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DEVELOPMENT STRATEGY OF RICE ORGANIC FARMING SUSTAINABILITY TOWARDS FOOD SAFETY: A CASE STUDY IN KEDIRI-INDONESIA

Organic farming is a way of farming that does not use chemicals as fertilizers and pesticides. In this way, what comes from the soil is returned to the soil so that the sustainability of the environment is maintained and the quality of the plant produced is guaranteed. This is in contrast to conventional rice farming that is still using fertilizers and chemical pesticides that are easily dispersed to obtain great results of harvest yield. This study aims to understand: (1) the characteristics of organic rice farmers; (2) organic farming implementation; and (3) to give recommendation strategy to develop organic rice farming in Kepung Subdistrict Kediri. This is a descriptive-qualitative research, primary data was collected through interview with 30 farmers in May-June 2017. Data were analyzed descriptively by using information on farming system conducted by farmer, then, SWOT analysis was used to formulate strategy. The results showed that although Kepung District has LSO as organic certified region on rice, but some farmers still conducting conventional farming. Farmers are also find difficulty in market their product. Development strategies can be focused on strengthening farmer group institutions especially in seraching for wider target market.

Organic farming is an agricultural technology package that not only concerns to the availability of food in the form of productivity but also environmentally friendly. It is a technique that relies on natural ingredients without the use of synthetic chemicals. Anorganic fertilizers or chemical fertilizers are considered as the cause of pollution of ground water such as rivers and lakes. It is, in addition, also lower soil quality especially if the use of it is not appropriate (Syam, 2008).
The main objective of organic farming is to provide agricultural products, especially food that is safe for the health of producers and consumers and environmental friendly. Healthy lifestyle has been institutionalized internationally that requires assurance that agricultural products must be safely consumed, have high nutrients and be environmentally friendly. Consumer preferences like this and economic development cause the demand for organic agricultural products of the world that increased rapidly (Mayrowani, 2012).
The development of organic agriculture in Indonesia has been encouraged by the current government through Regulation of the Minister of Agriculture No. 64 of 2013 on organic farming system. To cope well with the opportunities, the challenges faced in the development of rice in the future are how domestic rice production can compete in the export market. To meet the market demand is through the development of organic rice. In Indonesia the development of organic rice is a small part of the agribusiness, but certainly the trend is always increasing from year to year. The development of organic farming area in Indonesia can be seen in table 1.
Based on table 1, it is known that from 2012 to 2015 the area of land for organic farming has fluctuations. The wider area of organic agriculture is expected to provide broad benefits in meeting the needs of the community for healthy and safe food. Demand of organic rice in Indonesia is fluctuate from 28,95 tonnes to 73,55 tonnes (2011-2014) and then grows-up in 2015 to 2016 (see Table 2), it needs to be paid attention as the opportunity to export agriculture product abroad. While in the domestic, the opportunities of increasing consumer demand for organic food cannot be utilized optimally because the production tends to remain and the area of organic certified land has not been maximal. Table 3 shows consumer demand in a case study of organic red rice production in Subak Wongayabetan Bali.   The position of rice as the staple food of the people of Indonesia makes rice commodity as the main commodity in organic agriculture which is developed. In the Statistics of Organic Agriculture Indonesia (SPOI) noted that in 2010 the area of organic rice reaches 3000 ha that have been certified. Organic rice is processed into organic rice is a food that is easily converted into energy, in addition to contains enough nutrients and boosters for the body (Mayrowani,2012).
Kediri Regency is one of the potential areas for the development of organic rice that can contribute in realizing organic food. Some locations of organic farming in Kediri Regency are located in Kepung and Semen Subdistricts. Geographically the location of the sub-district is located in the upstream area with the condition of natural resources and ecosystem that is still pure, so that the area is potential for the development of organic farming. The average production of organic rice farming in Kediri regency is 8,098,36 kg per hectare per year and this can be categorized as sufficient production (Kurniawan, 2016). Based on the background, this research has aim to understand (1) the characteristics of organic rice farmers, (2) organic farming implementation and (3) to give recommendation strategy to developing organic rice farming in Kepung Subdistrict Kediri.

LITERATURE REVIEW
Rice is a plant that belongs to the genus Oryza L. which includes approximately 25 species spread in the tropics and subtropical regions. Rice is one of the food crop varieties that can be cultivated organically. The Ministry of Agriculture has set standards for organic agriculture in Indonesia, contained in SNI 01-6729-2002 and has been revised to become SNI for Organic Food System The organic farming system adheres to the Organic Process concept, meaning that all organic farming system processes starting from land preparation to post-harvest meet the standards of organic cultivation, not seen from the organic products produced. Organic rice cultivation will produce rice that is free of pesticide residues and chemical fertilizers. Apart from being environmentally friendly, the cost of planting is very low because the fertilizers and pesticides used come from nature around the farmers (Agricultural Human Resources Development and Development Agency of the Republic of Indonesia, 2011).
The way to grow organic rice is basically not different from conventional rice cultivation (non-organic). The only difference is in the selection of varieties and the use of basic fertilizers. Not all suitable rice varieties are cultivated organically. Rice varieties that are suitable to be planted in 9 organic species are only natural varieties. The 2 types of organic rice varieties are Cintanur and Ciherang. Organic fertilizers that are often used to fertilize plants are compost. Compost is an organic fertilizer that comes from the rest of plants, animals, and organic waste that has undergone a process of decomposition. The total conversion of fertilizers does not use inorganic fertilizers such as urea, TSP or KCl at all. Organic rice requires 4 tons of manure and legume compost / ha. Control of pests and diseases of organic rice plants can be done by: (1) Mechanical control is carried out by directly capturing pests or using traps; (2) Control by technical culture is carried out by planting host plants around organic rice fields; (3) Control using organic pesticides which can control pest stink pest, stem borer, brown planthopper, and green planthopper. Organic rice has good market prospects, because organic rice farming has the opportunity to continue to be improved and allows to produce higher profits compared to non-organic rice farming. Organic rice cultivation can be done by utilizing the surrounding environment so that input costs become cheaper. Besides that, the price of organic rice is higher than the price of nonorganic rice (Directorate General of Food Crops, Ministry of Agriculture of the Republic of Indonesia, 2015).
To find out the financial benefits of a farm, it can be known by using incremental B / C Ratio, which is the difference in revenues with costs ( The qualitative approach in the SWOT matrix displays eight boxes, namely the top two are boxes of external factors (opportunities and challenges), while the two boxes on the left are internal factors (strength and weakness) (Rangkuti, 2006).
Formulation of the Strategy for the Development of the Benefit Sharing Model in the MSME financing scheme of the Organic Rice Institution Agribusiness sector in Kediri Regency is done by means of integrating the internal and external factors contained in the SWOT matrix. The other four boxes are boxes of strategic issues that arise as a result of meeting points between the internal and external factors of institutional organic rice.
Menurut Rangkuti (2006), SWOT is used in identifying various factors systematically for later use in strategy formulation. The combination of internal and external factors will greatly affect the performance of the company. SWOT is applied by comparing external factors in the form of opportunities and threats with internal factors in the form of strengths and weaknesses of the company or organization. Determination of alternative development strategies should be formulated after the institution or organization analyzes the quadrant which conditions and position of the current institution or organization. This is intended, so that alternative alternatives are in accordance with the internal and external conditions of the institution in this case micro, small and medium enterprises at this time.  (Rangkuti, 2006) In quadrant I, a company is advised to use an aggressive strategy. The situation of quadrant I company is very profitable, because the location of the company that is in a condition has the opportunity and strength so it is necessary to be able to take advantage of the opportunities that exist.
In quadrant II, the company's strategy is a diversification strategy. Companies facing a situation must face various threats by using internal strength, so that they need a strategy that is able to take advantage of long-term opportunities.
In quadrant III, the appropriate strategy to implement is turn around. In this condition the company has a large market opportunity but is accompanied by the emergence of considerable internal constraints / weaknesses. Companies need to formulate strategies that allow reduction of their internal weaknesses to be able to take advantage of better opportunities.
In quadrant IV, a defensive strategy is needed. The company experiences difficult conditions where it must face various threats and internal weaknesses of the company. The most important thing to do is that companies must be able to defend themselves.

METHODS OF RESEARCH
This is a descriptive-qualitative research located in Kepung Sub district Kediri. It uses primary data related to production factors, costs and revenue was collected through interview with 30 farmers in May-June 2017. Data were analyzed descriptively by using information on farming system conducted by farmer, then, SWOT analysis was used to formulate strategy. SWOT is strategic planning tool consists of elements such as strength, weakness, opportunity, and threat. It is done by determining each elements including the weight and rating. The next step is counting the total score of every elements to determine the best strategy (Rangkuti, 2006).

