (κ−, ι−, and λ).

Behavior of Andean Blueberry Quality Parameters during Storage
The changes in the respiration rate of Andean blueberries with and without edible coatings during storage are shown in Figure 3. All edible coatings caused a significant decrease in the respiration rate of the Andean blueberries. It has been well documented that alginate edible coatings are suitable to work as a barrier to oxygen, retarding the respiration rate, and also browning reactions [9,37,38]. At the end of the storage, the control fruits increased their respiration rate, while the fruits coated with alginate and alginate/CNFs 0.3% showed a similar respiration rate than at the initial time. Unlike the other samples, the fruits coated with alginate/CNFs 0.1% showed a significant decrease in their respiration rate at the end of the storage. It has been reported that CNFs with a high aspect ratio can turn and entangle into a network structure, leading to a tortuous diffusion path, which prevents the gaseous exchange [39,40]. However, above a critical concentration, CNFs tend to form agglomerate held together by hydrogen bonding [34]. Weight loss is a primary factor in blueberry deterioration [41]. It is well known that the loss of water in fresh fruits is mainly associated with respiration and transpiration through the skin, and it causes sensory quality loss generally because of the fruit shrinkage. Here, all systems showed a gradual increase in weight loss during storage (Figure 4). However, in the case of the coated fruits, Besides, the surface solid density (SSD) was estimated as an indicator of the applied coating's thickness. It was observed that the addition of CNFs did not cause important changes in the SSD of the fruits, obtaining values of 0.52, 0.54, and 0.60 g.m −2 for the alginate, alginate/CNFs 0.1%, and alginate/CNFs 0.3% systems, respectively. SSD values between 1.0 and 1.85 g.m −2 were reported by Falcó et al. [31] for blueberries (Vaccinium corymbosum) coated with edible coatings based on carrageenan (κ−, ι−, and λ).
The changes in the respiration rate of Andean blueberries with and without edible coatings during storage are shown in Figure 3. All edible coatings caused a significant decrease in the respiration rate of the Andean blueberries. It has been well documented that alginate edible coatings are suitable to work as a barrier to oxygen, retarding the respiration rate, and also browning reactions [9,37,38]. At the end of the storage, the control fruits increased their respiration rate, while the fruits coated with alginate and alginate/CNFs 0.3% showed a similar respiration rate than at the initial time. Unlike the other samples, the fruits coated with alginate/CNFs 0.1% showed a significant decrease in their respiration rate at the end of the storage. It has been reported that CNFs with a high aspect ratio can turn and entangle into a network structure, leading to a tortuous diffusion path, which prevents the gaseous exchange [39,40]. However, above a critical concentration, CNFs tend to form agglomerate held together by hydrogen bonding [34]. Besides, the surface solid density (SSD) was estimated as an indicator of the applied coating's thickness. It was observed that the addition of CNFs did not cause important changes in the SSD of the fruits, obtaining values of 0.52, 0.54, and 0.60 g.m −2 for the alginate, alginate/CNFs 0.1%, and alginate/CNFs 0.3% systems, respectively. SSD values between 1.0 and 1.85 g.m −2 were reported by Falcó et al. [31] for blueberries (Vaccinium corymbosum) coated with edible coatings based on carrageenan (κ−, ι−, and λ).
The changes in the respiration rate of Andean blueberries with and without edible coatings during storage are shown in Figure 3. All edible coatings caused a significant decrease in the respiration rate of the Andean blueberries. It has been well documented that alginate edible coatings are suitable to work as a barrier to oxygen, retarding the respiration rate, and also browning reactions [9,37,38]. At the end of the storage, the control fruits increased their respiration rate, while the fruits coated with alginate and alginate/CNFs 0.3% showed a similar respiration rate than at the initial time. Unlike the other samples, the fruits coated with alginate/CNFs 0.1% showed a significant decrease in their respiration rate at the end of the storage. It has been reported that CNFs with a high aspect ratio can turn and entangle into a network structure, leading to a tortuous diffusion path, which prevents the gaseous exchange [39,40]. However, above a critical concentration, CNFs tend to form agglomerate held together by hydrogen bonding [34]. Weight loss is a primary factor in blueberry deterioration [41]. It is well known that the loss of water in fresh fruits is mainly associated with respiration and transpiration through the skin, and it causes sensory quality loss generally because of the fruit shrinkage. Here, all systems showed a gradual increase in weight loss during storage (Figure 4). However, in the case of the coated fruits, Weight loss is a primary factor in blueberry deterioration [41]. It is well known that the loss of water in fresh fruits is mainly associated with respiration and transpiration through the skin, and it causes sensory quality loss generally because of the fruit shrinkage. Here, all systems showed a gradual increase in weight loss during storage (Figure 4). However, in the case of the coated fruits, Polymers 2020, 12, 824 8 of 13 the weight loss was significantly lower than in the control ones, regardless of the presence of cocoa shell nanofibrils. At the end of storage, control fruits and coated fruits showed water loss around 10% and 5%, respectively (Figure 4). It has been suggested that postharvest treatments that restrict blueberry water loss to below 8% are likely to be beneficial in retaining acceptable blueberry freshness and firmness [4,41]. The ability to reduce weight loss of alginate coatings has been demonstrated in several fruits, including pears [42] and plums [9].
Polymers 2020, 12, x FOR PEER REVIEW 8 of 14 the weight loss was significantly lower than in the control ones, regardless of the presence of cocoa shell nanofibrils. At the end of storage, control fruits and coated fruits showed water loss around 10% and 5%, respectively ( Figure 4). It has been suggested that postharvest treatments that restrict blueberry water loss to below 8% are likely to be beneficial in retaining acceptable blueberry freshness and firmness [4,41]. The ability to reduce weight loss of alginate coatings has been demonstrated in several fruits, including pears [42] and plums [9].  The changes in Andean blueberry soluble solids content, titratable acidity, and pH are shown in Figures 5 and 6. Control fruits showed a slight increase in their soluble solids content during storage ( Figure 5). This behavior was probably due to both the water loss and the cell wall degradation caused by the fruit ripening. In the case of the coated samples, it was expected that the water loss caused an increase in the soluble solids contents. Therefore, it can be hypothesized that the edible coatings were able to prevent the cell wall degradation, avoiding the increase in the soluble solids content of the coated samples. The changes in Andean blueberry soluble solids content, titratable acidity, and pH are shown in Figures 5 and 6. Control fruits showed a slight increase in their soluble solids content during storage ( Figure 5). This behavior was probably due to both the water loss and the cell wall degradation caused by the fruit ripening. In the case of the coated samples, it was expected that the water loss caused an increase in the soluble solids contents. Therefore, it can be hypothesized that the edible coatings were able to prevent the cell wall degradation, avoiding the increase in the soluble solids content of the coated samples. Concerning the pH and the titratable acidity (%), no relevant differences were detected among the uncoated and the differently coated samples neither at the initial time nor during the storage period ( Figure 6). The behavior of the firmness of the Andean blueberries with and without edible coatings during storage is shown in Figure 7. All the coated fruits showed significantly higher firmness than the uncoated fruit throughout the storage. This behavior could be attributed to both the presence of a coating that provides structural rigidity to the surface of the fruit and to the use of calcium ions as a cross-linking agent, which could act as a textural enhancer, minimizing the softening [10,43]. In addition, the increase in the firmness of the samples throughout the storage could be associated with the increase in the water loss.
At the end of the storage, the firmness of coated fruits was 25% higher than that the control ones. Concerning the pH and the titratable acidity (%), no relevant differences were detected among the uncoated and the differently coated samples neither at the initial time nor during the storage period ( Figure 6).
The behavior of the firmness of the Andean blueberries with and without edible coatings during storage is shown in Figure 7. All the coated fruits showed significantly higher firmness than the uncoated fruit throughout the storage. This behavior could be attributed to both the presence of a coating that provides structural rigidity to the surface of the fruit and to the use of calcium ions as a cross-linking agent, which could act as a textural enhancer, minimizing the softening [10,43]. In addition, the increase in the firmness of the samples throughout the storage could be associated with the increase in the water loss. firmness than the other systems. This fact could be due to the good dispersion of Cocoa shell nanofibrils within the alginate matrix when it was used at lower concentrations. Then, at 7 and 15 days of storage, the two coatings added of Cocoa shell nanofibrils provided higher firmness to the Andean blueberries. However, at the end of the storage, the firmness of the fruits with alginate and alginate/CNFs was similar.

