Agronomic performance of pigeonpea germplasm evaluated under field conditions at Chitedze (Malawi, 14oS).
\r\n\tNotably, the book encourages academic scholars and researchers to contribute to the modern concepts of CSR. Fundamentally, it speaks for well-developed literature for entrepreneurs and managers, thus assisting them in the decision-making process.
\r\n\tFurthermore, this book is of great value to policymakers, practitioners, and corporations, thus contributing to various disciplines (e.g., social science and management).
\r\n\tThese proposed themes encourage future researchers and professionals to share their ideas, concepts and work concerning these subject domains. All these suggested topics had recommended under the rubrics of CSR. Perhaps, all the professionals, researchers, and scholars are welcome to submit their piece of work, in particular to the suggested topics.
\r\n\tIndeed, the recommended topics include the following but are not limited to these only.
\r\n\t• Corporate Governance and Sustainability
\r\n\t• Green Innovation and CSR
\r\n\t• Social Entrepreneurship
\r\n\t• Green Economy and Social and Environmental Sustainability
\r\n\t• Sustainable Development and Industrialization
Pigeonpea [
The crop is grown for its multiple benefits mainly by smallholder growers and is useful in providing household food security in the region. The crop provides highly nutritious food for human consumption (Amarteifio et al., 2002) and fixes considerable amounts of atmospheric nitrogen (Mapfumo et al., 1999), thus improving soil fertility. Considerable quantities of the grain are traded within the region and in international markets particularly in the Indian sub-continent thus generating income for farmers. In addition, the stover is used for fuelwood and building material in some of the rural communities in the region (Silim et al., 2005). Pigeonpea is also useful for controlling soil erosion in those areas prone to floods. The crop is also relatively tolerant to drought (Kumar et al., 2011) thus making it suitable for cultivation in the semi-arid agro-ecological conditions prevalent in the region.
However, the average grain yield obtained by farmers in the region is generally low. In Tanzania, growers obtained 0.4 t/ha (Mligo and Myaka, 1994). This is partly because some of the smallholder pigeonpea growers cultivate largely unimproved landraces in mixed cropping systems (Fig. 1) partly because of the pressure of limited land for cultivating crops as well as the need to minimize the risk of crop failure. In addition, typical smallholder farmers are subsistent. Therefore, the broad objective of the study reported in this Chapter was to evaluate improved pigeonpea germplasm for agronomic performance across the region under rain-fed field conditions. The specific objectives were to evaluate the germplasm for (i) sensitivity to photoperiod (ii) reaction to fusarium wilt (iii) reaction to insect pests and (iv) grain quality traits that are preferred by end-users.
A mixed cropping system consisting of pigeonpea (foreground), corn, sorghum and cowpea.
A significant proportion of the smallholder farmers in the region largely grows traditional landraces. The landraces are characterized by late maturity, inherently low grain yield and dark seeds. However, the landraces are adapted to the local biotic and abiotic stresses. In particular, they are tolerant to severe droughts that occur in the region. On the other hand, the improved cultivars fall into either short-duration (SD) or medium-duration (MD) or long-duration (LD) types. This classification is based on the duration to maturity.
The short-duration types require about 90 days in order to mature. Therefore, they mature in the middle of the rainy season (in the region) when post-harvest handling is difficult. This renders the grain susceptible to spoilage by fungal diseases in particular. Consequently, this type of pigeonpea is poorly preferred by farmers in the region. On the other hand, medium duration (MD) types require about 150 days in order to attain maturity while long-duration (LD) types can require up to 240 days to mature fully. The majority of the landraces in the region fall into this category. In general, late maturity in pigeonpea is attributed to sensitivity to day length (or photoperiod).
The production of pigeonpea in southern Africa is constrained by a range of abiotic and biotic factors. In particular, the crop is sensitive to photoperiod. The crop is also threatened by fusarium wilt (Gwata et al., 2006; Kannaiyan
When the crop is grown in high latitude areas (>10o away from the equator), it is sensitive to photoperiod and temperature (Silim et al., 2006) with plant height, vegetative biomass, phenology and grain yield being affected most (Whiteman et al., 1985). Consequently, the delayed flowering and maturity lead to increased susceptibility to terminal drought that frequently occurs in southern Africa. Therefore, the cultivation of the late maturing LD types in the region pauses many challenges for the smallholder farmers. For instance, the winter season (which commences in June in the region) is associated with frost and generally low temperatures, to which pigeonpea is susceptible. Furthermore, after harvesting the main crops (during May), the small-holder farmers traditionally release their domestic livestock to graze freely (or unattended) in the fields. Such livestock interfere with late maturing crops that may still be growing in the fields. In addition, the delay in crop maturity may interfere with the timing of the succeeding crop. Therefore, this makes it difficult for farmers to develop consistent crop management practices and predictable cropping systems. In terms of marketing, the pigeonpea grain from the region is exported mainly to international markets in the Indian sub-continent where the prices are attractive before the glut in November. Therefore, the pigeonpea growers in southern Africa require pigeonpea types that can flower and mature early in order to have ample time for processing the grain for export to these distant markets when demand is at a peak.
Apart from sensitivity to photoperiod, pigeonpea is threatened by the fusarium wilt disease caused by the fungal pathogen
Pigeonpea is susceptible to a wide range of insect pests that attack the crop at both the vegetative and reproductive stages (Minja et al., 1999). Among the pests, the pod borer is regarded as a major threat to pigeonpea because of its destructiveness and extensive host range while pod sucking bugs and thrips can cause up to 78% (Dialoke et al., 2010) and 47% (Rotimi and Iloba 2008) yield loss respectively.
Currently, the production area of this pigeonpea is expanding to non-traditional areas such as the semi-arid belt of the Limpopo River Basin (LRB) in southern Africa. However, the occurrence of insect species of economic importance in pigeonpea has not been investigated in the LRB.
In southern Africa, pigeonpea is consumed mainly as whole fresh green peas. Usually, these fresh beans are boiled after or before shelling. In general, end-users in the region prefer large (100-grain weight = 15.0 ± 2.0 g). Where the dry peas are utilized for human consumption, the end-users prefer the large white (cream) bold types that are easy to cook. In contrast, landraces originating from central Africa possess small hard seeds which have no commercial value in the regional markets since end-users prefer large-seeded types that are easier to cook. Favourable end-use qualities also influence cultivar adoption by growers. Therefore, grain color and size measurements were evaluated as integral components of the field studies reported in this Chapter.
Pigeonpea germplasm was evaluated under rain-fed field conditions across the region. The specific genotypes were selected on the basis of preliminary information obtained from previous large-scale screening of many pigeonpea genotypes in the field (Mogashoa and Gwata 2009), seed availability as well as local farmer-preferences in the area represented by each testing location.
The major objective of this study was to evaluate pigeonpea germplasm that was developed previously for production in high latitude areas (>10o away from the equator) for adaptation as measured by the agronomic performance. The evaluation was conducted under rain-fed conditions initially at Chitedze (Malawi; 14o S) and subsequently at Thohoyandou (South Africa, 22 oS) testing locations.