RESULTS AND DISCUSSION
Characteristics of organic rice farmers. Farmers of indonesia has different characteristic. These characteristics can be demographic, social character and character based on farmer's economic condition itself. These characteristics distinguish the type of peasant behavior in certain situations. Characteristics observed in this study were age, education level, sex, and land area.
Organic rice farmers are mostly men who are their in middle age (43%) and young (40%). While only 17% of them are old in term of age. A person's age determines the person's work performance. The older the workforce the absorptive power and the comprehension of innovation -more difficult new application of agriculture to accept. But in terms of responsibilities the older the workforce will not give significant influence because they are more experienced.
Education will affect behavior and adoption rate of an innovation. Someone who is well educated tends to be more open to accept and try new things. Education is a learning tool, which instills a sense of attitude and also beneficial to the development of more modern agricultural practices. In this research, farmers are mostly has mid level of education (77%). However, they have the knowledge of farming mostly from informal education more than formal education such as from agricultural extension.  Table 4 also shows that most farmers are the land owner, however they have small scale of area (80%) that is only 0,006 -0,67 hectares while the rest of them has middle even high land area width. Farmers of organic rice use not all part of their land to conduct organic rice farming. Most of them (97%) use only small part of their land, while the rest of it (3%) uses all part of their land. From this fact it can be seen that organic farming is not yet becoming their priority, however this will give opportunity for agricultural extension worker to motivate them to conduct organic farming for better ecosystem.
Organic farming implementation in Kepung Kediri. The soil is plowed as deep as 25 -30 cm, then the remains of plants and grasses are buried. Grounding with rake is done until a perfect mud structure is formed, then the mud is flattened so that w height is evenly distributed. Fertilization with organic fertilizer (manure, compost and green manure) is done to prepare a good land for plants.
To get a good quality seeds then the selection of seeds. Figure 4 shows the type of seed used in organic rice farming. The seeds are produced by farmers or labeled seeds.
The seedbed is done by preparing the nursery where it is coated with banana leaf that has been softened, then it is given fertile soil mixed with compost (ratio 1: 1), th nursery around 4 cm. Then, the Seed is sown into the seedbed and covered with thin ground. Figure 4 Seedlings are planted at the age of number of seeds per hole one, and shallow 1 Fertilization has not been done purely using organic fertilizer, partly using an organic fertilizer. But the amount of organic fertilizer used is relatively more benyak than an organic. Grounding with rake is done until a perfect mud structure is formed, then the mud is flattened so that when it is irrigated, the height is evenly distributed. Fertilization with organic fertilizer (manure, compost and green manure) is done to prepare a good land for plants.
To get a good quality seeds then the selection of seeds. Figure 4 shows the type of eed used in organic rice farming. The seeds are produced by farmers or labeled seeds.
The seedbed is done by preparing the nursery where it is coated with banana leaf that has been softened, then it is given fertile soil mixed with compost (ratio 1: 1), th cm. Then, the Seed is sown into the seedbed and covered with thin Seedlings are planted at the age of 5-15 days (leaf two) after the seedlings, with the number of seeds per hole one, and shallow 1-1.5 cm, and the root position like the letter L.
Fertilization has not been done purely using organic fertilizer, partly using an organic unt of organic fertilizer used is relatively more benyak than an organic. To get a good quality seeds then the selection of seeds. Figure 4 shows the type of eed used in organic rice farming. The seeds are produced by farmers or labeled seeds.
The seedbed is done by preparing the nursery where it is coated with banana leaf that has been softened, then it is given fertile soil mixed with compost (ratio 1: 1), the height of cm. Then, the Seed is sown into the seedbed and covered with thin 15 days (leaf two) after the seedlings, with the 1.5 cm, and the root position like the letter L. Fertilization has not been done purely using organic fertilizer, partly using an organic unt of organic fertilizer used is relatively more benyak than an organic.
Weeding is done by using a rotary weeder, or with any kind of equipment in order to eradicate weeds and simultaneously enrich the soil. Weeding done as much as 3 times or more.
Harvesting is done after the mature organic rice is characterized by yellowing all the grain evenly or cooking grain. Harvesting is done when the organic rice grain has completely filled. When calculated from the nursery, the age of organic rice harvest is shorter than the conventional way. Table 5 shows average production, cost, revenue and income. It also shows that organic rice cultivation in Kepung sub district is feasible in price of Rp 15.000 -20.000 because in this way, farmers will get income as much as Rp 25.585.613. In general, the emphasis on institutional activities of farmer groups is not to provide capital services for its members, but to the management of collective resources (eg irrigation arrangements and planting schedules) and social life as practiced by KUD Kelompok Tani Budidaya. This KUD is able to produce organic fertilizer sold in general with the price of (Bokasi Trikodermin fertilizer) Rp.30.000 / sack, Organic liquid fertilizer from goat urine is Rp.40.000, Liquid smoke (for pest spray) is only for Rp.40.000 / liter and Local mikro organic (MOL) is Rp.30.000.
As a post-harvest institution, KUD of Budi Daya farmer group has served as a regulator of the selling price of organic rice, in order not to experience a sharp price declining during the harvest. While related to marketing, KUD of Budi Daya Farmer Group has had simple equipment in the process of packing organic rice. For pure organic rice products, farmer groups sell for around Rp.17.000-20.000 / kg under the Parikesit trademark. Organic rice has local label called "Parikesit" 3 0,05 0,15 2.
There is good institution to support the farm management of organic paddy 4 0,1 0,4 4.
There is government support in term of agricultural extension that is regularly done to motivate farmers in planting organic paddy 4 0,1 0,4

5.
Integrated farming has been done by farmers to support organic paddy cultivation 3 0,1 0,3 Weaknesses (0.60) There is not yet patent label and it is still produce locally.
The continuity of organic rice availability is still low, due to the lack of interest and motivation of farmers Government as a policy maker has a strategic role in the development of organic rice in Kediri region, especially related to the dissemination of innovation through extension activities. Agricultural extension has been done by Mr. Sunandar who has short-term program to apply organic cultivation technology, because not all farmers understand the cultivation. As many as 70% of farmers have implemented organic cultivation well and this will be continued in the long term, that is farming by restoring the ecosystem balance system. Extension workers also develop strategies related to how to communicate innovations to be easily digested by farmers through the best extension methods.
Strategy to develop organic rice farming in Kepung Subdistrict Kediri. People's desire to switch to organic produce causes the demand for organic agricultural products to grow rapidly. Organic farming is a solution to restore the health of the land and the resulting product. Based on the previous discussion it can be seen that organic rice cultivation is feasible, but farmers tend to be afraid to take risks and this can be seen from the narrowness of land used for organic farming of the entire land area owned by farmers. SWOT analysis in table 6 and 7 shows internal and external factors need to be paid attention to develop organic rice farming in Kediri.
From internal and external factors shown in table 6 and 7, it can be seen that agribusiness of organic rice in Kediri is categorized in condition that is weak and full of threat (-0,4,-1). The best strategy is Defense Strategy. This strategy is based on defensive activities and seeks to minimize weaknesses and avoid threats. The WT strategy aims to reduce internal weaknesses by avoiding external threats. Organic farming has a long process to do while farmers need income to meet family needs  There is high competition of market with non-organic rice, because organic rice is still very expensive; Land degradation due to the use of chemical or fertilizers; Organic farming has a long process to do while farmers need income to meet family needs; Conversion of agricultural land for the benefit of settlement.

Strengths
Organic rice has local label called "Parikesit"; The number of farmers as organic paddy grower can increase; There is good institution to support the farm management of organic paddy; There is government support in term of agricultural extension that is regularly done to motivate farmers in planting organic paddy; Integrated farming has been done by farmers to support organic paddy cultivation.
SO Strategy marketing subsystem in KUD Budi Daya can be improved by cooperating with suppliers such as supermarket; taking licensing and getting certified so as to reach broad consumers; increase the implementation of integrated farming to support organic farming, and maximize institutional function as a place for marketing by establishing cooperation with the government for example in terms of providing equipment for post-harvest process and packaging.

ST Strategy
Regular agricultural extension is important to motivate farmers in conducting organic cultivation and also in terms of expanding organic land to increase crop productivity; increase the role of KUD to accommodate organic rice harvest and also marketing; KUD can also work with the government to get the aid of packaging equipment, trade licensing, and also marketing to reach consumers widely. Table 8 Continue Weaknesses There is not yet patent label and it is still produce locally; The continuity of organic rice availability is still low, due to the lack of interest and motivation of farmers; Farmers have difficulties in marketing organic rice widely because of constrained knowledge and lack of licensing as well as the difficulty in accessing the capital; The practice of organic farming implementation has not been done purely, there are still the use of chemicals substances such as the using of fertilizers and chemical pesticides; Farmers produce organic rice only by orders.
WO Strategy certification tests need to be done to patent labels and obtain organic nutritional content so it is worth selling; Agricultural extension needs to be done to improve farmers' knowledge about good agricultural cultivation; cooperation with stakeholders for example to overcome the weakness of marketing also by increasing the role of agricultural academics.