Conclusions
Agro-food byproducts such as cocoa shell show promise as a precursor of cellulose nanofibrils than can be used in the formulation of edible films and coatings for their potential application in the preservation of foods such as Andean blueberries.
Cellulose nanofibrils with diameters between 15 and 50 nm were successfully extracted from cocoa shells through chemical and mechanical treatment. When these cellulose nanofibrils were incorporated within calcium alginate matrices, they provoked a significant decrease in the water vapor permeability and the transparency of the films. Moreover, the alginate edible coatings were able to decrease the respiration rate and weight loss and to improve the firmness of Andean blueberries, regardless of the presence of cellulose nanofibrils. Therefore, it can be concluded that alginate and alginate/cellulose nanofibrils coatings were useful in the delay of the postharvest deterioration of Andean blueberries when compared to the fruits without coatings.  At the end of the storage, the firmness of coated fruits was 25% higher than that the control ones. At the beginning of storage, the fruits with alginate/CNF 0.1% edible coatings showed higher firmness than the other systems. This fact could be due to the good dispersion of Cocoa shell nanofibrils within the alginate matrix when it was used at lower concentrations. Then, at 7 and 15 days of storage, the two coatings added of Cocoa shell nanofibrils provided higher firmness to the Andean blueberries. However, at the end of the storage, the firmness of the fruits with alginate and alginate/CNFs was similar.

Conclusions
Agro-food byproducts such as cocoa shell show promise as a precursor of cellulose nanofibrils than can be used in the formulation of edible films and coatings for their potential application in the preservation of foods such as Andean blueberries.
Cellulose nanofibrils with diameters between 15 and 50 nm were successfully extracted from cocoa shells through chemical and mechanical treatment. When these cellulose nanofibrils were incorporated within calcium alginate matrices, they provoked a significant decrease in the water vapor permeability and the transparency of the films. Moreover, the alginate edible coatings were able to decrease the respiration rate and weight loss and to improve the firmness of Andean blueberries, regardless of the presence of cellulose nanofibrils. Therefore, it can be concluded that alginate and alginate/cellulose nanofibrils coatings were useful in the delay of the postharvest deterioration of Andean blueberries when compared to the fruits without coatings.


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

text:
Combined Effects of Carica papaya Seeds with Albendazole on Adult Pheritima posthuma

Background: Carica papaya seed is commonly used as herbal remedy for intestinal worms. This study investigated the combined effect of Carica papaya seed extracts and albendazole on adult Pheretima posthuma. Methods: Cold percolation method was used to extract bioactive compounds in dry powdered seeds of Carica papaya using water, ethyl acetate and petroleum ether as solvents. Individual extracts and combinations of the extracts with albendazole were assayed for anthelminthic activity in which the time of paralysis (P) and time of death (D) of the worms were determined. Results: Aqueous extract gave highest extractive yield of 19.8% followed by the ethyl acetate extract at 17.2%. Moreover, the individual aqueous extract and that combined with albendazole paralyzed the worm within shorter time (41.00 and 42.00 minutes respectively) compared to the conventional drug albendazole alone (48.67 minutes). Conversely, Albedazole killed the worms faster than any other extract in just 1.18 hours. Albendazole combined either with water or ethyl acetate killed the worm at 24 th hour. Combined albendazole with petroleum ether extract could not kill the worm even after 24 hours. Conclusion: From this study, combining the C. papaya seed extracts with albendazole did not shorten paralytic and anthelminthic activity on adult Pheretima posthuma. Carica papaya seeds extracts should rather be used individually and best when extracted in water.

INTRODUCTION
lobally, soil transmitted helminthes affect over 1.5 billion people and it accounts for loss of about 200,000 human lives annually 1 . Whipworms (Trichuris trichiura), Roundworms (Ascaris lumbricoides), and Hookworms (Necator americanus and Ancylostoma duodenale) are helminthes of medical importance and are mainly transmitted through contaminated soil, water, food, and contact with animals' dung 2 . They are responsible for infections and complications such as; weakness, loss of appetite, 3 reduced weight gain and decreased productivity, aggravating malnutrition, retarded growth and development, retarded cognitive development, anemia, abdominal pains and diarrhea in the affected host 4 . To date, the conventional gold standard for the treatment of soil transmitted helminthiasis has been the use of Benzimidazole (Albendazole and Mebendazole) drugs 5 . However, with the outgrowth of Anthelmintic Resistance (AR) in the livestock industry due to years of mass drug administration and misuse of chemical anthelmintic drugs, soil transmitted helminthiasis is gradually re-emerging 6 . Besides, majority of patients (80%) in poverty stricken economies like Uganda, can hardly afford the conventional drugs hence depending mostly on herbal or medicinal alternatives 7 . Carica papaya (Linn.), commonly known as papaya is a fruit crop cultivated in tropical and subtropical regions, and is well known for its nutritional benefits and medicinal applications 8 . Several G studies have revealed the anthelmintic potentials of C. papaya on gastrointestinal worms. For instance, a study by Wasswa and Olila 9 revealed the effective dose (ED50) of Carica papaya seeds on Ascaris suum at 12.5 mg/ml. Likewise, in comparison to albendazole, C. papaya seeds was found to be three times more effective on adult Pheritima posthuma 10 and mixed gastrointestinal nematodes 11 . Still, no study has made an attempt to consider the effect of combining the papaya seed extract with a common drug Albendazole. Given the far limited number of anthelmintic drugs in market together with hardly new compounds, this study investigated the possibility of a combination treatment approach by combining papaya seed extracts with Albendazole (5-propylthio-2benzimidazolecarbomate) on adult earth worms (Pheritima posthuma). This could contribute to the knowledge base for a sustainable alternative against soil transmitted helminthiasis.