Six elite genotypes and two check cultivars were used in the study conducted at Chitedze. The experiment was arranged as a randomized complete block design replicated three times. At the beginning of the cropping season (in early December), seed of each genotype was sown manually in field plots, each measuring 5.0 m in length and containing five rows spaced at 1.2 m apart with 0.5 m between plants in the row. Standard agronomic management recommendations for pigeonpea were followed throughout the season. In each season, no chemical fertilizers were applied on the crop in consistency with other similar studies (Silim et al., 2006). In particular, inorganic N fertilizer was deemed unnecessary for the crop since pigeonpea can symbiotically fix about 40-160 kg/ha of N per season (Mapfumo et al., 1999). Pigeonpea is also able to access forms of phosphorus that are normally poorly available in the soil (Ae et al., 1990). This is achieved through the presence of piscidic acid exudates that solubilize phosphorus in the rhizosphere (Ae et al., 1990).
During the evaluation, four key indicators for agronomic performance namely the number of days to 50% flowering (50% DF), the number of days to 75% physiological maturity (75% DM), grain size as measured by 100-grain weight (100-GW) and grain yield were measured (Table 1). Statistical analysis of data sets using statistical analysis system (SAS) procedures (SAS Institute, 1989) was applied. Tukey’s method (Ott, 1988) was applied to separate the trait means obtained for each respective set of the five genotypes.
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t||||
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t|||
01144/13 | \n\t\t\t86 b | \n\t\t\t115 c | \n\t\t\tWhite | \n\t\t\t13.7 a | \n\t\t\t2.7 a | \n\t\t
01160/15 | \n\t\t\t112 a | \n\t\t\t167 a | \n\t\t\tWhite | \n\t\t\t14.4 a | \n\t\t\t2.4 a | \n\t\t
01480/32 | \n\t\t\t102 ab | \n\t\t\t166 a | \n\t\t\tWhite | \n\t\t\t13.8 a | \n\t\t\t3.0 a | \n\t\t
01162/21 | \n\t\t\t102 ab | \n\t\t\t163 ab | \n\t\t\tWhite | \n\t\t\t14.9 a | \n\t\t\t2.6 a | \n\t\t
01167/11 | \n\t\t\t96 ab | \n\t\t\t166 a | \n\t\t\tWhite | \n\t\t\t15.5 a | \n\t\t\t2.2 a | \n\t\t
01514/15 | \n\t\t\t84 b | \n\t\t\t153 bc | \n\t\t\tWhite | \n\t\t\t14.4 a | \n\t\t\t2.9 a | \n\t\t
Mean | \n\t\t\t97 | \n\t\t\t161 | \n\t\t\t- | \n\t\t\t14.0 | \n\t\t\t2.7 | \n\t\t
Royes* | \n\t\t\t83 b | \n\t\t\t173 a | \n\t\t\tWhite | \n\t\t\t13.7 a | \n\t\t\t1.0 b | \n\t\t
MtawaJuni** | \n\t\t\t119 a | \n\t\t\t172 a | \n\t\t\tBrown | \n\t\t\t16.8 a | \n\t\t\t1.1 b | \n\t\t
Agronomic performance of pigeonpea germplasm evaluated under field conditions at Chitedze (Malawi, 14oS).
Means in the same column followed by the same letter are not significantly different at the 0.05 probability level by Tukey\'s test. *Commercial cultivar in Malawi; **Unimproved traditional landrace popular in Malawi.
The newly developed germplasm showed considerable improvement in terms of duration to flowering and maturity as well as yield potential. For instance, in order to attain 50% flowering, cultivar ‘01144/13’ and the unimproved local landrace MtawaJuni required 86 d and 119 d respectively (Table 1). Cultivar ‘01514/15’ matured significantly (
The germplasm was subsequently introduced to non-traditional areas in the semi-arid LRB (Fig. 2) as represented by the testing location at Thohoyandou (Limpopo, South Africa) which is a typical ecotope representing the agro-ecological conditions in the region (Mzezewa et al., 2010). The soils at the location are predominantly deep (>150 cm), red and well drained clays with an apedal structure. The clay content is generally high (60 % ) and soil reaction is acidic (pH 5.0).
The daily temperatures at the location vary from about 25°C to 40oC in summer and between approximately 12°C and 26oC in winter. Rainfall is highly seasonal with 95% occurring between October and March, often with a mid-season dry spell during critical periods of crop growth (FAO, 2009). Mid-season drought often leads to crop failure and low yields (Beukes et al., 1999). This spatial and temporal variability in annual rainfall experienced in the area imposes several major challenges for smallholder farmers mainly because their crop choices must take into consideration the challenges imposed by moisture stress during the cropping season. In this regard, the use of drought tolerant crops such as pigeonpea as a means for achieving sustainable crop production in the area is merited.
Nineteen exotic MD genotypes of pigeonpea (obtained from the International Crops Research Institute for the Semi-Arid Tropics, Nairobi, Kenya) as well as one unimproved landrace obtained from Limpopo [designated Limpopo Local (LL)] were used in the study. The evaluation was conducted over two cropping seasons (2008/2009 and 2009/2010) at Thohoyandou (596 m a.s.l.; 22º 58\'S, 30º 26\'E) in Limpopo Province (South Africa) following the method described above (Section 4.1.1). In each season, the experiment was laid out as a 5x4 lattice design replicated three times. At physiological maturity, yield attributes and the grain yield were measured.
The results showed that at least five cultivars produced >1.5 t/ha with cultivar ‘01508/10’ obtaining the highest (2.36 t/ha) grain yield at this testing location. Apart from the local check cultivar, 30% of the cultivars evaluated in the field trial produced <0.5 t/ha indicating that they were low yielding under the agro-ecological conditions at Thohoyandou (Limpopo). However, the average grain yield obtained from the trial was about 1.01 t/ha. The average grain yield among the best five performing cultivars (1.98 t/ha) represented at least 70.0% more productivity relative to the trial mean. In comparison with the local check, cultivar ‘01508/10’ produced almost ten-fold more grain yield indicating the potential increase in pigeonpea productivity in the area. In contrast, cultivar ‘01480/32’ produced a low (< 0.6 t/ha) grain yield compared to 3.0 t/ha that was observed for this cultivar at Chitedze. This was expected since grain yield is a quantitative trait that is influenced by the environment. Subsequently, partly because of their high yield potential, appropriate time to maturity and good grain attributes, these cultivars were adopted widely by growers in the region.
The Limpopo River Basin in southern Africa.
The objective of this study was to evaluate selected pigeonpea germplasm under high disease pressure across the southern Africa region in order to identify resistant cultivars that produce optimum yields under high disease pressure.
Selected local and exotic pigeonpea genotypes were evaluated in three countries in eastern and southern Africa (Table 2). The genotypes used at each testing location were selected on the basis of preliminary information obtained from previous large-scale screening of many pigeonpea genotypes in the field, seed availability as well as local farmer-preferences in the area. The evaluation of the germplasm was conducted in wilt-sick plots (Bayaa et al., 1997) at testing locations where the disease pressure was considered to be high. At the beginning of the cropping season, seed of each genotype was sown in field plots using the same method as described above (see section 4.1.1).