WT Strategy
Nutrition testing needs to be done to raise awareness that organic products have health-intensive content; counseling needs to be done by involving comparative studies to places that have been carrying out organic cultivation and proven to be profitable thus increasing the farmers expectations; overcome land degradation by expanding the land for organic farming; expand the reach of marketing to demand not only through orders alone, such as cooperation with supermarkets and online marketing.
Source: Primary data, 2017. Table 8 shows the best strategy (Defense strategy) and it consists:  Nutrition testing needs to be done to raise awareness that organic products have health-intensive content;  Counseling needs to be done by involving comparative studies to places that have been carrying out organic cultivation and proven to be profitable thus increasing the farmers expectations;  Overcome land degradation by expanding the land for organic farming;  Expand the reach of marketing to demand not only through orders alone, such as cooperation with supermarkets and online marketing.

CONCLUSION
Most farmers are in middle and old age, with 100% of land owner, this is generation with low knowledge. However they have more than 5 years of experinence in farm management that helps them master the knowledge of organic plantation easily.
Farmers of Kepung Kediri still not yet implemented 100% pure organic activity, this is shown by the using of production factors especially for manure and pesticides. Agriculture institution has big role in helping to provide input and marketing the product by orders.
Turn Around strategy is the best strategy in handling organic farm management of rice. Synergy between stake holders is needed in empowering organic farmers through the Community Empowerment Strategy such as partnership in marketing. Development strategies can be focused on strengthening farmer group institutions especially in seraching for wider target market.


extfieldsofstudy: Medicine Biology

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

text:
LG biplot: a graphical method for mega-environment investigation using existing crop variety trial data

Due to the presence of genotype by environment interaction (GE), no crop cultivar performed the best in all regions. Therefore, the growing regions of a crop must be divided into sub-regions or mega-environments, and specifically adapted cultivars must be bred and deployed in each mega-environment. Meaningful mega-environment delineation must be based on repeatable GE patterns, which can be extracted from multi-year, multi-location crop variety trials. In regional crop variety trials, usually the same set of genotypes are tested across locations within a year, but different sets of genotypes are tested in different years, leading to highly unbalanced multi-year data. Such data are abundant for all crops and regions; but there has been no way to fully utilize them for mega-environment delineation. This paper presents a new method that allows utilization of existing variety trial data to identify repeatable GE patterns, to delineate mega-environments, and to understand the scope of unrepeatable GE at a location and within a mega-environment.

www.nature.com/scientificreports www.nature.com/scientificreports/ the biplots) allow the following interpretation: the cosine of the angle between two locations in the biplot approximates the Pearson correlation between them across tested genotypes ( Table 1). The closeness of the approximation is related to the goodness of fit of the biplot, which is also shown on the upper left corner of each biplot (Fig. 1). An acute angle means positive correlation, an obtuse angle means negative correlation, and a right angle means lack of correlation between two locations across tested genotypes for the trait of interest (here grain yield). Both Fig. 1 and Table 1 can be used to understand the yearly relations among test locations. While the numerical values in Table 1 are more accurate, Fig. 1 allows a quick grasp of the main yearly patterns. From example, Fig. 1 shows that there were some negative correlations among locations (obtuse angles between locations) in each of the five years, indicating strong GE. The problem with both Table 1 and Fig. 1, however, is that it is difficult to extract the common patterns across years; that is, it is difficult to separate repeatable GE from unrepeatable GE, which is essential for meaningful mega-environment delineation.
the LG biplot to show repeatable and non-repeatable Ge. The LG biplot (Fig. 2) is a graphical presentation of the yearly correlations among locations in Table 1. It may be viewed as a chart that stacks the location markers from each of the five GGE biplots (Fig. 1), with the location-year markers aligned according to their interrelations. The yearly patterns shown in Fig. 1 were largely retained in Fig. 2. An added function of the LG biplot is that it allows visualization of the similarity (repeatable GE) and variability (unrepeatable GE) of a location in correlation with other locations across years, a function similar to the GGE-GGL biplot 2 .
A more functional form of the LG biplot is presented in Fig. 3. It is the same biplot as in Fig. 2 but shows the mean placement of each location and its distribution in different years. The placement of a location was determined by the mean coordinates of all trials conducted at the location, as was done in the GGE-GGL biplot 2 . For example, the placement of "OTT" was determined by the placements of the three trials conducted at Ottawa in 2008, 2009, and 2010, indicated by OTT_08, OTT_09, and OTT_10, respectively (Fig. 3). The following information can be visualized from Fig. 3: First, the 11 locations involved in the 2006 to 2010 trials fell into two distinct groups (mega-environments); the GE between the two mega-environments were, therefore, repeatable GE. The first group (the southern mega-environment) included five locations: three Quebec Zone-1 locations (NDHY1, STRO1, and STS1), a Quebec Zone-3 location (LAPO3), and the Ottawa Ontario location (OTT). The other group (the northern mega-environment) included six Zone-2 or Zone-3 locations (PINT2, PRIN2, STAU2, CAU3, HEB3, and NORM3). This is fully consistent with the conclusion based on GGE-GGL biplot analysis 2 . The grouping of LAPO3 with the Zone-1 locations demonstrates the superiority and essentiality of mega-environment delineation based on repeatable GE patterns. The biplots were based on location standardized data (Scaling = 1, Centering = 2) and the singular values were partitioned entirely to the location vectors to focus on the relations among test locations (SVP = 2). The genotypes are shown as "+" for clarity. See Table 1 for the full location names.
www.nature.com/scientificreports www.nature.com/scientificreports/ Second, the correlations between trials (location-year combinations) from different mega-environments were mostly negative, ranged from highly negative (wide obtuse angles) to highly positive (small acute angles); as a result, the two mega-environments were slightly negatively correlated. This indicates that different cultivars must be selected and recommended specifically to each mega-environment. Alternatively, a "super" cultivar must be developed that is best for both mega-environments.
Third, large yearly variation existed in the placement of each location in the biplot (see the OTT location as an example). As a result, two locations in the same mega-environment may not be closely correlated every year even though they rank genotypes similarly across years. The sum of the yearly variations for individual locations within a mega-environment represents the unrepeatable GE. Its presence demands multi-year multi-location tests to identify superior and stable cultivars for the mega-environment. Its magnitude determines how many years and locations are needed for reliable genotype evaluation 3 .
The LG biplot based on the grain yield data from the 2014 to 2018 Quebec oat trials (Fig. 4) supports the conclusions from the 2006 to 2010 data (Fig. 3). Despite dramatic breeding progresses and possible climate changes   www.nature.com/scientificreports www.nature.com/scientificreports/ occurred between the two periods, the two location groups observed in Fig. 3 remained obvious in Fig. 4. The first group (the southern mega-environment) consisted of NDHY1, STHU1, LAPO3, and OTT, and the second group (the northern mega-environment) consisted of three Zone-3 locations (CAUS3, HEBE3, and NORM3) and three Zone-2 locations (STAU2, PRIN2, and STFR2). A difference is that the two mega-environments tended to be positively correlated in Fig. 4, as opposed to the negative correlation in Fig. 3. This was because the introduction of some more widely adopted cultivars such as Nicolas and Akina in recent years (see more in Discussion). Note that the Zone-3 location LAPO3 again fell with the southern locations (NDHY1, STHU1, and OTT), rather than with the other Zone-3 locations.