Test Worms and Control Drug
The study used adult Pheritima posthuma (earthworms) obtained from moist soils around Lake Victoria in Entebbe, Uganda (0.0436 o N, 32.4418 o E). The worms were authenticated at the Helminthology section of National Animal Disease Diagnostics and Epidemiology Centre (NADDEC), Entebbe, Uganda. The choice of the earthworms was based on their anatomical and physiological resemblance with the gastrointestinal roundworm parasites of human beings 12 . Albendazole tablet (400 mg) manufactured by Beximco pharmaceuticals (Gazipar, Bangladesh) was used as a control drug.

Collection of plant materials
The fresh seeds were collected from ripe Carica papaya fruits and washed with clean water to remove dirt. The seeds were then dried in the hot air oven (Memmert, Schwabach, Germany) at temperature of 50°C for 24 hours and later coarsely powdered using a homogenizer prior to extraction.

Extraction procedure
Dry powdered seeds was extracted by cold percolation method according to Parekh & Chanda et al, 13 with minor modifications. Petroleum ether, ethyl acetate and distilled water were used as solvents to maximize phytochemical extraction due to varying polarity. Briefly, 5 g of dried powder was added to a 250 ml conical flask and Petroleum ether was added to the conical flask (Phyrex, Werthiem, Germany) with sample to the 100 ml mark, the flask was sealed with aluminum foil, and kept on a rotary shaker (Ika, Staufen, Germany) at 120 rounds per minute (rpm) for 24 hours.
The solution was filtered through eight layers of muslin cloth, centrifuged (Beckman, Indianapolis, USA) at 5000 rpm for 15 minutes and the supernatant was collected and the residue was evaporated to dryness using an oven (Memmert, Schwabach, Germany). Each of the dry residue was transferred to 100 ml of individual solvent (Distilled Water and Ethyl Acetate) and was kept on a rotary shaker at 120 rpm for 24 hours. After 24 hours, the solution was filtered through eight layers of muslin cloth, centrifuged at 5000 rpm for 15 minutes and the supernatant was collected. Each of the filtered solution was left on petri dishes to evaporate. Extractive yield of each extract was calculated and later each of the three extracts were stored at 4°C for anthelminthic assay.

Anthelminthic assays
The individual and combined anthelminthic activity of Carica papaya seed extracts was carried out as described by Ajaiyeoba et al. 14 with minor modifications. Extracts, control drug and their respective combinations were administered through contact. Two worms were fully immersed in a 9 cm radius petri dish containing 10 ml of each of the extracts described above, or a combination of the extracts and albendazole. For this study, a uniform concentration of 10 mg/mL was empirically used. This was done in triplicates for all the samples and the time for induced paralysis (P, in minutes) was taken when no movement of any sort could be observed, except when the worms were shaken vigorously. Time of death of worms, if death occurred (D, in minutes) was recorded after ascertaining that worms neither moved when shaken vigorously nor when pricked with a needle. Albendazole (10 mg/ml) was included as reference/control drug, while distilled water was included as the negative control.

Statistical analysis
Tableau Software v2019.4 was used in data presentation. All experiments were repeated at least three times and the Standard Error of the Mean (SEM) determined. The means were compared using SPSS software v16.0 in which a onesample T-test was performed to determine the significance of the anthelmintic potentials of Carica papaya seeds.

Ethical consideration
Ethical approval and permission to conduct this study was obtained from the Bugema University Research Ethics Committee, Kampala, Uganda (Ref. BU-REC/2019/U09).

Extractive yields
The extractive yield of Carica papaya seeds across three different solvents is presented in Figure 1.

EA-Ethyl acetate DW-Distilled water PE-Petroleum ether
Distilled water presented with the highest yield (19.8%) as compared to ethyl acetate and petroleum ether extracts. Ethyl acetate gave about 17.2% while petroleum ether presented with the least extractive yield (1%).

Anthelminthic Activity assay
The anthelminthic potentials of 10 mg/mL Carica papaya seed extracts and extract-drug combinations are presented in Table 1. The mean paralysis time for distilled water extract and its combination with Albendazole were found to be shorter than that of the control drug Albendazole (p<0.05), indicating that they are more effective in paralyzing the worm than Albendazole alone. Even though, individual ethyl acetate and its combination also had shorter paralysis time than Albendazole, this was not statistically significant (p>0.05) and the rest of the extracts were less promising compared to the control drug (Mean Paralysis Time > 46.67±4. 16). With respect to the time of death, neither individual petroleum ether extract nor its combination could kill the worms even after 24 hours whereas, the ethyl acetate extracts (individual extract) could kill the worms within a few hours (2.10±0.10). Surprisingly, combining C. papaya extract with Albendazole delayed anthelminthic activity by an average of 12 minutes (r=1.00, p=0.001). A similar trend was observed in distilled water extract and its combination with albendazole.

DISCUSSION
Here we found that combining individual extract of Carica papaya seeds with Albendazole decreases their anthelminthic activity on adult Pheritima posthuma. One inherent weakness of this study is that a fixed dose (10 mg/mL) was used instead of varying the concentration of the extracts. Besides the extract used was crude in comparison to pure control drug, Albendazole of the same concentration and this could have Extracts time of <35 minutes and < 1 hour respectively. The variation in paralysis and death time in this study could be due to lower concentration, 10 mg/ml as compared to the 25-100 mg/ml concentration range in the above-mentioned comparative study. Similarly, a number of other documented studies have demonstrated the anthelminthic activity of Carica papaya, 9,10,[16][17][18][19] . However, this study advanced to report the effect of combining the individual extracts with control anthelminthic drug, albendazole. Carica papaya seeds contain several bioactive compounds with the main ones being enzyme papain and benzylisothiocyanate. 20 . In addition, the seeds are rich with other phytochemicals like tannins, alkanoids, oxalates and trypsin inhibitors 21 . The presence of tannins in Carica papaya seeds is the chief reason for its anthelminthic potentials, possibly because tannins can bind onto glycoproteins on the cuticle of the worm to induce necrosis thereby causing death of the worm 22 . Thus, suggesting the possible use of extract of such seeds as de-wormers. However, the individual extract in combination with control drug showed decreased activity. This could be due to a number of reasons ranging from target incompatibility as well as inhibitory effects 23 . The study thence suggests that seeds of Carica papaya should be used individually if effective action against the worms is to be achieved. Furthermore, it is needful that anthelminthic bioactive compounds be purified, and structure elucidated for an accurate evaluation of antagonistic activity and its mechanism of action. Toxicity studies of the individual seed extract should as well be executed to establish the safety indices of such extracts.