At physiological maturity, the percent incidence of fusarium wilt (% FW) was determined. Initially, individual plants in each plot were scored for wilting (as a symptom of
Both the highest (92.0) and lowest (1.7) % FW scores were observed at Ngabu (Malawi) for ‘00068’ and ‘00020’, respectively (Table 2). The disease incidence in the local cultivar (Royes) was 90.2% compared with <5.0% for cultivar ‘00040’. However, the disease incidence in ‘00040’ was consistently low (<20.0%) at all three locations. Grain yield was influenced by the location. Nevertheless, at least 1.5 t/ha of grain yield was obtained for ‘00040’ compared with <1.0 t/ha for the susceptible genotype (Table 2). At Ilonga testing location, the unimproved traditional landrace (Ex-Loguba-1) attained a low grain yield (1.3 t/ha) as well as size (100-GW = 10.1 g) but the elite genotypes (‘00020’ and ‘00040’) averaged 2.7 t/ha.
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t||
00040 | \n\t\t\tImproved, resistant | \n\t\t\t12.4 | \n\t\t\t2.2 c | \n\t\t\t1.9 b | \n\t\t\t3.0 b | \n\t\t
00020 | \n\t\t\tImproved, moderately resistant | \n\t\t\t13.9 | \n\t\t\t1.6 b | \n\t\t\t1.7 b | \n\t\t\t2.4 b | \n\t\t
00068 (Check) | \n\t\t\tImproved, susceptible | \n\t\t\t90.1 | \n\t\t\t0.6 a | \n\t\t\t0.1 a | \n\t\t\t0.1 a | \n\t\t
Royes (Check) | \n\t\t\tImproved, susceptible | \n\t\t\t- | \n\t\t\t- | \n\t\t\t0.6 a | \n\t\t\t- | \n\t\t
Ex-Loguba-1 (Check) | \n\t\t\tUnimproved, landrace | \n\t\t\t- | \n\t\t\t- | \n\t\t\t- | \n\t\t\t1.3 b | \n\t\t
Performance of improved pigeonpea cultivars under high disease pressure in eastern and southern Africa.
Means in the same column followed by the same letter are not significantly different at the 0.05 probability level by Tukey\'s test.
The results of this study indicated a high level of wilt resistance in the elite pigeonpea germplasm particularly ‘00040’. The classification of this cultivar as resistant to fusarium wilt was consistent with approaches used in classifying resistance to the disease in other leguminous species. For instance, genotypes showing <10% (Halila and Strange, 1997) and <20% (Bayaa et al., 1997) incidence of fusarium wilt were considered resistant in chickpea (
The objective of this study was to identify insect species that occur in pigeonpea and damage the crop particularly at the reproductive growth stage in the LRB. This information would be useful in designing more detailed investigations on the economic impact of such species on the crop. Twenty exotic pigeonpea genotypes were planted in a field experiment at Thohoyandou following the procedure described above (see section 4.1.1). In order to achieve optimum natural infestation, no pesticides or control measures were applied on the crop throughout the season.
Starting at the flower initiation stage through to 50% physiological maturity of the crop, random samples of above-ground insects were collected using the active insect sampling approach that utilizes a combination of scouting and the sweep net technique which has been used successfully in a variety of crops including soybean and cotton (Marston et al., 1976; Spurgeon and Cooper, 2011). Sampling was performed weekly between 14.00 - 15.00 h when insect activity was at its peak. Sampled insects were collected into a specimen jar (15.0 cm in diameter x 25.0 cm) containing ethyl acetate. Individual specimens were identified to the species level in the laboratory using a pigeonpea pest identification handbook (Dialoke et al., 2010; Night and Ogenga-Latigo, 1994). Identification to the species level is necessary since there is a possibility that species within a family and even genera can exhibit differential host plants and natural enemy complexes.
The study showed that a broad range of insect species injurious to pigeonpea was collected from the crop and identified (Table 3). The species included those that are known to be pests in pigeonpea such as blister beetles, pod suckers, flower thrips and the pod borer which is regarded as a serious threat to pigeonpea because of its destructiveness and extensive host range. The range of insects found in the study was in agreement with that reported from the surveys conducted in other parts of Africa confirming that a fairly common spectrum of insect pests occurs in the crop across the African sub-continent (Dialoke et al., 2010; Minja et al., 1999). In addition, most of the insect species identified in this study have been classified as pests of pigeonpea elsewhere (Minja, 2001; Shanower
1. Coleoptera | \n\t\t\tMeloidae | \n\t\t\t(i) | \n\t\t
(i) | \n\t\t||
2. Diptera | \n\t\t\tAgromyzidae | \n\t\t\t(i) | \n\t\t
3. Heteroptera | \n\t\t\tCoreidae | \n\t\t\t(i) | \n\t\t
(ii) | \n\t\t||
(iii) | \n\t\t||
(iv) | \n\t\t||
Alydidae | \n\t\t\t(i) | \n\t\t|
Pentatomidae | \n\t\t\t(i) | \n\t\t|
4. Homoptera | \n\t\t\tAphididae | \n\t\t\t(i) | \n\t\t
Cicadellidae | \n\t\t\t(ii) | \n\t\t|
5. Hymenoptera | \n\t\t\tMegachilidae | \n\t\t\t(i) | \n\t\t
6. Lepidoptera | \n\t\t\tNoctuidae | \n\t\t\t(i) | \n\t\t
7. Orthoptera | \n\t\t\tPyrgomorphidae | \n\t\t\t(i) | \n\t\t
8. Thysanoptera | \n\t\t\tThripidae | \n\t\t\t(i) | \n\t\t
Range of insect species occurring in the pigeonpea crop grown at Thohoyandou in the Limpopo River Basin during the 2008/09 season.
These results suggested that effective insect pest control measures would be necessary for the crop in LRB since to date, there are no improved commercial cultivars of pigeonpea that posses complete resistance to the common insect pests of economic importance. Therefore, it is necessary to establish the pest status of the various insect species in order to apply effective management strategies that utilize a wide array of natural parasites and predators such as carabids, coccinellids, anthocorids and vespids. Botanical methods (using tephrosia or neem) have been suggested but the use of broad-spectrum synthetic pyrethroids is discouraged since they can kill non-target organisms such as spiders. The effectiveness of insect pathogens such as the
The yield loss due to insect pests in the region can also be attributed to lack of knowledge on the part of growers in some countries. For instance, Minja et al., (1999) observed that smallholder pigeonpea growers in some countries in the region did not control insect pests with conventional pesticides in the field. The majority (70% ) used wood ash and about 10% used pirimiphos-methyl (Actellic dust) to protect both the grain and seed. Moreover, according to Minja et al., (1999) some farmers lacked sufficient training to distinguish between the damage caused by the various insect pest groups.
Because of the relative importance of different insect species due to location, flowering time and season, future studies could focus on quantifying the damage caused by each insect type in order to determine its pest status on the crop as well as the economic threshold. In southern Africa, economic thresholds for pigeonpea pests have not been established. In other pigeonpea production regions, one larva (or three eggs) per plant can be considered as the threshold level for applying insecticides (Singh and Oswalt 1992). Likely, determining these economic thresholds would contribute to the regional optimization of the pigeonpea value chain which encompasses input supply, policy makers, farmers, harvesting, storage, processing and marketing.