Discussion
Implication of mega-environment delineation. The purpose of mega-environment delineation is to utilize repeatable GE in plant breeding and crop production. This is achieved by breeding and deploying specifically adapted crop cultivars according to mega-environments. The GGE biplots presented in Fig. 5, which approximates the grain yield data of 15 oat cultivars tested throughout 2014 to 2018, illustrates this point. When viewed across all trials conducted during 2014 to 2018 (i.e., the whole region), the highest yielding cultivars was Nicolas, closely followed by Akina (Fig. 5a). The arrow on the single-arrowed line (the average environment axis or AEA) points to higher mean yield across trials, and the arrows on the double-arrowed line point to higher instability across trials 10 . Figure 5a shows that Nicolas yielded very well in trials above the AEA such as LAPO3_14 but not so well in trials below the AEA such as NORM3_18. When the yield data were summarized by mega-environment, Nicolas showed outstanding mean yield and stability across all trials in the southern mega-environment, clearly better than any other cultivars (Fig. 5b). Therefore, Nicolas can be recommended without hesitation to this mega-environment. In the northern mega-environment, while Akina and Nicolas were still the highest yielders on average, they were not the highest yielders in about half of the trials. Instead, Nice, Canmore, and/or Richmond were the highest yielders in these trials (Fig. 5c). Thus, Nice, Canmore, and Richmond showed contrasting responses to the environments within this mega-environment, in comparison with Akina and Nicolas (Fig. 5c). Therefore, to stabilize the oat yield in this subregion within and across years, Nicolas, Akina, Nice, Canmore, and Richmond should all be recommended to buffer the large and unpredictable GE. Note that Nice and Canmore yielded only about average across all trials (Fig. 5a) and yielded among the poorest in the southern mega-environment (Fig. 5b). Considering that the northern mega-environment is a key oat production area in Quebec, this understanding has important implications on oat production in Quebec.  (Table 1). No scaling or centering was applied ("Scaling = 0", "Centering = 0") before subjecting the correlation table to singular value decomposition so that the biplot approximates the correlation values. The singular values were partitioned entirely to the location-year vectors ("SVP = 2"). The rows in Table 1 (trials) are presented in red and as location-year combinations. For example, OTT_10 means the trial at Ottawa in 2010. The columns in Table 1 (the locations) are presented in blue. See Table 1 for full location names.
www.nature.com/scientificreports www.nature.com/scientificreports/ An alternative LG biplot. The model setting of no-scaling and no-centering ("Scaling = 0" and "Centering = 0") in the LG biplots (Figs 2-4) allows visualization of the correlation coefficients between locations or trials (such as Table 1). When the LG biplot does not adequately display the correlation table, or when separating the locations, rather than visualizing the correlations between locations, is the focus, a LG biplot based on grand-mean centered correlation values ("Scaling = 0" and "Centering = 1"), referred as the "LG1 biplot", may be used as a supplement. This LG1 biplot may be able to reveal the dissimilarities among locations that are masked by inclusion of the grand mean in the LG biplot based on un-centered correlation values. Presented in Fig. 6 is the LG1 biplot based on the 2014 to 2018 dataset. Instead of showing two groups of mega-environments in the LG biplot (Fig. 4), it shows two subgroups of locations within the northern mega-environment (on the left side of the biplot): CAUS3 and PRIN2 versus the other locations. It must be noted that the LG1 biplot cannot be interpreted as approximating the correlations between locations or trials in ranking genotypes and should only be used only as a supplement to the LG biplot. some pitfalls in LG biplot analysis. In the correlation table used to generate a LG biplot (e.g. Table 1), the correlation of a trial with itself was assigned to "1". While this is logical and standard, it may create an artifact to suggest that each location is a mega-environment by itself when only a few (e.g. three) locations are included in the analysis. In fact, when there are only two or three locations, the relations among them can be easily understood from examining the yearly GGE biplots (e.g. Fig. 1) or the correlation table (e.g. Table 1), and there is no need to resort to a LG biplot.
Another issue that needs attention is that in LG biplot analysis genotypes are treated as random samples of a genotype population. When this assumption is violated, i.e., when only a few genotypes are tested each year or when tested genotypes are dramatically different between years, location grouping may be blurred due to unusually large within-location variability.

Conclusions
Crop variety trials are probably the most well-funded agricultural research. Regardless of economic developmental levels and financial situations, crop variety trials are conducted every year in every region for every main crop, and abundant data have been accumulated for all crops and regions. Such data can be utilized in selecting genotypes and recommending cultivars as short-term goals and in understanding the target region and the test locations as long-term goals, which in turn facilitates achieving the short-term goals. The long-term goals have been hindered due to lack of methods in utilizing the abundant but highly unbalanced multi-year data. The LG-biplot methodology solves this problem. It allows utilization of existing variety trial data to identify repeatable   Fig. 2. The placement of a location is determined by the mean coordinates of all trials conducted at the locations. For example, the placement of "OTT" was determined by the placements of the three trials conducted at Ottawa in 2008, 2009, and 2010, i.e., OTT_08, OTT_09, and OTT_10. See Table 1 for full location names. The makers for the columns in Table 1 are presented as "+" for clarity.
GE patterns, to delineate mega-environments, and to understand the scope of unrepeatable GE at a location and within a mega-environment. Such information is crucial for improving plant breeding efficiency, crop cultivar deployment, and crop productivity. Use of LG biplot analysis will save tremendous time and resources in planning and conducting new experiments.
Methods the sample data. Grain yield data from the 2006 to 2010 Quebec oat trials were used in this study. This is the same dataset described and used in 2 to conduct mega-environment analysis using a GGE-GGL biplot approach. Briefly, yield data were available for 26, 29, 34, 35, and 35 oat genotypes tested at 7, 7, 8, 8, and 9 locations across Quebec plus Ottawa Ontario in 2006 to 2010, respectively. The test locations were chosen to represent the three ecological zones of Quebec, ranging from Zone-1 in the south to zone 3 in the north. Fifteen genotypes were common in all five years; more genotypes were common in adjacent years. The locations used to represent each zone varied across years (Table 1). To present this multi-year, multi-location data in a single GGE biplot, a missing value imputation method had to be employed 10 . Based on the GGE-GGL biplot, two distinct oat mega-environments were identified. One mega-environment (the southern mega-environment) consisted of all Zone-1 locations plus Ottawa and La Pocatière, the latter being a Zone-3 location, geographically; the other mega-environment (the northern mega-environment) consisted of all Zone-2 and Zone-3 locations except La Pocatière. GGE-GGL biplot analysis was possible because of the presence of a relatively large number of common cultivars between years, which allowed missing values to be imputed with some confidence. The use of this dataset in the current study was to show that the same mega-environment delineation can be achieved using the LG biplot approach, which does not require the presence of common genotypes across years.
The yield data from the 2014 to 2018 Quebec oat trials consisted of 49 trials involving 10 locations and 116 genotypes; the number of genotypes tested each year was 50, 49, 45, 45, and 44, respectively. This dataset was used to validate the results from LG biplot analysis of the 2006 to 2010 dataset and to demonstrate the importance of mega-environment delineation on cultivar selection and recommendation.  www.nature.com/scientificreports www.nature.com/scientificreports/ Data standardization. GGE biplot analysis was conducted yearly for the yield data from the 2006 to 2010 Quebec oat trials (Fig. 1); it is used here to relate yearly GGE biplot analysis to cross-year LG biplot analysis. The genotype-by-environment data of grain yield were standardized before subjecting to singular value decomposition (SVD). The standardization was conducted by: where P ij is the standardized yield of genotype i in environment j, T ij is the original yield of genotype i in environment j in the genotype-by-environment table, T j is the mean yield across genotypes in environment j, and s ij is the standard deviation in environment j. This data standardization is denoted as "Scaling = 1" and "Centering = 2" in the GGE biplots (Figs 1 and 5). (Figs 1 and 5) approximates the genotypic main effect and GE of a genotype-by-environment two-way table 11 . It is based on the first two principal components (PC) resulting from subjecting the standardized genotype-by-environment table (P ij ) to SVD. This process decomposes the table into genotype eigenvalues, environment eigenvalues, and singular values: LG1 biplot Figure 6. The LG1 biplot based on the same correlation table on which the LG biplot in Fig. 4 was generated. It differs from the LG biplot in Fig. 4 only in that the correlation table was centered by the grand-mean before subjecting to singular value decomposition, which is indicated by "Centering = 1".

GGe biplot analysis. A GGE biplot
www.nature.com/scientificreports www.nature.com/scientificreports/ where ζ i1 and ζ i2 are the eigenvalues for PC1 and PC2, respectively, for genotype i; τ 1j and τ 2j are the eigenvalues for PC1 and PC2, respectively, for environment j, and ε ij is the residual from fitting PC1 and PC2 for genotype i in environment j; λ 1 and λ 2 are the singular values for PC1 and PC2, respectively. α is the singular value partitioning (SVP) factor. When α = 1 (i.e., SVP = 1), the biplot is said to be genotype-focused, and is suitable for comparing genotypes (Fig. 5). When α = 0 (i.e., SVP = 2), the biplot is said to be environment-focused, and is suitable for visualizing correlations among environments (Fig. 1). The scalar d is chosen such that the length of the longest vector among genotypes equals to that among environments; this is important for generating a functional biplot 10  for environments in the same plot.
LG biplot analysis includes two steps. First, the yearly Pearson correlations among test locations across tested genotypes were calculated to form a location by trial table of correlations like Table 1. Second, this table was subjected to SVD and displayed in a LG biplot. The process of generating a LG biplot is the same as generating a GGE biplot (equation 2) except to define P ij as the Pearson correlation coefficient between location i and location-year combination j (Table 1) and replace "genotypes" with "locations" and "environments" with "trials" or "location-year combinations". The LG biplot was so named because both the rows and the columns of the correlation table (Table 1), and their makers in the biplot (Figs 2-4), are locations, and the purpose was to visualize the similarity/dissimilarity among locations in ranking genotypes. No scaling or centering was performed before subjecting the correlation table to SVD (denoted as "Scaling = 0" and "Centering = 0"). The LG biplot, therefore, approximates the correlation values in Table 1. Prior to SVD, any missing values in Table 1 were estimated using a method described in 11 . Note that no information is required about the tested genotypes in LG biplot analysis; they are treated as random samples in the population of genotypes.