CONCLUSION
From this study, combining the C. papaya seed extracts with albendazole did not shorten their paralytic and anthelminthic activity on adult Pheritima posthuma. Carica papaya seeds extracts should rather be used individually and best when extracted in water.


text:
Impact of environmental changes and land-management practices on wheat production in India

There are six stages in the growth of C3 crop that are modeled in ISAM (Song et al. 2013). The concept of Growing Degree Days (GDD) is used to define wheat growth and divide the wheat-growing season into different phenological stages. Daily GDD is calculated as the difference between daily average air temperature and base temperature since the planting day. Each stage is governed by heat requirement range (fraction of maximum GDD) and switches to the next stage when the heat unit index (=accumulated GDD/maxGDD) exceeds the maximum limit for that stage. GDD is calculated daily and accumulated GDD is calculated as cumulative GDD that increases along the growing season.

reported heat stress in wheat for temperatures between 25 to 35 • C  during the grain development stages. Beyond the temperatures of 35 • C, wheat fails to survive. High temperatures are terminal to wheat yield specifically in the flowering and grain filling stages during the second half of the growing season (Farooq et al., 2011). Increasing temperature change and heat stress events in recent decades and their impacts on wheat crop growth processes have been extensively studied (Asseng et al., 2015;Lobell et al., 2012;Farooq et al., 2011;Ortiz et al., 2008). Another environmental factor that has been widely studied is the impact of increasing [CO 2 ]. The resulting CO 2 fertilization effect is found to promote crop growth (Dubey et al., 2015). Apart from environmental factors, management practices including nitrogen fertilizer application and irrigation also significantly affect wheat production (Myers et al., 2017;Leaky et al., 2009;Luo et al., 2009). Because wheat is grown in the non-monsoon months, it is a high irrigation crop with almost 94 % of the wheat fields in India equipped for irrigation (MAFW, 2017). The quantification of the impacts of land management practices on crop growth helps in understanding how croplands can be managed to improve production (Tack et al., 2017).
Even though India is the third largest wheat producer in the world, domestic production is barely sufficient to meet the country's demand for food and livestock feed (USDA, 2018). Data from different sources report a relatively poor yield of wheat in India as compared to other countries (FAO-STAT online database, 2019). Hence, there is an urgent need to address this yield gap by developing better land management practices under different environmental conditions Zhao et al., 2014;Luo et al., 2009). A key first step to achieve this goal is to understand the processes involved in interactions of the crop with its environment and the factors responsible for impacting crop growth.
Dynamic global vegetation models (DGVMs) are wellestablished tools to study global climate-vegetation systems. Implementation of crop-specific parameterization and processes in DGVMs provides us with a better framework to assess and represent the role of agriculture in climatevegetation systems Bondeau et al., 2007). This improves the representation of biogeochemical and biogeophysical processes, especially the feedback mechanisms, and the prediction of crop yield and production. Multiple process-based models with crop-specific representations are currently being used (e.g. Lu et al., 2017;Drewniak et al., 2013;Song et al., 2013;Lokupitiya et al., 2009;Bondaeu et al., 2007) instead of stand-alone crop models for this purpose.
This study explores the effects of environmental drivers and management practices on spring wheat in India using the Integrated Science Assessment Model (ISAM) . The specific objectives of this study are (1) to implement a dynamic spring wheat growth module in ISAM and (2) to study the effect of environmental factors (elevated [CO 2 ] and climate change, including temperature and precipitation change) and land management practices (irrigation and nitrogen fertilizers) on the production of spring wheat in India for the 1980-2016 period using ISAM. To the best of our knowledge, this is the first study that evaluates the impacts of multiple environmental factors and land management practices on spring wheat in India at a country level by implementing spring wheat specific processes in a land surface model.

Study design
The study is designed as follows. First, field data on crop physiology are collected at an experimental spring wheat field site. Next, the spring wheat model is developed and implemented in ISAM. The model is run at site scale for the calibration and evaluation of the site data. Next, the model is run for the entire country and evaluated with country-scale wheat production data. Finally, numerical experiments are conducted to estimate the effects of various environmental factors and land management practices on spring wheat production. Details of each step are described below.

Site data
Field data on spring wheat growth are required to develop, calibrate, and evaluate the spring wheat model. Such data are not readily available in the public domain. Hence, a field campaign is conducted during two growing seasons: 2014-2015 and 2015-2016. Leaf area index (LAI) is measured for 2014-2015, and LAI and above-ground biomass at different growth stages are measured for the growing season 2015-2016 at a wheat experimental site. The site is approximately 650 m 2 in area and is located at 28 • 40 N, 77 • 12 E in the Indian Agricultural Research Institute (IARI) campus in New Delhi, which is a subtropical, semi-arid region. The crop was sown on 18 November 2014 and 20 November 2015. It reached physiological maturity on 30 March in both years. The wheat field is irrigated with an unlimited amount to ensure that the water stress to the crop is minimal. Mimicking local farming practices, whenever the soil is perceived to be dry, water is added till the top layers are near saturated. These led to four irrigation episodes in 2014-2015 and five in 2015-2016. A total amount of nitrogen fertilizer of 120 kg N ha −1 is added to the crop in three batches of 60, 30, and 30 kg N ha −1 in a span of 60 d from planting day.
The LAI is measured at weekly intervals with LI-COR's LAI-2000 plant canopy analyser that measures gap fraction at five zenith angles using hemispherical images from a fisheye camera. LAI is estimated by comparing one abovecanopy and three below-canopy measurements. The observed LAI is actually an average of multiple (at least five) LAI observations at different locations in each plot. To measure above-ground biomass, plant samples from 50 cm row length are first cut just above the soil surface. Then, different plant organs like leaves, stem, and spike (after anthesis) and portions of plant samples are separated out. These are initially dried in the shade and later dried at 65 • C in an oven for 72 h till the weight stabilizes. Finally, the weight of dried plant samples is measured using an electric balance. To measure yield, two samples of mature wheat crops are harvested from 1 m×1 m area in each plot and allowed to air dry. The total weight of grains and straw in each plot is recorded with the help of a spring balance. After thrashing and winnowing by mechanical thrasher, grains are weighed to estimate grain yield and thousand grain weight.

Dynamic C 3 crop model in ISAM
ISAM is a well-established land model that has been used for a wide range of applications (Gahlot et al., 2017;Song et al., 2016Song et al., , 2013Barman et al., 2014a, b). ISAM simulates water, energy, carbon, and nitrogen fluxes at a 1hour time step with 0.5 • × 0.5 • spatial resolution. ISAM has vegetation-specific growth processes for all major plantfunctional types implemented in the model to better capture seasonality for each. Song et al. (2013) have developed a soybean and maize model for ISAM. Because soybean and wheat are both C 3 crops, the dynamic C 3 crop model framework from the soybean model is used as a foundation to build a spring wheat model for this study. The model structure, phenological stages, carbon, and nitrogen allocation processes, parameters, and performance have been extensively described and evaluated in various studies (Song et al., 2016.