The main end-use grain qualities of pigeonpea such as grain color and size, were measured in each of the field experiments conducted at Chitedze, Ilonga, Kiboko, Ngabu and Thohoyandou as described above. The results showed that the improved cultivars possessed large (100-grain weight = 14 g), white grain (Table 1) which are preferred by end-users in the region. These types are easier to cook compared to the small-seeded types. In contrast, most of the landraces in the region possess brown (or dark) as observed for MtawaJuni even though the size may be acceptable. Nonetheless, the grain color is relatively easy to change through conventional breeding approaches. These end-use qualities are also important in the adoption of new cultivars by growers in the region (Shiferaw et al., 2007).
There is potential for improving the depressed productivity of pigeonpea smallholder cropping systems in the region by using improved cultivars. The results from the various field evaluation studies demonstrated consistently that improved cultivars can produce several fold higher yields than unimproved landraces. In addition, wilt resistant cultivars showed that even under high disease pressure, they can attain optimum grain yields across the region. While inter-cropping is popular among smallholder growers, the crop can be managed better particularly in terms of pest and weed control if it is planted as a sole crop. Ideally, the crop requires effective control of insect pests. Because of its multiple benefits, pigeonpea offers smallholder farmers in the region realistic opportunities for increasing the production of grain legumes.
Soybean (
This condition was related to the discouraged situation of soybean production during the last 10 years (2010–2020). The average productivity during this period was 1.50–1.54 t/ha and no significant increase was recorded [2]. Also, only a slight increase in the harvested area occurred. A number of problems were noted regarding such conditions, including (a) high competition of land use with other commodities, (b) low stability of the yield as soybeans are highly susceptible to pest and disease attacks, (c) efforts to extend the planting area has not been fully succeeded, (d) relatively low quality of seeds as the soybean seed industry has not been well developed, (e) less conducive of soybean trading system, (f) less intensive cultivation techniques, and (g) low profit of soybean farming relative to other crops.
Soybean was targeted to be self-sufficiency by the Government in 2014 through four main strategies as follows: (1) gradually increasing the productivity (2) improving the roles of public and private sectors as well as local government in soybean development, (3) improving the marketing and trading system to be more conducive to farmers, and (4) improving the source of farming capital and partnerships. As a follow-up of such strategies, action steps were undertaken to achieve soybean self-sufficiency, including (a) supporting the research activities, which concerned on the release of new improved varieties with high yield potential, resistance to biotic and abiotic stress, short maturity; assembling the advanced cultivation technologies; and implementing different methods of dissemination, (b) initiating the growth of seed industry in soybean producing areas, (c) subsidizing the fertilizer prices, and (d) improving the access for agricultural tools and machinery application. However, these efforts have not fully succeeded as the increased rate of soybean productivity at the farmer level was considerably low, the planting and harvested areas were stagnant and even tended to decline, resulting in a decreased domestic production. As a consequence, a large amount of soybean is imported annually, suggesting more efforts and proper strategies are needed to achieve soybean self-sufficiency in Indonesia.
This paper will discuss the soybean production matters in Indonesia, including the current status and predicted soybean production and demand, the national program for increasing production, land availability for soybean development and specific production technologies for the different agroecosystems as well as the essential socio-economic aspects to support the achievement of soybean self-sufficiency in Indonesia.
The development of the harvested area, productivity, production, and import of soybean in Indonesia during the period 2016–2020 and the prediction for the year 2024 are presented in Table 1. Until 2020, the harvested area and production highly fluctuated, whereas the productivity tended to increase. It is estimated that the soybean harvested area until 2024 will not significantly expand as soybean hardly competes with other commodities, particularly maize. There was a considerable increase in soybean production (49.07%) during 2019–2020 as a result of expanding the harvested area. However, for the next four years, it is predicted that soybean production will tend to decline by 3% per year [3]. This was due to the competition of land use with other profitable commodities, such as corn and chili, resulting in a decrease in the harvested area of about 5% per year. Even though the productivity increased by 2% per year, this value was set below the rate of declined harvested area, thus giving no significant increase in soybean production. As a result, a large amount of soybean needs to be imported with an average of 2.49 million tons per year.
Years | Harvest area (ha) | Productivity (t/ha) | National production (t) | National demand (t) | Net Import (t) | The additional need of harvested area (ha) |
---|---|---|---|---|---|---|
2016 | 576,987 | 1.49 | 859,653 | 3,121,456 | 2,261,803 | 1,517,989 |
2017 | 355,800 | 1.51 | 538,730 | 3,103,475 | 2,671,914 | 1,698,507 |
2018* | 493,546 | 1.31 | 650,000 | 3,215,258 | 2,565,257 | 1,958,212 |
2019* | 285,270 | 1.49 | 424,190 | 2,726,091 | 2,301,902 | 1,544,900 |
2020** | 381,331 | 1.65 | 632,326 | 3,293,377 | 2,661,051 | 1,612,758 |
2021** | 262,612 | 1.69 | 613,318 | 3,279,452 | 2,666,134 | 1,577,594 |
2022** | 344,455 | 1.72 | 594,629 | 3,240,236 | 2,645,607 | 1,538,144 |
2023** | 326,861 | 1.76 | 576,278 | 3,163,759 | 2,587,481 | 1,470,160 |
2024** | 309,849 | 1.80 | 558,293 | 3,030,085 | 2,471,792 | 1,373,218 |
The development and projected of harvested area, production, and import of soybean in Indonesia during the period 2016–2024 [3].
Agreement figures of Central Bureau of Statistics (BPS) and the Indonesian Ministry of Agriculture.
Forecast of the Indonesian Agricultural Data and Information Center.
Note:
The national demand ranged from 2.73 up to 3.29 million tons during the period 2020–2024, which is mostly for consumption purposes. The consumption level of soybeans during this period is predicted to fluctuate and tends to increase by 1.46% per year. In 2019, the figure was 10.17 kg and it slightly increased to 12.15 kg/capita/year in 2020 [3]. It is assumed to be associated with the global pandemic of Covid-19, which led to a decline in people’s purchasing power for animal protein sources and shifting to soybean as an affordable protein source, particularly as tempe and tofu. In addition, the increase in soybean consumption is also influenced by the healthy lifestyle of the middle and upper class who prefer a vegetarian diet. It seems that the consumption level will go back to 10.74 kg/capita/year in 2024. Table 1 shows that the self-sufficiency in soybean within the next four years (2021–2024) can be achieved with an additional harvested area of 1.3–1.5 million hectares per year and productivity of 1.7–1.8 t/ha. Even though it seems hard to achieve such figures, the Government relentlessly encourages both the Ministry of Agriculture and farmers to increase the national soybean production.