Data Availability
All necessary data are included in the manuscript.


text:
Forage Yield Estimation with a Process-Based Simulation Model

A process-based simulation model of natural grasslands and improved pastures can be used to compare mean productivity and stability of forage productivity across years, agroecological regions, and management approaches. Model simulations can help farmers develop management practices to optimize livestock stocking rates and nutrient management for native and improved grasses on different soils with varying rainfall amounts. Likewise, forages are adapted to a wide variety of soils, rainfall zones, and latitudes. The objective of this chapter is to describe the Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) model that simulates a wide variety of environmental and management impacts on forage production, soil health, and conservation concerns, including nutrient and sediment losses. We describe the various processes simulated in the model and input data requirements. We also describe how to derive plant parameters for various forage plant species. The model has been applied to simulate forage yields across years and diverse environments in the U.S. and tested using published forage yield data from Natural Resources Conservation Service, United States Dept. of Agric. Many common native and introduced grasses or grass mixtures in the U.S. have been successfully simulated. We also describe and discuss knowledge gaps for the model that future research should address to improve this and similar simulation models.

Introduction
A process-based simulation model of natural grasslands and improved pastures offers managers a science-based decision tool with many possible applications. Such a model can be used to compare mean value of and stability of forage productivity across years, agroecological regions, and management approaches. Model simulations can help farmers develop practices to best manage livestock and to most effectively fertilize pastures for native and improved grasses on different soils with varying rainfall amounts. Likewise, how forage productivity responds to different grazing management can be simulated. The ideal model for these applications would have sufficient detail to simulate several plant species, soils, and climatic conditions without excessive input requirements. The model should be able to simulate forage legumes, improved grasses, and common native grasses, as sole crops or mixtures. The required plant parameters should be readily derived from published studies in conjunction with measurements that can be obtained without an inordinate amount of time and effort given to field experiments. This model will have process-based components to simulate leaf area growth, biomass production, and nutrient uptake. In addition, the soils and weather data needed should be readily available, and there should be data sets with sufficient detail for validating forage production simulations. Historically, there has been a diversity of process-based models developed to simulate dynamics of grass growth and dry matter yield for different species. Some examples of process-based models are GRASIM [1,2], Simulation of Production and Utilization on Rangelands (SPUR) model [3][4][5], the Ecosystem Level Model (ELM) [6], and DAFOSYM [7].
GRASIM is a grazing simulation model designed to simulate intensive rotational grazing management linked to components (e.g., carbon, nitrogen, and water budgets) of the pasture system This model predicts daily growth rate, biomass accumulation, protein and fiber content, and water and nutrient levels [1]. Simulation of Production and Utilization of Rangelands (SPUR) is a mechanistic process model designed to simulate growth initiation, germination, carbon assimilation, translocation between roots and shoots, N mineralization, and nitrogen uptake [8,9]. SPUR has been modified and incorporated into the Integrated Farm System Model, IFSM, to simulate the growth and competition of multiple plant species in pastures [10]. The Dairy Forage System Model, DAFOSYM, is a simulation model of the dairy forage system. This model simulates plant growth of lucerne (Medicago sativa), maize (Zea mays L.), small grains, and soybean (Glycine max) using historical weather data. DAFOSYM evaluates the forage qualities and accumulation of dry matter based on daily weather and soil moisture.
c. Soil water storage capacity as impacted by soil texture d. Slope and rock fragment that impact infiltration/runoff rates e. Aeration differences with some soils having prolonged flooding f. Soil nutrient variability, due to either inherent soil fertility or applied nutrients that include organic and inorganic fertilizers The complexity of these factors and their interactions make forage management a challenge. Animal stocking rates, haying frequency, and optimum fertilizer applications add to the complexity. A producer must take all of these factors into account, plus personal experiences and expert opinion to manage forage lands. However, a process-based simulation model could provide a more science-based approach to management decision making. By systematically simulating forage growth with different soils, weather, plant species, and management systems, such a model will be a valuable tool. In addition, built-in water and nutrient balances in the model allow users to derive guidelines that producers can apply to better adjust management practices among years with high, low, or normal rainfall patterns. Likewise, impacts of various management scenarios on soil erosion, soil organic matter buildup or depletion, and water quality can be evaluated.
The ALMANAC model is adaptable to any species of forage in any geographic location, provided adequate input data are available to inform the model simulation. It can be used to simulate a diversity of forage species as well as woody species, crops, and interspecies competition. The model can simulate plant growth using independently derived plant parameters, with no recalibration among sites. It includes components for the water balance, the nutrient balance, and interception of solar radiation by monocultures or competing plant species. Daily values for weather variables, including temperature and rainfall, are required. Soil inputs are readily available from published USDA-NRCS soil surveys. When fully calibrated, ALMANAC has been shown to reasonably simulate native grass productivity on diverse sites in the U.S. as well as improved grass species at several U.S. sites as discussed below. The objective of this chapter is to describe the Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) model [11], a process-based model capable of considering a broad variety of environmental and management impacts on forage production, soil health, and conservation concerns, including nutrient and sediment losses.
This chapter consists of five sections: ALMANAC model simulated processes: The basic processes ALMANAC simulates are described, including processes specific to forage simulation, to include: a. Cessation of forage plant development due to intense drought stress (Growing degree day accumulation stops temporarily) b. Midseason dormancy in cool season forages when the daylength gets sufficiently long. This model functionality enables simulation of the bimodal growth pattern observed in such grasses (e.g., tall fescue (Schedonorus arundinaceus (Schreb.) Dumort)).
c. Plant competition for water and nutrients, including woody species competing with herbaceous forages Soils and weather data: The available and required soils and weather data are described.
Deriving plant parameters for a forage species and accommodating ecotypes: The steps for deriving plant parameters for various forage species and ecotypes are outlined.
Model testing against independent data: Soft calibration of the model via comparison of outputs to independent data to ensure the model is working reasonably well is described and discussed.
Knowledge gaps and areas for future improvement as a guide for additional research: Finally, the knowledge gaps and potential areas for improvement are outlined, as a guide for potential additional research.