Development and implementation of spring wheat processes in ISAM
The spring wheat processes in ISAM are implemented using the C 3 crop framework . For this purpose, C 3 crop specific equations and parameters are updated based on the literature. The model equations are available in Song et al. (2013). A brief description is given in the Supplement, and the revised parameters are available in Table S1 in the Supplement. Some of the parameter values are collected from the literature while the rest are estimated during model calibration. ISAM accounts for dynamical planting . This unique feature of ISAM is quite important for modelling wheat in India because in India wheat is grown in different climatic conditions (Ortiz et al., 2008) and in multiple cropping systems. In the rain-dependent, tropical, central parts of India, wheat is planted early; in eastern parts of India, where rice is harvested before the wheat is planted on the same field, wheat is planted late; and it is timely sown in the northern and western parts of India (Table S2). ISAM uses different conditions based on a 7 d average of air temperature and 30 d total precipitation to dynamically calculate the planting day. Observed wheat planting and harvest dates (Sacks et al., 2010) are used to calibrate the planting time and harvest time criteria in the model along with other state-level and regional datasets (NFSM, 2018). This allows for the correct simulation of the observed spatial variability of the planting date.
The heat stress effect is implemented to account for the observed negative effects of high temperatures on grains (Asseng et al., 2015;Farooq et al., 2011) during the reproductive stage of the phenology (Zhao et al., 2007). To include these effects, net carbon available for allocation to grains decreases as daily average temperatures increase from 25 to 35 • C in the flowering and grain filling stages (Table S3; Eqs. S1-S3). This limits the growth of a plant. Beyond daily average temperatures of 35 • C, the grains fail to develop.

Site-scale simulations for calibration and validation
The spring wheat model is calibrated at site level using LAI and above-ground biomass data collected at the IARI site for the 2015-2016 growing season using the protocol described in Song et al. (2013) and validated using LAI data for the 2014-2015 growing season. ISAM can be configured to run for a single point. Using this capability, ISAM is run at site scale to simulate spring wheat growth observed at the IARI site.
The model is spun up by recycling the Climate Research Unit-National Centers for Environment Prediction surface climate data (CRU-NCEP; Vivoy, 2018), Global Carbon Project Budget 2017 [CO 2 ] data (Le Quéré et al., 2018), and the airborne nitrogen deposition data (Dentener, 2006) for 2015-2016 until the soil temperature, soil moisture, the soil carbon pool, and the soil nitrogen pool reach a steady state. Then, the above-ground biomass carbon (leaves + stem + grain) is calibrated using above-ground biomass (Fig. 1a), nitrogen fertilizer amount added, sowing date, and harvest date for the 2015-2016 growing season. Next, phenologydependent carbon allocation fractions for leaves, stem, and grain are calibrated using the LAI data ( Fig. 1b), duration, and heat unit index requirement for each growth stage. The model is evaluated by comparing simulated and observed LAI for the 2014-2015 growing season.

Gridded data for country-scale simulations
Driver data for environmental and anthropogenic forcings are required to conduct ISAM simulations. ISAM is driven by 0.5 • × 0.5 • surface climate data from CRU-NCEP (Viovy, 2018) with 6-hourly temperature, specific humidity, incoming short-wave and long-wave radiation, wind speed, and precipitation rate that are interpolated to hourly values. Annual [CO 2 ] data are taken from the Global Carbon Project Budget 2017 (Le Quéré et al., 2018). Spatially explicit an-  (Table S3; Eqs. S4-S5).
Gridded data for the wheat harvested area, nitrogen fertilizer application, and irrigation are required as model input to estimate actual wheat production for India in recent years . For this purpose, an annual, spatially explicit gridded wheat harvested area dataset for India is created as part of this study by combining spatially explicit wheat area from Monfreda et al. (2008) for the mean value over the time period 1997-2003 (ca. 2000) and non-gridded state-specific annual wheat harvested area from the Directorate of Economics and Statistics, Ministry of Agriculture and Farmers Welfare, India (MAFW, 2017) (Eqs. S6, S7, S8). The annual area equipped for irrigation (AEI) dataset is created by linear interpolation of decadal data from Siebert et al. (2015) (Eq. S9).

Country-scale simulations
Country-scale simulations are conducted after model calibration and evaluation. First, we spin up the model for the year 1901 until the soil temperature, soil moisture, and the soil carbon and nitrogen pools reach a steady state at approximately 1901 levels. For the spin-up run, the model is driven by recycled CRU-NCEP (Viovy, 2018) climate data for the period 1901-1920, while [CO 2 ] (Le Quéré et al., 2018) and airborne nitrogen deposition (Dentener, 2006) are kept fixed at 1901 levels, and nitrogen fertilizer and irrigation are set to zero. Details of the spin-up process are described in Gahlot et al. (2017). After the model spin-up, numerical experiments are conducted as transient runs from 1901 to 2016. To estimate the effects of external forcings, countryscale runs are conducted over wheat-growing regions in India by varying different input forcings (Table 1). The control run (S CON ) represents the model run from 1901 to 2016 with time-varying annual [CO 2 ], climate data, annual gridspecific nitrogen fertilizer, and full irrigation to fulfil the wa-ter needs of the crop. Four additional simulations are conducted by assigning a constant value to each input forcing one at a time. For instance, in S CO 2 , all input variables (temperature, nitrogen, and irrigation) are the same as in the S CON case except [CO 2 ], which is held constant at the 1901 level. The difference in model simulations from S CON and S CO 2 then gives the effect of elevated [CO 2 ] on wheat crop growth processes. Here we present the results only for the recent decades of 1980 to 2016.
Model performance at the country scale is evaluated by comparing the model simulated total wheat production at the country level with the FAOSTAT online database (2019) and the Directorate of Economics and Statistics, Ministry of Agriculture and Farmers Welfare (MAFW, 2017) data. The production for each grid cell is an area-weighted sum of production from irrigated and rainfed area fractions (Eq. S10).
To study the spatial variation in production, the wheatgrowing regions of India are divided into spring wheat environments (SWEs) based on the mega-environment concept (Chowdhury et al., 2019). For this purpose, we divide the wheat-growing regions of India into five SWEs (Fig. 2) based on temperature, precipitation, and area equipped for irrigation (Table 2) to identify regions with similar growing conditions for wheat. SWE1 (Fig. 2) represents mostly the Indo-Gangetic Plain that offers good access to irrigation for wheat, which is a non-monsoon crop. The growing season temperatures fall in the optimum range for wheat growth. SWE2, which is mainly comprised of the wheat-growing regions in the proximity of the Himalayas, is characterized by very low growing season temperatures and high rainfall. SWE3 represents the north-western parts of the country with moderate to high growing season temperatures, low rainfall, and small values of AEI. SWE4 represents the central parts of India and tropical wheat-growing regions with high temperatures and moderate growing season precipitation. SWE5 represents the crucial wheat-growing regions of the country where the conditions are similar to SWE1 but irrigation facilities are lacking. Wheat production for each of the SWEs are discussed further in the following sections.

Spring wheat model evaluation
The simulated magnitude and intra-seasonal variability in LAI for 2014-2016 compared well with the experimental wheat site at IARI, New Delhi (Fig. 1c).
The spatial distribution of the model-estimated wheat production at a country scale compared well, including the highly productive Indo-Gangetic Plain, with the data from Monfreda et al. (2008) for the year 2000 (Fig. 3). ISAMsimulated country-scale wheat production for 1980-2014 also compares well with production data from the FAOSTAT online database (2019) and MAFW (2017) datasets (Fig. 4) with correlation coefficients of 0.92 and 0.91, respectively, for the two datasets. However, the model-estimated production is slightly higher than both observed datasets. This may be attributed to the fact that the model is calibrated to the high-yielding wheat cultivars grown in recent years (2015)(2016). Hence, the model is a valid tool for studying interactions of wheat with its environment for recent years.