Since 2000, the Government has been working hard to increase soybean production in order to achieve self-sufficiency through the program entitled “Gema Palagung”, “Bangkit Kedelai”, and “Farmer’s School for Integrated Crop Management/FSICM for soybean”. In 2018, a particular intercropping program between soybean with upland paddy or maize was launched, covering an area of 22 thousand hectares in 22 provinces [4]. Initially, the Government established the target for soybean self-sufficiently in 2014. However, as it unsucceded, the target was postponed to be 2017 and again postponed to be 2018, and then to 2020. In 2017–2018, the Ministry of Agriculture had a target of soybean planting area approaching 2 million hectares. Planting started from October to December 2017 with the first target of 500 thousand ha (approximately 25% of the total target). The remaining 1.5 million hectares expectedly can be fulfilled in the next planting season in 20 provinces, from Aceh in the west to East Nusa Tenggara in the eastern part of Indonesia. Meanwhile, another 500 hectares of land were available from the existing traditional farmers. It is estimated that in 2018, the soybean planting area will be becoming 2.5 million hectares [5] and would meet the domestic demand if the productivity was 1.5 t/ha.
Nevertheless, such a target was hard to be achieved as in fact, the total soybean production was only 650,000 tons in 2018 with a harvesting area of 493,546 hectares. In addition to climate and technical/cultivation factors, this failure was also related to economic aspects. It is obvious that soybean farming requires high input, possesses a high risk of crop failure, particularly due to pest and disease attacks, and inadequate income or less profitability. Planting of soybean starting from land preparation to harvesting and processing costs seven to nine million IRD per hectare and 60% of which is accounted for labor cost. The soybean production process in the field is also inefficient as most of the activities are done manually. In fact, the Government has established the selling price of soybean at the farm level that was about IDR 8,500 per kg in 2017 as Minister of Trade’s Regulation no 27/2017. However, the price is normally following the market conditions and frequently is below the selling price determined by the Government, particularly during the harvesting season giving a low profit to soybean farming.
Indonesia has a wide and diverse potential land for the development of soybean. Table 2 shows that there are 3.8 million hectares of irrigated paddy fields and 3.6 million hectares of non-irrigated paddy fields available (optimal land). In irrigated paddy fields, soybean can be grown using a cropping system of paddy-paddy-soybean, and a paddy-soybean cropping system in non-irrigated paddy fields. The main obstacle of soybean cultivation in optimal land is competition with other commodities that have higher economic value, especially maize. Therefore, soybean development in this optimal land should be selected to those lands that have less water available for growing maize. The need for water to grow soybean is only about half compared to growing maize.
Islands as central of soybean production | Irrigated lowland (ha) | Non-irrigated lowland (ha) | Drylands (ha) |
---|---|---|---|
Sumatera | 676,816 | 852,985 | 3,655,378 |
Jawa | 2,258,066 | 1,549,255 | 2,613,514 |
Bali+Nusa Tenggara | 197,316 | 245,619 | 921,281 |
Kalimantan | 214,298 | 432,462 | 1,605,806 |
Sulawesi | 430,621 | 508,033 | 1,981,629 |
Maluku | 10,094 | 9,448 | 252,032 |
Papua | 17,180 | 8,558 | 468,358 |
Indonesia | 3,804,391 | 3,606,360 | 11,497,998 |
Irrigated and non-irrigated lowlands available for soybean development in Indonesia [6].
There is also the potential of sub-optimal lands for the development of soybean in Indonesia, including dry acidic land, dryland with dry climate, and tidal land area, accounting for 4.5 million ha, 1.2 million ha, and 0.8 million ha, respectively (Table 3). The acidic land showed the least favorable for soybean production due to lower fertility, potential toxicity from soluble forms of microelements such as Al, Mn, and Fe, and unfavorable physical properties [8, 9, 10]. Therefore, to obtain high soybean productivity in this type of land (soil), use of ameliorants and high doses of inorganic fertilizers are needed. On the dry land with a dry climate, the main constraint faced is the short wet month that is only around 3–4 months/year with a rainfall >200 mm/month. In this region, soybean needs to compete with other staple food crops, such as upland rice and maize. In tidal swampland, constraints like water-saturated root, high pyrite, the toxicity of Al, Fe, and Mn, as well as deficiencies of N, P, K, Ca, and Mg may limit soybean production [10, 11]. Therefore, specific cultivation technology is essential for such different types of land.
Island | Dry acidic soil (× 1,000 ha) | Dryland with dry climate (× 1,000 ha) | Tidal swampland (× 1,000 ha) | Total (× 1,000 ha) | ||||||
---|---|---|---|---|---|---|---|---|---|---|
AOU | AFC | AFP | AOU | AFC | AFP | AOU | AFC | AFP | ||
Sumatera | 536.6 | 104.3 | 659.5 | 24.9 | 34.0 | 58.3 | 137.4 | 13.5 | 271.2 | 1,839.7 |
Jawa | 46.3 | 0.0 | 202.2 | 8.7 | 0.0 | 31.6 | 0.3 | 0.0 | 0.0 | 289.1 |
Bali+NT | 1.6 | 0.0 | 0.0 | 257.8 | 10.7 | 30.4 | 0.0 | 0.0 | 0.0 | 300.5 |
Kalimantan | 329.9 | 227.9 | 1,297.8 | 0.0 | 0.0 | 0.0 | 82.1 | 1.6 | 46.5 | 1,985.8 |
Sulawesi | 25.8 | 14.2 | 0.0 | 61.0 | 42.8 | 0.0 | 0.8 | 0.0 | 0.0 | 144.6 |
Maluku | 0.0 | 39.6 | 0.0 | 0.0 | 0.0 | 0.0 | 2.7 | 3.3 | 0.3 | 45.9 |
Papua | 11.0 | 304.3 | 671.4 | 9.7 | 163.5 | 437.2 | 0.4 | 84.8 | 128.0 | 1,810.3 |
Indonesia | 951.2 | 690.3 | 2,830.9 | 362.1 | 251.0 | 557.5 | 223.7 | 103.2 | 446.0 | 6,415.9 |
The suboptimal lands available for soybean development in Indonesia [7].
Note: AOU = Area of Other Uses, AFC = Area of Forest Conversion, AFP = Area of Forest Production, NT = Nusa Tenggara.
Soybean cultivation in the irrigated paddy lowland generally follows the cropping pattern of paddy-secondary food crop, while the pattern is paddy-secondary food crop in the non-irrigated paddy land (rainfed land). It seems that soybeans yet have to compete with other commodities, especially maize or other food crops. Currently, the productivity of soybean using existing farmer’s technology is about 1.5–1.8 t/ha. Using high-yielding improved varieties and good environmental management through the application of advanced cultivation technology makes it possible to achieve soybean productivity as high as 3.0 t/ha in the lowland.