ALMANAC model simulated processes
Phenological development defines the duration of various plant growth stages and determines the length of the forage growing season. The ALMANAC model simulates phenological development with a growing degree day (GDD) system, species-specific base temperature, and optimum growing temperature. The sum of GDD calculates the duration of the growing season. Anthesis date is predicted with a defined fraction of the total GDD sum to physiological maturity. Daylength and drought affect simulated forage phenology as described below.
Leaf area growth is simulated on a whole canopy basis, with potential leaf area index (LAI) defined for each species/ecotype/variety. These are hereafter referred to as just "species." The climate and soils at different sites often dictate the plant density of forages, thereby affecting the potential LAI.
The development of LAI over the growing season is simulated with a 0.0 to 1.0 "S" curve defined for each species. Thus, LAI is simulated as a function of the ratio (current summed GDD)/(GDD to maturity). This ratio typically approaches 1.0 as the plants approach anthesis and transition from forage production to reproduction. The "S 00 curve thus defines the potential leaf area growth over the growing season.
Daily dry matter accumulation is simulated using a radiation use efficiency (RUE) approach. The potential dry matter produced each day is a function of the amount of photosynthetically active radiation (PAR) intercepted by the leaf canopy on that day. The RUE is a species-specific value (g of dry matter per MJ of intercepted PAR).
Partitioning among plant parts is also on a whole canopy basis. The root and shoot partitioning is defined by two parameters. Plants initially partition a greater fraction of the total dry matter production into the roots. This fraction decreases as plants approach anthesis. Stresses, especially drought, reduce the above-ground dry matter production more than the root dry matter production. This causes drought stress to change the simulated root:shoot ratio.
Partitioning of plant energies to the seed is simulated with a harvest index (HI) approach. The fraction of the total plant weight in the seed at maturity relative to the total plant weight is the species-specific HI parameter. While very small for most forage species due to their relatively small fruits and seeds, the partitioning into the seed begins after anthesis and is complete by physiological maturity.
Environmental stresses decrease leaf area expansion and dry matter accumulation. As described below, the model simulates the impacts of a variety of stresses each day. The most severe stress each day constrains leaf area growth and dry matter accumulation. Leaf area growth is more sensitive, especially to drought, than is dry matter growth.
Drought stress is simulated using the potential evapotranspiration (PET), calculated as a function of daily weather variables. The available soil water in the current rooting zone is calculated each day based on rainfall, soil infiltration, and soil water-holding capacity. If available soil water in the current rooting zone is insufficient to meet the plant's demand (based on PET and leaf area index), the model simulates a drought stress response in the plant through decreased leaf expansion rates and reduced dry matter accumulation rates.
Nutrient stresses, particularly nitrogen (N) and phosphorus (P) stresses, reduce plant growth. These nutrient stresses are simulated with a supply and demand approach. Plant N and P nutrient uptake is simulated with three input parameters that define how nutrient demand changes during the growing season. For each plant species, the optimum amount of available N and P is defined for each species early in plant development, near anthesis, and at physiological maturity. These three values are used to calculate the potential nutrient uptake from the soil each day. If the N and P in the current rooting zone are insufficient to meet demand (calculated from the optimum percentage of the nutrient and the potential daily plant dry matter growth), the model simulates nutrient stress by decreasing the species' dry matter accumulation rate and leaf expansion rate.
Temperature stress can also reduce plant growth in the model. Each plant species has a defined base temperature and an optimum temperature. When daily temperature is below the base temperature, cold temperature stress occurs. When temperatures are above the optimum, high temperature stress occurs.
Aeration stress is also simulated. When soils are saturated with water, aeration stress occurs in the model. Plants have variable sensitivity to aeration stress, as defined by the species-specific value of critical aeration factor (CAF). Plants such as eastern gamagrass (Tripsacum dactyloides L.) and rice (Oryza sativa) are less sensitive to poor aeration conditions, such as flooding, while upland grasses are more sensitive.
There are components in the model developed specifically for forage simulation. Forage development is not only dependent on GDD accumulation, but also on daylength and stresses. In order to accommodate growth dynamics typical of arid ecosystems where forage species are grown without irrigation, the model was modified so that sufficient drought stress stops GDD accumulation in the model. This is in addition to the direct effects on leaf area growth and dry matter increases as discussed above. As we began simulating plant growth in more arid environments, we had to introduce the ability to halt plant development when drought stress became sufficiently intense. Thus, we introduced a function that stops GDD accumulation (thus stopping phenological development), when the zero-to-one drought stress factor is less than 0.4.
The ALMANAC model is capable of simulating growth patterns exhibited by different types of forages. Cool season forages such as tall fescue often exhibit two intervals of active growth, with a slowdown during the hottest days of the year. Actual growth patterns of tall fescue in southwestern Missouri over 3 years [12,13] are shown in Figure 1. We incorporated a midseason dormancy function to simulate this. Thus as daylength gets sufficiently long plant growth slows and stops. The model now simulates this rapid growth in the spring, slowing and stoppage of growth near mid-season, and subsequent late summer and early fall growth. The forage-simulation functionality of ALMANAC stops plant growth and development when the maximum photoperiod of the year is reached and restarts growth when the photoperiod subsequently gets sufficiently short to trigger reinitiation of growth. This model functionality was developed based on observed tall fescue growth curves measured in Missouri [12][13][14].
Subsequently, we tested the model's simulation of tall fescue yields with USDA-NRCS reported yields for a number of sites and soils across the main regions of tall fescue pastures in the U.S. [14]. We used long-term measured weather and the appropriate soil parameters for these simulations. We compared the simulated yields to the reported yields for the lowyielding sites, the high-yielding sites, and for all the sites pooled ( Figure 2). The model with this function did an excellent job of simulating tall fescue yields on sites with differing reported yields across the main areas of tall fescue production in the U.S.
Additionally, ALMANAC simulations accommodate winter dormancy, typically observed in forage species when daylength gets sufficiently short in the fall. This capacity has been well tested on winter wheat [15], which is planted in the fall, goes dormant during the winter, then restarts growth in the spring. A parameter (DORMNT) defines this interval by defining the hours of photoperiod near the minimum for the latitude when plants are dormant. If the value is 1.0, during the winter when the photoperiod is within 1.0 hour of the minimum for the latitude, plants remain dormant.
Simulation of grazing and hay harvest is especially important when simulating forages. The model resets development (summed GDD, LAI, and height) when the simulation includes a grazing event or the forage is cut for hay. The model simulates a daily value for plant height from the fraction of GDD relative to the physiological maturity value and a species-specific plant height parameter (CHT). When forages are grazed or cut for hay, this height is reduced. If grazing or hay cutting reduces the plant height by 90%, the summed GDD for that day is reduced by 90% and the leaf area and above-ground dry matter is reduced by 90%. The plants then begin regrowth the following day.
Forage plant communities often have mixtures of species, due to the diversity typical of a native prairie, due to intercropping of legumes and grasses to better accommodate nutrient demands, or due to invasion of the forage site by undesirable herbaceous or woody plants. The ALMANAC model is capable of simulating both nitrogen fixation benefits to non-nitrogenfixing species and competition between plant species. The ALMANAC model was initially   [14].

Forage Groups 42
developed to simulate competition between crops and weeds and has been applied to communities of plants such as native range sites and woody plants competing with forages. Aspects of competition simulated in ALMANAC include competition for light, water, and nutrients.
The fraction of incoming solar radiation intercepted by the leaf canopy (FI) is: The light extinction coefficient (k) for Beer's law [16] is calculated for each harvest date as: where logn = natural log of the number, and FIPAR = fraction of IPAR.
The value of k has been determined for a number of forages in the U.S. [17][18][19][20]. Realistic simulation of LAI is critical for these equations describing light interception. This is true for both the increase of LAI during active growth and the decline as leaves senesce. The model uses an S-curve to simulate the accumulation of leaf area increase as a function of GDD.
Similarly, as described above, biomass growth is simulated with a radiation use efficiency (RUE) approach [17,21]. The RUE is calculated as the rate of increase in dry matter (g per m 2 ground area) per unit of intercepted photosynthetically active radiation (IPAR) (MJ per m 2 ground area). Regressions are fit with the treatment means of plant dry weight and summed IPAR for each sampling point. The RUE is the slope of the regression for this plant weight (g m À2 ) as a function of the summed IPAR (MJ m À2 ).
This regression is ideally based on multiple harvest dates during the active growth period of the forage. Occasionally, when only two harvest dates are usable, RUE is calculated from differences. Only data from dates showing increases in dry matter (actively growing) are included. This constrains RUE values to periods of active growth. Data from sites experiencing drought stress are avoided. Values for FIPAR are calculated on a daily basis, with values for dates between measurement dates calculated by linear interpolation.
Simulated light competition uses functions of [22], whereby the light interception of each plant species in the mixture is computed with the following formula: LAI*k (k being the light extinction coefficient). These products (LAI*k) of each species are summed and the sum used in Beer's law to compute the fraction of light interception by the whole plant community. This fraction of light intercepted for the whole plant community is then divided among the competing species by weighted fractions. The weights account for differences in species heights and LAI*k of the species. Thus, taller species and those with higher LAI values and higher k values intercept a greater fraction of the total light intercepted by the plant community.
Water and nutrient competition are simulated with a balance sheet approach. Once intercepted light for each plant species is computed as described above, the potential daily biomass growth is calculated for each species with the total daily incident solar radiation, assuming 45% of that is PAR [23,24]. The RUE for the species multiplied by the intercepted PAR is the potential biomass growth on any given day. Using the optimum nutrient concentrations for N and P at the current growth stage, the demands for N and P are calculated. If insufficient N and/or P is present in the current rooting zone, the model reduces simulate growth rates to account for N and/or P stress. This simulation of the balance of nutrients is done for each species within a mixture. The model accounts for variability in root scavenging capacities between species only through differences in the current rooting depth of each species. Potential rooting depths of various plant species are derived from measurements reported in the literature for forages grown on soils with no restrictive soil layers (such as in [17]).
Likewise, potential plant transpiration is calculated from the potential evapotranspiration and the total community LAI. If soil water in the current rooting zone is insufficient to meet the species' demand, simulated drought stress occurs and limits growth. This occurs for all plant species present. However, it should be noted that a deeper rooted plant species may have access to soil water (and nutrients) not available to any competing shallower rooted species; ALMANAC accommodates different rooting depths of species. The deeper rooted plant species may have adequate soil water and nutrients to avoid drought and nutrient stresses when a shallower rooted species is stressed. The ALMANAC model does not currently simulate hydraulic lift dynamics and the potential impacts of lift on water and nutrient redistribution.