Effects of environmental and anthropogenic forcings at the country scale
In this study, we examine the effects of two environmental factors ([CO 2 ] and temperature change) and two land management practices (nitrogen fertilizer and water availability) on the production of spring wheat. The impact of these factors is quantified as the difference between the control and the experimental simulations (Eq. S11) described in Table 1.
Results show that during the 1980-2016 period, [CO 2 ], nitrogen fertilizers, and water available through irrigation have a positive impact on wheat production, but the impact of temperature is negative (Fig. 5) (Table 3). CO 2 fertilization is the most dominant factor that has contributed to the increase in wheat production in India. Annual average [CO 2 ] worldwide has increased from 337.7 ppm in 1980 to 404.3 ppm in 2016. This increase in levels of [CO 2 ] at the rate of 1.82 ppm yr −1 has promoted growth in wheat as elevated [CO 2 ] levels are known to enhance photosynthetic CO 2 fixation and have a positive impact on most C 3 plants (Myers et al. 2017;Leakey et al., 2009;Allen et al., 1996).  Our results show that for every part per million rise in the [CO 2 ] level the total wheat production of the country has increased by 0.37 Mt ( Fig. 6a; Table 3). This amounts to a 22 Mt (30 %) increase in production compared to the 1980-1984 period due to increased [CO 2 ] levels. A positive correlation coefficient of 0.97 between annual wheat production and annual CO 2 concentration confirms a positive impact of [CO 2 ] on wheat production. Other studies based on multiple approaches including experiments have also shown an increase in yield and growth of C 3 crops under high [CO 2 ] conditions (Dubey et al., 2015;Leakey et al., 2009).
Nitrogen fertilizers are added to the farmland to reduce nutrient stress on the crop. The use of nitrogen fertilizers is important in the Indian context due to two reasons. First, India is a tropical country where higher temperatures and precipitation cause the loss of nitrogen from the soil due to denitrification. Second, crop nitrogen demand is high because multiple cropping is widely practised. The average amount of nitrogen fertilizer added per unit area shows a positive trend of 2.71 kg N ha −1 yr −1 during 1980-2016. This implies an increase in total wheat production at the rate of 0.10 Mt Table 3. Temporal variations of different input forcings and their impacts on annual wheat production in India during the study period    (Sen, 1968).
for every kilogram of nitrogen per hectare added to the farm ( Fig. 6c; Table 3). This amounts to an 10.63 Mt (15 %) increase in production compared to the 1980-1984 period due to increased fertilizer application. Irrigation is a key factor for spring wheat in India where 93.6 % of the wheat area is equipped for irrigation (MAFW, 2017), and most of the irrigated area is concentrated in the Indo-Gangetic Plain. Unfortunately, data on the actual amount of water used for irrigation water are not available. Hence, in the S CON simulation, we consider every grid cell is 100 % irrigated so that the crops do not undergo water stress at any point in the growing season. This is to say that irrigation water required in the model is dependent on the water demand of the crop. With this condition, our results show that with all the regions 100 % irrigated, wheat production shows a positive trend during 1980-2016. Overall, there is a 8.47 Mt (12 %) increase in production compared to the 1980-1984 period due to increased irrigation.
The average air temperature for the months of the wheat growing season (October-March) during the study period (1980 to 2016) showed an increase at the rate of 0.026 • C yr −1 . Higher temperatures during the second half of the growing season is specifically known to produce smaller grains and low grain numbers Deryng et al., 2014). Our results have shown a decrease of 8.38 Mt (∼ 10 % reduction) of wheat per degree Celsius increase in average growing season temperature (Fig. 6b). This is higher than the global estimate of 6 % reduction per degree Celsius rise in mean temperature (Asseng et al., 2015). Studies have reported that wheat-growing regions at low latitudes are more susceptible to rising temperatures (Tack et al., 2017;Rosenzweig et al., 2014) since optimum temperatures in these regions have already been reached. Overall, there is a 13 Mt (18 %) reduction in production compared to the 1980-1984 period due to the rise in average growing season temperatures.
In the presence of all input forcings (S CON ), the trend of wheat production in India remains positive at 1.17 Mt yr −1 from 1980 to 2016.

Effect of environmental and anthropogenic forcings at the regional scale
It is clear that environmental and management factors significantly affect wheat production at a country scale. It is important to understand how these factors can affect production for different regions. For this purpose, the results of the control simulation (S CON ) with all the forcings are analysed for each of the SWEs shown in Fig. 2. A SWE is representative of similar climatic and environmental conditions regionally under which wheat is grown. One SWE differs from the other in terms of different temperature ranges, precipitation received, and irrigation availability. The S CON case is analysed to ensure that the input factors are fully implemented in the model-estimated production, and their effect can be studied effectively. One important thing to note is that irrigation in the model is calculated as the excess water demand required by the crop to grow in no-water-stress conditions. Hence, the S CON calculates irrigation as the ideal case scenario assuming that all the water demand of the crop is met. Overall, this analysis will identify the factors (environmen-tal conditions and land management practices) that predominantly drive the wheat production range in a given SWE. The results of this regional analysis are presented in Fig. 7, which shows scatterplots of production as a function of various drivers for each wheat-growing grid cell in the model. A similar plot showing the relationship between production, AEI, and wheat area is presented in Fig. 8. Together, these two figures allow us to understand how different environmental factors and management practices can affect production in different SWEs. Atmospheric [CO 2 ] is omitted from this analysis because it is assumed to be spatially uniform.
The Indo-Gangetic Plain region (SWE1) is the bestsuited environment for growing spring wheat in India due to favourable growing season temperatures (Fig. 7a), moderate to high fertilizer application (Fig. 7b), high availability of irrigation facilities (Fig. 8b), and moderate water demand (Fig. 7c). Hence, SWE1 is the major contributor to the annual total wheat production of India. Low temperatures (Fig. 7a) in the Himalayan foothill region (SWE2) result in the limited production of wheat in this region. High rainfall in growing season months is helpful, and, hence, limited amounts of water are required for irrigation (Fig. 7c) in this area. The arid north-western Indian region (SWE3) is very low in production due to the high temperatures (Fig. 7a) coupled with a lack of irrigation facilities (Fig. 8b) needed to mitigate the high water demand created by low precipitation. SWE4 in central and north-eastern India is also low in production due to high temperatures during the growing season (Fig. 7a) even though the water demand is low (Fig. 7c) due to moderate rainfall. SWE5 areas in south-central India have limited wheat production because of limited irrigation facilities (Fig. 8b) despite favourable temperature conditions.
Wheat production is directly proportional to the area on which wheat is cultivated in a given region or SWE (Fig. 8a). Figure 8b shows that wheat production is, in fact, positively correlated to AEI at the grid level. Since production in this analysis is derived from the S CON case and no AEI data are used in its calculation, it is interesting to see such a strong correlation between wheat production and AEI at grid level because they are two independent datasets. This can be explained by Fig. 8c that clearly indicates that availability of irrigation (high AEI) is a major factor that drives the area on which wheat is cultivated in a grid cell. Wheat, being a nonmonsoon crop, is highly dependent on the availability of irrigation in a region. For regions with high growing season temperatures, additional water stress is induced in the crop along with heat stress that limits crop production. Hence, the availability of favourable temperatures is crucial for ideal growing conditions for wheat. If irrigation can be made available in these regions, like in SWE5, the wheat cultivation area and wheat production can significantly grow in the years to come.
Similar to the analysis done for the country-scale impact of different factors, we quantified the impact of factors on different SWEs. The results of this analysis are summarized in Table 4. SWE1 and SWE5 are the two regions where the magnitude of trends in the change in wheat production with different input forcings are the highest (Table 4). The magnitudes of the impacts of forcings on SWEs 2, 3, and 4 are relatively small. This is because the analysis involves production that is calculated as the yield times the harvested area. The numbers in Table 4, hence, do not reflect changes per unit of harvested area.
While CO 2 fertilization, water added through irrigation, and nitrogen fertilizers are found to increase wheat production in SWE1 at 0.26 Mt ppm −1 [CO 2 ], 0.35 Mt (1000 mm) −1 , and 0.07 Mt (kg N) −1 ha −1 , respectively, production is found to decrease by 3.52 Mt for every degree Celsius rise in average growing season temperatures. It is found that water added through irrigation has a small yet negative impact on production in SWE2. This can be due to excess surface runoff in SWE2 that might lead to the washing away of nitrogen from the soil resulting in nutrient stress in the crop. The impact of different forcings is also found to be significant for SWE5, where [CO 2 ], irrigation, and nitrogen fertilizers have promoted wheat production at the rates of 0.07 Mt ppm −1 [CO 2 ], 0.41 Mt (1000 mm) −1 , and 0.01 Mt (kg N) −1 ha −1 , respectively. Irrigation is seen to have the most impact on wheat production in SWE5 out of all the SWEs.