A number of new improved soybean varieties have the yield potential of more than 3.0 t/ha, namely Dega1, Detap 1, Mutiara 1, Dering 2, Biosoy 1, and Demas 2 [12] as presented in Table 5. In additon to new improved varieties, plant spacing is also an important factor in achieving high yield through optimal plant populations. Planting Burangrang, Grobogan, and Anjasmoro varieties at a spacing of 20–30 cm × 40 cm, two plants per hole with optimal fertilization in Malang, East Java gave a grain yield of 3.96 t/ha, 3.93 t/ha, and 3.36 t/ha, respectively [13]. Thus, to achieve the soybean yield >3.0 t/ha, the population of >340 thousand plants/ha which is obtained using a plant spacing of 30 cm × 15 cm needs to be applied as well as planting 2 plants/hole and optimal fertilizer application
Soybean variety | Plant spacing (cm), two plants/hill | ||
---|---|---|---|
50×15 | 40×15 | 30×15 | |
Number of crops can be harvested (×1,000) | |||
Dega 1 | 240.68bc | 255.20 b | 345.29 a |
Detap 1 | 204.41 c | 252.01 b | 344.62 a |
Derap 1 | 202.60 c | 249.16 b | 350.24 a |
Devon 1 | 204.72 c | 260.55 b | 358.90 a |
Seed yield (t/ha) | |||
Dega 1 | 1.98 d | 2.21 d | 3.12 b |
Detap 1 | 2.14 d | 2.61 c | 3.53 a |
Derap 1 | 1.90 d | 1.97 d | 3.15 b |
Devon 1 | 2.11 d | 2.69 c | 3.75 a |
The yield of soybean varieties in several plant spacing in irrigated paddy fields in Banyuwangi-East Java [14].
Note: The values within the same observation followed by the same letter are not significantly different at 5% DMRT level.
A study in the rainfed Alfisol soil of Maros, South Sulawesi, which had a pH level of 6.2–6.7 and moderate soil fertility showed that soybean yield increased from 1.6 t/ha (existing technology) to 2.7 t/ha through the application of advanced cultivation technology [15]. This technology consisted of using good quality seed, sufficient fertilizer (30 kg/ha N + 48 kg/ha P2O4 + 30 kg/ha K2O), rhizobium inoculant 250 g/50 kg of seeds, and organic fertilizer (1.5 t/ha). The performance of soybean crops grown after paddy in the irrigated lowland is presented in Figure 1. Using such technology, the labor cost accounts for the largest portion of the total production costs, reaching about 65% and 72% for advanced and existing technology, respectively. Nevertheless, both the R/C and B/C ratio of applying the advanced technology is higher relative to those of the existing technology (Table 5).
The performance of soybean crop grown after paddy in the irrigated low land.
Components | Soybean cultivation technology | |
---|---|---|
New technology | Existing (Farmers’) technology | |
Production costs (IDR/ha) | ||
a. Production facilities | 2,593,000 (34.7%) | 1,470,000 (27.5%) |
b. Labor | 4,876,667 (65.3%) | 3,880,000 (72.5%) |
Total costs (IDR/ha) | 7,469,667 (100.0%) | 5,350,000 (100.0%) |
Productivity (kg/ha) | 2,725 | 1,590 |
Total revenue (IDR/ha)* | 16,350,000 | 9,540,000 |
Total profit (IDR/ha) R/C ratio | 8,880,333 2.2 | 4,190,000 1.8 |
B/C ratio | 1.2 | 0.8 |
Financial analysis of soybean farming for advanced and farmer’s technologies in the rainfed land of South Sulawesi in the dry season (May to August) of 2017 [15].
With a selling price of soybean IDR 6,000/kg.
Note:
The cropping patterns in the dryland are generally maize-maize, upland paddy-maize, maize-peanuts, or maize-soybeans. Meanwhile, in a dryland with a dry climate, farmers normally only grow maize or upland paddy during the rainy season. The rainfall in the dryland with a dry climate is approximately <2000 mm per year with a dry period >7 months per year (<100 mm rainfall per month). This type of agroecology is mostly found in Bali and Nusa Tenggara, Sulawesi, and Java [11]. However, the insufficient and non-uniform distribution of rainfall in the dryland considerably results in drought stress during the growing period of soybean and may cause yield reduction and even harvesting failure [16]. In this particular land, soybean development can only be performed through intercropping with maize as it is one of the major staple foods as well as a source of cash income for farmers [17]. Maize productivity in the dryland is relatively low, which ranges from 2.5 to 5.0 t/ha [2]. This is caused by the erratic distribution of rainfall and less optimal maize cultivation by farmers. The introduction of soybean in the dryland through intercropping with maize is expectedly would increase the land productivity and farmer’s income. Intercropping system has been adopted all over the world as it can increase land-use efficiency [18, 19].
The use of adapted cultivars and optimal plant spacing in soybean intercropping systems can increase land productivity, reduce the risk of crop failure, increase crop yields and farmers’ income [19, 20, 21]. The cropping pattern of soybean monoculture in the dryland with a dry climate could produce dry seed about 1.4–2.4 t/ha depending on the variety used and distribution of rainfall. However, this cropping pattern is difficult to be developed in the dryland as such a pattern was less profitable relative to growing maize [9]. Therefore, the development of soybean in the dryland, particularly in the maize producing area should be done by intercropping. Soybean intercropping with a plant spacing of 30 cm × 15 cm, planting two seeds per-hill and planting maize in a double row with a plant spacing of (40 × 20) cm × 200 cm and one seed per hill (Figure 2) is able to produce high maize yield and increase the farming profit. Intercropping soybean variety of Dena 1 with maize in the dry land with dry climate (Tuban, East Java) showed higher benefit than using Argomulyo and Dega 1 varieties (Table 6). Dena 1 variety is particularly released for intercropping purposes as it is tolerant to shading up to 50%. Other soybean varieties that are suitable for intercropping with other crops, including young plantation crops are Dena 2, Denasa 1, and Denasa 2 (Table 5). Also, there are soybean varieties tolerant to drought stress, namely Dering 1, Dering 2, and Dering 3 (Table 7).
The optimal crop layout for soybean intercropping with maize in the dryland (a) and the crop performances in the field (b) [
Planting patterns | Yield (t/ha) | Total revenue (IDR 000/ha) | Cost production (IDR 000/ha) | Total cost (IDR 000/ha) | Total benefit (IDR 000/ha) | ||
---|---|---|---|---|---|---|---|
Maize | Soybean | Maize | Soybean | ||||
‘Maize NK212’ monoculture | 5.488 | 0 | 21,952 | 8,032 | 0 | 8,032 | 13,920 |
‘Argomulyo’ monoculture | 0 | 2.430 | 15,795 | 0 | 7,022 | 7,022 | 8,773 |
‘Dena 1’ monoculture | 0 | 1.873 | 12,174.5 | 0 | 6,802 | 6,802 | 5,372.5 |
‘Dega 1’ monoculture | 0 | 1.417 | 9,210.5 | 0 | 6,622 | 6,622 | 2,588.5 |
‘Maize NK 212’ + ‘Argomulyo’ | 4.876 | 1.447 | 28,909.5 | 7,972 | 4,540 | 12,512 | 16,397.5 |
‘Maize NK212’ + ‘Dena 1’ | 6.297 | 1.017 | 31,798.5 | 8,252 | 4,400 | 12,652 | 19,146.5 |
‘Maize NK212’ + ‘Dega 1’ | 5.635 | 0.820 | 27,870 | 8,047 | 4,180 | 12,227 | 15,643 |
‘Maize NK212’ monoculture | 5.648 | 0 | 22,592 | 9,737 | 0 | 9,737 | 12,855 |
‘Argomulyo’ monoculture | 0 | 2.880 | 18,720 | 0 | 7,342 | 7,342 | 11,378 |
‘Dena 1’ monoculture | 0 | 2.280 | 14,820 | 0 | 6,962 | 6,962 | 7,858 |
‘Dega 1’ monoculture | 0 | 3.060 | 19,890 | 0 | 7,542 | 7,542 | 12,348 |
‘Maize NK212’ + ‘Argomulyo’ | 3.657 | 1.927 | 27,153.5 | 9,817 | 4,520 | 14,337 | 12,816 |
‘Maize NK212’ + ‘Dena 1’ | 4.157 | 1.687 | 27,595.5 | 9,927 | 4,360 | 14,287 | 13,306.5 |
‘Maize NK212’ + ‘Dega 1’ | 3.367 | 1.613 | 23,952.5 | 9,787 | 4,380 | 14,167 | 9,785.5 |
Farming income of soybean intercropping with maize, Tuban District, East Java, Indonesia, planting season 2019 [9].