Soils and weather data
The soils and weather data described below are specific to the U.S. These are described in more detail, including how to download them at: https://www.ars.usda.gov/plains-area/temple-tx/grassland-soil-and-water-research-laboratory/docs/193226/. The soil and weather data for the country of Mexico have also been developed and formatted for ALMANAC model simulations (https://www.ars.usda.gov/plains-area/temple-tx/grassland-soil-and-water-research-laboratory/ docs/almanacmex/). As the model is applied outside of these two countries, the input data for soils and weather (as well as plant species growth curves) can be developed through cooperation with the senior author of this project.
The general philosophy of input data development is to make this model and other USDA-ARS models (including EPIC [25], APEX [26,27], and SWAT [28][29][30]) readily and easily applied. Input data are constrained by what is readily available and easily accessible. This means the daily weather inputs required consist of maximum and minimum temperature, rainfall amounts (and snowfall amounts), and solar radiation. When unavailable for a given location, solar radiation can be derived; wind speed and relative humidity can be used to approximate solar radiation. Weather data from the U.S. National Oceanic and Atmospheric Administration (NOAA) websites are readily downloaded for any state in the U.S. via the steps outlined in the model documentation (see website link above).
Similarly, required soil data are available through USDA-NRCS (https://www.nrcs.usda.gov/ wps/portal/nrcs/main/soils/survey/), which has the most extensive and verified, publiclyavailable soil database for the U.S. The soil data are readily downloaded for any state in the U.S., with the steps outlined in the ALMANAC model documentation. The most critical components of the soil data inputs are the depth, texture, and amount of rocks by soil layer.

Forage Groups
For each soil layer, the values for saturation, drained upper limit, and lower limit are used by the model. Soil organic matter is another input that impacts plant-available water and soil carbon balances in the model. The amount of runoff from rainfall events is calculated with the traditional runoff curve number system. The runoff is simulated with the slope and type of ground cover.

Deriving plant parameters for a forage species and accommodating ecotypes 4.1. Field plant species measurements
The group of readily derived plant parameters includes the potential leaf area index (LAI), the development curve for LAI over the growing season, the light extinction coefficient for Beer's law (k), the radiation use efficiency (RUE), the duration of the season in degree days, the harvest index for seeds (HI), and the N and P concentrations for each species over the growing season. All of these should be derived from measurements of a plant stand grown in a relatively stress-free environment to establish potential values for these for each forage species and ecotype. This means that ideally species being measured in field conditions should not have stresses due to drought or nutrient deficiency.
Field-derived values for the critical species-specific parameters have been described previously [17][18][19][20][21]. The model simulates light interception by the leaf canopy with Beer's law [16]  c. When taking light measurements under the canopy using the ceptometer, take at least six evenly spaced measurements in each quadrat near ground level. Record the average. Always take care to avoid biasing the sample in favor of more plants or more bare ground.
d. Finally, harvest plants, removing all plant material in the quadrat directly above the site where light was measured and place in labeled bag.
Repeat these steps at least three more times for a targeted plant species. For each set of measurements, make sure to measure plants on the same soil or ecological site. When returning to the general area for future measurements, select the same species to measure but not the exact same plant/plot area as previously measured.
Process plant material as soon as possible after sampling to avoid desiccation effects on leaf area.
a. When weighing the entire sample from field, if the entire sample is greater than 100 g, take a representative subsample. This is between 10 and 30% of the entire sample but no less than 100 g. Weigh and record the subsample weight. Make sure to select a subsample with the same proportion of green leaves, dead material, stems, and reproductive structure as the entire sample.
b. Use a belt-driven leaf area meter (or something similar) to measure leaf area of the subsample. Separate the subsample into dead material (anything completely brown), stems, leaves, and reproductive structures. Record the weight of the dead material, stems, and reproductive structures.
c. Determine the area of each structure using the leaf area meter. Run the dead material, stems, leaves, and reproductive structures through separately and record the area of each. Place the entire sample into a paper bag. These samples are dried in a 66 C forced air oven for 3 days or until weight stabilizes. Then record the dry weight of the entire sample.
d. Finally, grind dry sample to prepare for nutrient analysis. Total annual production of forages reported by NRCS are derived from end-of-season sampling on sites with closed canopy stands of the species of interest over 3 years or more. The NRCS procedure involves measuring dry matter biomass production above a 5-cm cutting height in at least 10 randomly selected plots at each field site. The specific soils for a location of interest can be downloaded as described above. Mean simulated forage yield over 10 years of real weather data can be compared to the reported annual production (from USDA-NRCS Web Soil Survey) for a site. The NRCS value of Animal Unit Month (AUM) is converted to Mg ha À1 (0% moisture) with a conversion factor assuming 700 lbs. (318 kg) of air-dried biomass (90% moisture) per AUM. Values for key plant parameters for the plant species of interest are derived from the field measurements described above.

Examples of testing ALMANAC's simulation of forage yields
We have several published examples of testing ALMANAC's simulation of forage yields. The first was for several Texas range sites with native warm-season grasses [32,33]. Next, we simulated old world bluestems (Bothriochloa Kuntze, Capillipedium Stapf, and Dichanthium Willemet) and buffelgrass (Pennisetum ciliare (L.) Link) in Oklahoma, Texas, and Mexico [18].
To evaluate the ability of the model to simulate introduced or improved grasses, we tested coastal bermudagrass (Cynodon dactylon (L.) Pers.) and bahiagrass (Paspalum notatum Flügge var. saurae Parodi) at several sites in Texas [19]. Western grasses in low-rainfall sites in Montana were simulated using parameters derived for some common native grasses there [20]. The cool-season forage "tall fescue" was simulated at several sites in several states where this grass is commonly grown [14]. In addition, creosote bush (Larrea tridentata [DC.] Cov.) parameters were derived the and model testing for its ability to describe competition of this woody species with forages in arid sites in western Texas [34].
Overall, the ALMANAC model predicted forage yields with reasonable accuracy, and hence when fully calibrated, the model can be used as an effective management tool to evaluate management practices that maximize forage yields, optimize inputs, and minimize negative environmental outcomes.

Knowledge gaps and areas for future improvement as a guide for additional research
The ALMANAC model uses the best plant growth modeling functions currently developed. Often, knowledge gaps force model developers to use placeholder functions with the hope that future research will enable development of improved, more realistic functions. Some areas for beneficial future research include nutrient and carbohydrate cycling, forage regrowth following haying, nutrient response functions, and legacy effects.
The simulated cycling of nutrients in the soil and between the roots to the shoots for perennials needs to be critically investigated for this model. As forages mature and leaves senesce during the fall and winter, often nutrients and carbohydrates are translocated back into the root system, to be used for regrowth the following spring. Grazing may also trigger plants to allocate more carbohydrate storage in roots to survive grazing pressures. Functions describing these processes need to be better developed and incorporated into the ALMANAC model in the future.
Likewise, the regrowth of forages following hay cutting or grazing within the growing season, needs to be more extensively tested. The functions currently in ALMANAC appear to function reasonably. However, as more extensive data are available for testing the model, improvements likely will be made.
The response of forages to applied nutrients often is highly dependent on what is already in the soil. This includes nutrients readily available and those coming from transformations within the soil during the growing season. Very often publications report a nutrient response of a forage without adequately describing initial soil conditions. If adequate nutrients are already present in the soil, the response of the forage to applied nutrients can be much dampened. Likewise, if the soil is initially very nutrient poor, the forage may show a large response to applied nutrients. An extensive testing of the model with data having good values for initial soil nutrients will be valuable.
Finally, legacy effects due to previous years' weather conditions and previous years' nutrient cycling need to be investigated. This has been studied with switchgrass [35], but needs more extensive studies with diverse representative forages.

Conclusions
In this chapter, we described the ALMANAC model, including the process simulated, how to derive plant parameters for additional forage species, and how to validate using measured field data. Because of its accurate simulation of plant production, the water balance, and the nutrient balance, the model is capable of simulating a wide variety of environmental and management impacts on forage production, soil health, and conservation concerns, including nutrient and sediment losses. The model will be a useful and valuable tool for forage management in pastures and rangelands in a wide range of conditions.


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Sound Absorption Performance of Sugar Palm Trunk Fibers

. The purpose of this research is to investigate the effect of bulk density, thickness, and air gap to sound absorption performance on absorber based sugar palm trunk fibers. The fibers were obtained from solid waste on Small-Medium Enterprises of sago flour processing in Klaten, Central Java, Indonesia. The absorber specimens were formed from the fibers using a simple press molding in an oven at 150 °C. According to ISO 10534-2, the absorber samples were tested using two microphones impedance tube with random noise source to get the curve of the sound absorption coefficient. The result shows that the absorption performance can be improved by increasing bulk density and increasing of sample thickness. Especially at low frequencies, improvement of the sound absorption coefficient can be achieved (NAC > 0.8) by applying the air gap behind the sample. The best performance of absorber based sugar palm trunk fiber can be achieved for (1 000 to 6 000) Hz range frequency.