Conclusions and discussions
This study explores the effects of environmental drivers and management practices on spring wheat in India using ISAM. For this purpose, we build and implement a dynamic spring wheat module in ISAM where (i) we parameterize and calibrate the equations in the C 3 crop model framework available in ISAM, (ii) develop new equations for dynamic planting time and heat stress, (iii) collect field data to calibrate and evaluate the model at site scale, and (iv) develop gridded datasets of wheat cultivated area, irrigation, and nitrogen fertilizer data to conduct country-scale simulations. The model is able to simulate the spatio-temporal pattern of spring wheat production at the country scale. This evaluation implies that the model can serve as a simulation tool to conduct numerical experiments to understand the behaviour of spring wheat.
In order to quantitatively study the role of environmental and anthropogenic factors, we conducted a series of numerical experiments by switching off one factor at a time. Our analysis focuses on the 1980-2016 period. Results show that the increase in [CO 2 ] has a positive impact on wheat production due to the CO 2 fertilization effect. Atmospheric CO 2 concentration has increased by 1.82 ppm yr −1 , and production has increased at a rate of 0.37 Mt for every part per million rise in [CO 2 ] since the 1980s, which translates into a 22 Mt (30 %) increase in countrywide production during the study period. This is consistent with observational stud-  ies such as Kimball (2016) that show an increase in yield of C 3 grain crops due to elevated [CO 2 ].
The application of nitrogen fertilizer has increased at a rate of 2.71 kg N ha −1 yr −1 , leading to increased production of spring wheat at the rate of 0.10 Mt for every kilogram of nitrogen per hectare added, which is equivalent to a 10.63 Mt (15 %) increase in countrywide production during the study period. Nitrogen deficiency is very high in India because of high consumption due to multiple cropping and nitrogen loss due to the denitrification of the soil aggravated by the tropical climate. Nitrogen fertilizer contributes to increased production by mitigating this nutrient deficiency.
Our model results suggest irrigation increase could have led to an increase in production of spring wheat at a rate of 0.31 Mt (1000 mm) −1 of water added, implying a 8.47 Mt (12 %) increase in countrywide production during the study period. Irrigation appears to be the most important factor controlling production across all the spring wheat environments. We note here that in our experiments irrigation is equivalent to "no water stress". This approach seems to be the best option because data on actual water use in irrigation are not available. In grid cells that are equipped for irrigation, we set the water stress term to zero. In reality, water stress may not go to zero in some areas where water or power availability is limited. In these areas, the model underestimates the simulated effect of irrigation on productivity.
Average growing season temperatures have increased by 0.026 • C yr −1 leading to a productivity loss of 8.38 Mt (∼ 10 %) per degree Celsius rise in temperature, which is equivalent to a 13 Mt (18 %) decrease in countrywide production during the study period. Crop heat stress is a major reason behind this loss. The optimum temperature for wheat is 25 • C in the reproductive stage. The heat stress effect is triggered in the model when the canopy air temperature higher than 25 • C and less than 35 • C reduces grain filling and negatively impacts the growth of storage organs. The observed 10 % reduction rate in production is higher than the global average of 6 % (Asseng et al., 2015) because the growing season temperatures in India are already near the upper limit of the optimal range.
The regional-scale analysis shows that SWE1 is the best environment for growing spring wheat in India due to favourable growing season temperatures, moderate water demand, and the availability of irrigation facilities. Hence, this region is the main contributor to the annual total wheat production of India. North-western India (SWE3), covering the states of Rajasthan and Gujarat, is the least productive region due to high growing season temperatures coupled with a lack of irrigation facilities needed to mitigate the high water demand created by low precipitation. Studies have concluded that in order to improve and represent crop growth processes in the models and to increase certainty in model-based assessments, there is a need for more focused regional-scale Table 4. Impacts of different external forcings on annual wheat production in the SWEs during the study period  studies (Maiorano et al., 2017;Koehler et al., 2013). This study is an attempt to work in a similar direction with a focus on wheat in India.
Apart from advancing our understanding of spring wheat growth processes, the crop model can also contribute to realworld decision-making. For example, our results show that wheat production in India has steadily increased at a rate of 1.17 Mt yr −1 from 1980 to 2016. This implies that the negative effect of rising temperatures was offset by positive contributions from other drivers. Our model can be used to conduct experiments to identify optimal solutions to future scenarios. Furthermore, using crop-specific models like the spring wheat model developed in this study will improve the simulation of crop phenology for agroecosystems. This will likely lead to better estimates of carbon fluxes and their spatio-temporal variability.
The Earth system is a non-linear system in which different components interact with each other. In this study, we used a process-based model that includes such interactions and feedbacks between different drivers. For instance, higher temperatures increase the crop water demand. Higher [CO 2 ] increases photosynthesis that also affects nutrient and water demand. Because of these interactions, the sum of the effects will not add up to 100 %. Moreover, the experiments conducted in this study are not exhaustive; there are other factors like relative humidity and solar radiation that might affect production.
There is scope for improving the crop model and the modelling framework. The processes involved in CO 2 fertilization need improvement to match the Free-Air CO 2 Enrichment (FACE) studies. The addition of new processes accounting for the effects of pests and multiple cropping will make the simulations more representative of the Indian situation. Better data will also improve the fidelity of the simulations. A key bottleneck in simulating crop growth at regional to global scales is the lack of irrigation water use datasets.
To the best of our knowledge, large-scale observation-based datasets of water used in irrigation do not exist even though there are numerous datasets for irrigated areas and areas equipped for irrigation (e.g. Zohaib et al., 2019). The development of irrigation water use datasets will reduce the uncertainty in simulating the effect of water stress on crop production. Equipped with these improvements, ISAM can become an indispensable tool for informing policy on food security and climate change adaptation.
Code availability. ISAM model code is available upon request.
Author contributions. SG, AKJ, and SBR conceptualized the study. SG, TSL, and AKJ designed the numerical experiments and generated the input datasets. SG conducted the numerical experiments and analysed the outputs. VKS and RD collected the field observations. SG, AKJ, and SBR wrote the paper.
Competing interests. The authors declare that they have no conflict of interest.
Review statement. This paper was edited by Govindasamy Bala and reviewed by three anonymous referees.