Notes: The population of maize crops 100% (plant spacing of 80 cm × 20 cm, 2 seeds per-hill) was 62,500 crops/ha and soybean 333,333 crops/ha. The selling price of maize and soybean (dry seeds) were IDR 4,000/kg and IDR 6,500/kg, respectively.
Variety | Seed coat color | 100-seed weight (g) | Protein (% dw) | Fat (% dw) | Potential yield (t/ha) | Specific characters | Year of release |
---|---|---|---|---|---|---|---|
Gepak Kuning | Yellow | 8.3–10.3 | 35.4–41.1 | 13.4–15.1 | 2.9 | Adaptive in irrigated lowland and upland, both in rainy and dry seasons | 2008 |
Dering 1 | Yellow | 10.7 | 34.2 | 17.1 | 2.8 | Drought tolerant; adaptive in irrigated lowland and dry land (upland) | 2012 |
Dering 2 | Light yellow | 14.8 | 35.9 | 19.7 | 3.3 | Drought tolerant during the reproductive phase | 2019 |
Dering 3 | Light yellow | 13.9 | 40.5 | 17.5 | 3.0 | Drought tolerant during the reproductive phase | 2019 |
Gema | Light yellow | 11.3–11.9 | 37.8–39.1 | 15.6–19.1 | 3.1 | Adaptive in irrigated lowland and dryland (upland) | 2011 |
Dena 1 | Yellow | 14.3 | 36.7 | 18.8 | 2.9 | Tolerant up to 50% crop-shading | 2014 |
Dena 2 | Yellow | 13.0 | 36.5 | 18.2 | 2.8 | Highly tolerant up to 50% crop-shading | 2014 |
Demas 1 | Yellow | 13.0 | 36.1 | 19.9 | 2.5 | Adaptive in a dryland with acidic soil; good planted at the altitude of 0–600 m asl | 2014 |
Demas 2 | Light yellow | 14.9 | 37.5 | 19.7 | 3.3 | Adaptive in dryland with acidic soil; early maturity; large-seed size | 2019 |
Demas 3 | Light yellow | 14.4 | 37.2 | 17.7 | 2.9 | Adaptive in dryland with acidic soil; early maturity; large-seed size; break-pods tolerant | 2019 |
Devon 1 | Yellow | 14.3 | 34.8 | 17.3 | 3.1 | High isoflavone content (2219.7 μg/g) | 2015 |
Devon 2 | Yellow | 17.0 | 37.9 | 18.8 | 2.9 | High isoflavone content (303.7 μg/g) | 2017 |
Anjasmoro | Yellow | 14.8–15.3 | 41.8–42.1 | 17.2–18.6 | 2.3 | Broadly adaptive in all land conditions | 2001 |
Panderman | Light yellow | 18.0–19.0 | 36.9 | 17.7 | 2.4 | — | 2003 |
Grobogan | Yellow | 18.0 | 43.9 | 18.4 | 3.4 | Broadly adaptive in all land conditions, particularly irrigated lowland | 2008 |
Burangrang | Yellow | 20.0 | 39.0–41.6 | 14.9–17.0 | 2.5 | — | 1999 |
Argomulyo | Yellow | 19.3–20.8 | 37.0–40.2 | 18.0–19.0 | 2.0 | — | 1998 |
Dega 1 | Yellow | 22.9 | 37.8 | 17.3 | 3.8 | Adaptive in irrigated lowland | 2016 |
Detap 1 | Yellow | 15.4 | 40.1 | 16.2 | 3.6 | Resistant to leaf rust | 2017 |
Deja 1 | Yellow | 12.9 | 39.6 | 17.3 | 2.9 | Highly tolerant to water saturation stress | 2017 |
Deja 2 | Yellow | 14.8 | 37.9 | 17.2 | 2.8 | Tolerant to water saturation stress | 2017 |
Depas 1 | Yellow | 11.9 | 39.8 | 19.5 | 2.8 | Adaptive in tidal land type C; good planted at the altitude of 0–600 m asl | 2020 |
Depas 2 | Yellow | 11.4 | 39.7 | 19.2 | 2.9 | Adaptive in tidal land type C; good planted at the altitude of 0–600 m asl | 2020 |
Denasa 1 | Yellow | 18.1 | 36.4 | 19.6 | 3.4 | Highly tolerant up to 50% crop-shading | 2021 |
Denasa 2 | Light yellow | 18.6 | 34.1 | 20.6 | 3.4 | Tolerant up to 50% crop-shading | 2021 |
Biosoy 1 | Yellow | 21.7 | 39.7 | 18.4 | 3.3 | Gamma irradiated soybean | 2018 |
Biosoy 2 | Yellow | 22.4 | 40.5 | 20.1 | 3.6 | Gamma irradiated soybean | 2018 |
Mutiara 1 | Yellow | 23.2 | 37.7 | 13.8 | 4.1 | High production in irrigated lowland; adaptive in irrigated lowland and dryland (upland) | 2010 |
Mallika | Black | 9.0–10.0 | 37.0 | 20.0 | 2.9 | Well adaptive in low land and high land; in rainy and dry season | 2007 |
Detam 1 | Black | 14.8 | 45.4 | 13.1 | 3.5 | High protein, suitable for soy sauce | 2008 |
Detam 2 | Black | 13.5 | 45.6 | 14.8 | 3.0 | High protein, moderate drought tolerant, suitable for soy sauce | 2008 |
Detam 3 Prida | Black | 11.8 | 36.4 | 18.7 | 3.2 | Moderate drought tolerant; early maturity | 2013 |
Detam 4 Prida | Black | 11.0 | 40.3 | 19.7 | 2.9 | Drought tolerant; early maturity | 2013 |
As discussed previously, acidic soils are the least favorable condition for soybean cultivation, therefore the use of ameliorants and high doses of inorganic fertilizers is essential in terms of increasing productivity. The application of 23 kg/ha N + 27 kg/ha P2O5+ 30 kg/ha K2O + 1,500 kg/ha organic fertilizers and rhizobium biofertilizer 0.25 kg/50 g seeds in acidic soil with a pH of 5.30 and Al saturation of 30% exhibits a good growing performance of four soybean varieties, namely Anjasmoro, Panderman, Dega 1, and Demas 1 [24]. These varieties give a yield of 2.52 t, 2.29 t, 2.72 t, and 1.78 t per hectare, respectively. Demas 1, Demas 2, and Demas 3 varieties are tolerant to acid soil with a potential yield ranging from 2.5 t up to 3.3 t/ha (Table 7). Biofertilizers also have a significant role in increasing soybean yield through the natural processes of nitrogen fixation, solubilizing phosphorus, stimulating plant growth, improving soil texture, pH, and other soil properties [25, 26].