Sound absorbers from natural materials
Noise pollution causes adverse effects on human health, i.e. bad emotions, sleep disturbances which cause daytime drowsiness, increase the risk of high blood pressure, cardiovascular disease and deafness which may be experienced by people who work in noisy environments [1]. Therefore, to meet human health needs, environmentally friendly acoustic materials are required to solve air pollution problems. In general, the sound absorber is grouped into three types, i.e. porous absorber, membrane absorber, and volume absorber [2]. While porous absorber can be categorized into three types, i.e. cellular, fibrous, and granular [3]. Fibrous is one type of absorber whose widely used. Fibrous type absorber is used in many areas including architecture, automotive industry and audio equipment [4]. The reasons for the use of fiber material are an economic factor and excellent sound absorption performance. Examples of fiber-based absorbers including natural material groups such as cotton (Gossypium arboretum L.) [5,6], sugarcane (Saccharum officinarum L.) [7], wood [8], tea-leaf-fiber (Camellia sinensis L.) [9], rice straw (Oryza sativa L.) [10,11], kapok (Ceiba pentandra L.) [4]; synthetic material group such as glass woll [12], polyacrylonitrile [13] and polyester [14]. Synthetic fibers have negative impacts including production processes which require large amounts of energy, causing high global warming potential [15,16] and air pollution which irritates on eyes and skin, also cause pulmonary disease [17]. Therefore, research on natural materials is interesting research to seek alternative "green" and sustainable acoustic absorber.

Previous studies on natural fibers
Lim et al. [18] studied the absorption performance of kenaf fiber (Hibiscus cannabinus L.) and they found that the absorption coefficient of the fibers increased along with an increase of bulk density and thickness. For the thickness of 75 mm, the absorption coefficient reached a maximum value of 1.0 at 1 100 Hz. Putra et al. [11] studied the acoustic performance of biomass made from rice straw and absorption coefficient reaches an average value of 0.8 above 2.5 kHz. An increase in the absorption coefficient can be achieved by adding a polyester fabric to the surface. Or et al. [19] proposed noise control absorber made from oil palm (Elaeis guineensis Jacq.) empty fruit bunch fibers. The optimum absorption coefficient with values above 0.9 ranging from (1 000 to 1 500) Hz is achieved by 50 mm thick sample with a bulk density of 292.3 kg m -3 . Xiang et al. [4] investigated sound absorption performance in kapok fibers. This study shows that kapok fibers have acoustic performance similar to glass woll. Moreover, kapok absorber produces a better performance at lower bulk density. Putra et al. [7] studied sugarcane waste fibers and found that sugarcane absorber with a thickness of 0.5 inches has equivalent performance to commercial synthetic fibers with the absorption of 0.65 at (1.2 to 4.5) kHz. Putra et al. [20] proposed noise control absorber made from extracted PALF (pineapple-leaf fibers (Ananas comosus (L.) Merr.). PALF has an average sound absorption coefficient 0.9 at above 2 000 Hz at 20 mm thickness, while the sample with 30 mm thick has an average absorption coefficient of 0.8 at above 1 000 Hz.
This paper discusses the potential of sugar palm (Arenga pinnata (Wurmb.) Merr.) trunk (SPT) fiber as a sound absorber based on natural, green and sustainable material. Conventionally, SPT fiber was used as food for worms [21] and mushroom growing media [22]. Until recently, early development of research has been done on palm sugar trunk fiber as raw material in glucose production [22], reinforcement in composite production [23,24], and a raw material of briquette production [25]. As one of the waste in the flour processing industry with abundant quantity, SPT fiber also causes environmental problem [26]. Considering the potential of the SPT fiber, this paper contributes to the enrichment of acoustic materials and the opportunity to use SPT fiber as a commercial absorber and provide the resolution of solid waste in sago flour processing industries.

Materials and experimental setup 2.1 Sample Fabrication
The sugar palm trunk (SPT) fibers were obtained from Small-Medium Enterprises (SMEs) of sago flour processing in Klaten, Central Java, Indonesia. The SPT was processed using shredding machine to produced palm starch traditionally and it left solid waste containing fibers. On the SMEs area, the solid waste accumulates in many places and causes unhealthsmelling pollution. In the other hand, the fiber content in solid waste is quite high, which is around 50 %. These fibers were separated manually from the other materials. Then, the fibers were dried naturally and finally they were dried in an oven at 80 °C for 30 min to maximize the drying process. The fibers can be classified as hard fibers as shown in Figure 1.
Samples were made according to the standard size of the impedance tube measurement test. Fibers were weighed based on bulk density calculation and desired thickness. Then, the fibers were put into an aluminum mold with a diameter of 30 mm. The forming process of samples was carried out in an oven under the temperature of 150 C for 10 min. Bulk density was obtained from fibers mass divided by sample volume. Table 1 shows the variation of sample mass, bulk density, and thickness.

Experimental setup
The measurement of sound absorption coefficient was done according to ISO 10534-2 test standard [26]. The configuration of the sample on the tube as shown in Figure 2. The sample was located at the end of the tube while at the other end mounted two microphones to record the sound. Before the microphones measurement was used, they had to be calibrated first using calibrator CA115 calibrator. The sound was produced by a sound generator. The analyzer was used to process the recorded signal to get the transfer function between two microphones. Then the software BSWA VA-Lab4 basic was used to calculate the sound absorption coefficient.
The measurements were operated in the frequency range of (1 000 to 6 000) Hz. In this research, the frequency of (1 000 to 2 000) Hz was referred to as mid frequency, while the frequency of (2 000 to 6 000) Hz was categorized as high frequency. This experiment varied on bulk density, thickness, and application of air gap behind the sample. All measurements were performed three times in each sample. The result of sound absorption coefficient measurement on different bulk density at a fixed 30 mm thickness is shown in Figure 3. At the same thickness, it shows that higher bulk density, higher sound absorption coefficient. The improvement is obvious from mid to high-frequency range. An increase in bulk density causes an increase in flow resistivity and tortuosity [20]. The high flow resistivity enhances the difficulty level of sound waves through fibers, so more energy will be lost due to friction between sound waves and fibers. While high tortuosity shows the level of complexity of fibers configuration in the sample. It causes more trapped sound waves and more absorbed sound waves.

Effect of thickness
The result of the sound absorption coefficient measurement varied in thickness is shown in Figure 4. In this result, samples were made with different thicknesses while bulk density was maintained at 141 414 kg m -3 (bulk density with the best sound absorption coefficient from previous experiments). For the same bulk density, the measurement result shows that the sound absorption coefficient at lower frequencies increases along with an increase of sample thickness. The samples with 30 mm thick show a good sound absorption coefficient (> 0.8) and it was achieved at frequencies above 2 000 Hz. This is a special characteristic of porous absorber which has good sound absorption at high frequencies. The increasing sample thickness causes an improvement in sound absorption coefficient below 1 250 Hz.

Effect of air gap
This research also investigated the effect of adding an air gap applied behind the sample. The configuration of the sample in the impedance tube is illustrated in Figure 5. The sound absorption coefficient was measured using four different air gap configurations, i.e., d = 0 mm, 10 mm, 20 mm, and 30 mm. The application of an air gap is intended to shift the peak toward a lower frequency. However, the sound absorption coefficient above the peak frequency has decreased slightly due to the formation of peaks and dips caused by standing waves in the air gap behind the sample [18,27]. The decline phenomenon can be seen in Figure 6.
In order to obtain good sound absorption coefficients at lower frequencies, the application of air gap is more efficient than using full-thickness absorbers. Application of the air gap can reduce the use of fiber so that the production costs can be reduced. As shown in Figure 7. sample with a thickness of 30 mm added with 10 mm air gap thickness indicates a sound absorption capability similar to a full thickness sample of 40 mm in the (1 000 to 2 500) Hz frequency range.

Conclusion
Sound absorption performance has been investigated in this experiment. From the experiment, samples with bulk density 141 414 kg m -3 have the optimum sound absorption coefficient on 30 mm thick sample with a value above 0.7 at a frequency above 1 600 Hz. This research also showed that samples with 50 mm thickness had the optimum sound absorption performance compared to samples with lower thickness with values above 0.8 at a frequency of 1 000 Hz and above. Furthermore, the sound absorption coefficient reaches 0.9 at (1 200 to 2 000) Hz range of frequency. Good sound absorbing performance at lower frequencies can also be achieved by applying the gap behind the sample. The use of air gap allows fiber usage reduction compared to full thickness samples in the same total space.