text:
A 20-Year Analysis of the Evolution of Automatic Milking Systems: Processes, Technologies and Livestock Environment

: Over the last 20 years, the dairy industry has implemented new technologies related to automatic milking systems (AMS). AMSs have the potential to maximize milk production and animals’ welfare thanks to voluntary milking access, as well as to increase the resource efficiency and environmental sustainability of dairy farms. In this study we assessed the state of the art of research on AMS through a systematic review of patent trends in the last two decades. Patents from the last 20 years were extracted from the EspaceNet database. Terms appearing in titles and abstracts of a total of 154 patents were processed using a text mining approach, ignoring low-frequency and meaningless words, and including stemming analysis to aggregate variant forms of the same word. Four clusters were identified: Components, Sensors, Process and Animal. The results showed that the highest number of patents was yielded in the early 2000s, thus indicating great interest in AMS in the initial period. The cluster trend pointed out that the focus on the animal and sensing technologies has been constant over time. In recent years, the priority of research has shifted towards process efficiency and components. Detailed analysis of clusters allowed us to appreciate an increasing interest in the animals’ health and body conditions over time (+249% and +391% from 2000 to 2019, respectively). The processes which showed increasing relevance were the ones related to the cleaning of facilities (+291%). The study of new sensing technologies has focused primarily on imaging, allowing researchers to develop new decision models (+348%). The results suggest that AMS patents are moving their attention towards more efficient and sustainable systems. This trend represents an important opportunity for a significant increase in the sustainability of the dairy sector, not only for animals but also for the farmers through the efficient use of the resources, thus enhancing the con-sumer’s perception of sustainability.

Introduction
The use of automatic technologies is a growing trend in the livestock industry [1,2], and plays a key role in future prospects [3]. Automatic milking systems (AMS) are one of the most significant technological changes in the dairy industry [4], and the number of commercial farms milking with AMSs has increased rapidly in the last few years [5]. In fact, from 2011 to 2014, around 25,000 farms have installed an AMS [6], and nowadays the process can be considered a well-established technology [7]. AMSs have the potential to maximize milk production and animals' welfare thanks to voluntary milking access, as well as to increase the resource efficiency and environmental sustainability of dairy farms [8,9].  This study assesses the state of the art of research on AMS through a systematic review of patent trends in the last two decades (2000-2019) in order to identify research tendencies and critical gaps. In total, 154 patents were extracted from the EspaceNet database and processed using a text mining approach.

Methods
The systematic review was carried out by extracting the patents of the last 20 years from the patent server EspaceNet (www.epo.org). Patents were searched using different synonyms to define the AMS and filtered by publication date (2000-2019). Once extracted, the patents were organised into five groups of four years.
The content of "Title" and "Abstract" were processed with the text mining process. Analyses were carried out using Microsoft Excel and GraphPad Prism 8.0.0 (GraphPad Software, Inc., La Jolla, CA, USA). Titles and Abstracts were pre-processed to improve the quality of information retrieval [10,11]. Meaningless features, such as punctuation marks, numbers and symbols, were removed by applying a tokenization process. The list of single words resulting from tokenization was further perfected by deleting the low-frequency words and connectors. Finally, word-sense disambiguation and the stemming processes were applied. These processes allowed for clarification of the ambiguity of acronyms and included in a single lemma all variant forms of the same word.
After the pre-processing, the words included in the dataset were grouped into four clusters: "Animal", "Process", "Sensors" and "Components". First, the trend of the four clusters over the years was analyzed. Then, a word frequency analysis was carried out within each cluster to identify the patent trend tendencies. Frequency analysis was also applied to the applicants to determine the top contributing countries.

Results and Discussion
The trend of the patents awarded over time is reported in Figure 1. The chart reports the number of patents starting from 1996, as the employment of AMS in farms dates to the 1990s. The number of patents reached the maximum number in 1998-1999, when the first voluntary milking systems were commercialised [12]. After a sharp decrease, the patent registration has stabilized, despite natural oscillations.
The highest number of patents was granted by the Netherlands (33.6%), followed by Sweden (14.3%) and China (9.7%). The text mining process allowed us to identify the following main clusters: Components, Sensors, Process and Animal, weighting 30%, 29%, 25% and 16%, respectively. As reported in Figure 2, the trend of the clusters was not constant over the 20 years. Initially, the research focused on sensors to support high-technology milking systems [13]. In the last 10 years, the development of sensors has been incorporated into the improvement of the efficiency of the process. Meanwhile, the target of the patents has moved towards components. As concerns the single clusters, the frequency analysis of the cluster "Animals" showed that after the initial implementation of the feeding, interest has recently focused on the dairy barn. Moreover, more consideration has been given recently to the animals' body condition/weight and health (+249% and +391% from 2000 to 2019, respectively). To maintain high-efficiency production and ensure animal welfare, health-monitoring equipment is strongly required [14], and the evolution of the AMS has also involved the use of automated weighing scales [15] and more recently optical-based techniques [2].
Confirming these findings, the topic with the highest increase in the last decade within the cluster "Sensors" was related to imaging techniques. Models are strictly connected with sensing technologies, which support farmers' decision-making. In fact, the models showed a 348% increase from 2000 to 2019.
As regards the cluster "Process", the analysis highlighted the increasing relevance of the management of the cleaning operations for facilities. For example, the use of the word "water" increased rapidly from 2000 to 2019 (+291%). Moreover, the implementation of pulsation technologies ("pulsation" +65% from 2000 to 2019) reflects the evolution toward more efficient milking units, ensuring udder health [16].
Within the cluster "Components", the word frequency analysis showed that the industry spent the first decade perfecting the teat cups (−44% from 2000 to 2019), whereas in the second decade it focused on storage systems, e.g., improving tanks and pumps. Moreover, in the second decade, several patents were registered about the milking arm.

Conclusions
In this study, we analyzed the AMS patent trends over the last two decades. The systematic review approach allowed us to identify research tendencies and gaps. Cluster analysis showed that the AMS industry is focused on the implementation of more efficient and sustainable systems. The evolution of components, sensors and technologies ensures both compliance with high-quality products and animal welfare. Topics related to the animal aspect are still underdeveloped, but their increasing trend allows us to expect a progressive evolution in relation to the issue of animal welfare.

Data Availability Statement:
The data presented in this study are available on request from the corresponding author.

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