In the acidic soil of Banten with a pH of 5.5, the use of 200 g/ha of biofertilizer could substitute 50% of the recommended inorganic fertilizer [27]. Another study in acidic soil in Lampung reported that the use of Rhizobium biofertilizer tolerant to acidic soil about 1.5 t/ha and organic fertilizer enriched with P and Ca, could replace the use of 100% N and P, and 50% of K. The yield also increased more than 50% relative to control and gave higher yield compared to recommended NPK dosage [28]. The performance of soybean crops grown in acidic soil is presented in Figure 3.
The performance of soybean crop at 40 days after planting in the acidic soil in Lampung, Indonesia.
In tidal swampland, water-saturated roots, high pyrite, the toxicity of Al, Fe, and Mn, deficiencies of N, P, K, Ca, and Mg are the major constraints in soybean development [8, 10]. Among such limitations, low soil pH and high Al saturation are more concerned regarding soybean growth as they may cause a decrease in nitrogen fixation and nutrient uptake, particularly phosphorus which is important for cell growth and photosynthesis. It was reported that liming can improve the growth and yield of soybean in the tidal swampland of South Kalimantan [10]. The highest yield was obtained at a rate of liming equivalent to 10% of Al saturation, which was applied by mixing the lime with soil up to 20 cm depth. Another study in tidal swampland of South Kalimantan investigated that using dolomite to decrease the Al-dd saturation by 20% by using organic fertilizers (1.25 t/ha), application of bio-fertilizer (0.25 kg/50 kg seeds), and inorganic fertilizer (23 kg/ha N, 27 kg/ha P2O5 and 30 kg/ha K2O) gave the yield about 2.0 t/ha [24].
In addition, soil water management can be applied to reduce the pyrite content as the soil is in a reductive condition [29]. The response to water-saturated conditions varied among soybean varieties. Tanggamus and Anjasmoro, the yellow-seeded soybean are classified as adaptive varieties, while the black-seeded soybean varieties, such as Cikuray, Ceneng, and Lokal Malang are less adaptive when grown under the saturated condition in tidal swampland. However, using the technology called water-saturated soybean farming [30], which consisted of appropriate application of Ca (dolomit) and NPK fertilizers with optimal plant population, the yield of soybean cultivation in tidal swampland in South Sumatera could reach 3.2–3.5 t/ha. There are some soybean varieties adapted to tidal swampland, namely Depas 1 and Depas 2 (Table 7).
A study on soybean cultivation in tidal swampland of South Kalimantan [22] also reported that the use of technological package (listed as an alternative technology in Table 8) consisting of the application of dolomite until soil Al saturation is reduced to 30%, NPK fertilizer with a dosage of 23 kg/ha N + 27 kg/ha P2O5 + 30 kg/ha K2O + 1,500 kg/ha organic fertilizers, and rhizobium inoculant of 0.25 kg/50 kg seed as well as the saturated soil culture (SSC) technology was able to increase the number of filled pods per plant and yield per hectare relative to farmer’s existing technology. Using the SSC and alternative technology packages, the seed yield increased by 27% and 17%, respectively compared to that of farmers’ existing technology (Table 8). The performance of soybean crops treated with an alternative technology is presented in Figure 4.
Technological package | Number of filled pods/plant | 100 seeds weight (g) | Seed yield (t/ha) | Increased yield (%) |
---|---|---|---|---|
Existing | 30.70 b | 15.52 a | 2.067 a | 100 |
SSC | 34.55 ab | 15.40 a | 2.422 b | 117 |
Alternative | 40.80 a | 15.45 a | 2.625 c | 127 |
Number of filled pods, 100-seed weight, and soybean seed yield obtained from the application of different technological packages in tidal swampland. Wanaraya District, Barito Kuala Regency, South Kalimantan [24].
Note: The values followed by the same letter do not differ at the 5% DMRT level. SSC = Saturated Soil Culture.
An example of the performance of 40 days after planting of soybean crops in tidal swamps with soil Al saturation of 30% in South Kalimantan Province, Indonesia.
In addition to several types of agroecosystem as described previously, growing soybean under shading is also potential for soybean development. Shaded land is available under young high state crop plantations, such as teak, palm oil, and eucalyptus trees. The land associated with teak and eucalyptus trees is generally under the management of State Company, namely Perhutani where the lands/areas are managed by the local community (FACI/Forest Area Community Institution), while the land planted with palm oil crops belongs to the Government. However, there is no accurate data regarding the potential shaded land that can be used for soybean development. This includes the dry land agroecology with flat or hilly topography. Therefore, soybean planting in this agroecology can be only done in the beginning of the rainy season.
The yield of soybean grown under the shading of four to six-year-old of palm oil tree (50% shading) was relatively lower (0.54 t/ha) than that of without shading (2.6 t/ha). Burangrang, Anjasmoro, and Grobogan varieties show similar tolerance to such shading. The recommended N fertilizer application is 100–150 kg/ha [31]. In another study, the application of 34.5 kg/ha N + 36 kg/ha P2O5 + 60 kg/ha K2O + 20 t/ha manure and planting space of 20 cm × 20 cm using three soybean varieties (Dena 1, Anjasmoro, and Grobogan) were able to produce seeds of about 1.8 t/ha at 25% shading level and about 1.4 t/ha at 50% shading level [32]. In particular, Dena 1, Dena 2, Denasa 1, and Denasa 2 varieties are released for shading cultivation of soybean (Table 7).
In terms of soybean grown under the two-year-old teak tree in Blora, Central Java, using the technological package of NPK fertilization (30 kg/ha N+ 66 kg/ha P2O5 + 30 kg K2O), biofertilizer (20 g/10 kg of seed), “legowo” planting space (30 cm–50 cm × 15 cm) or regular planting space (40 cm × 15 cm), gave a yield about 1.5 t/ha. Meanwhile, using the existing technology (farmer’s method), only 0.75 t/ha of seeds was obtained (Table 9) [33]. Soybean grown under the young teak stands and eucalyptus trees is presented in Figure 5.
Components of performance | Soybean variety | ||||
---|---|---|---|---|---|
Dega 11 | Dena 11 | Anjasmoro1 | Argomulyo1 | Local2 | |
Average of productivity (t/ha) | 1.35 | 1.10 | 1.05 | 0.99 | 0.63 |
a. Production input (IDR/ha) | 3,844,000 | 3,844,000 | 3,844,000 | 3,844,000 | 3,844,000 |
b. Labor (IDR/ha) | 1,350,000 | 1,350,000 | 1,350,000 | 1,350,000 | 1,350,000 |
Total production cost (IDR/ha) | 5,194,000 | 5,194,000 | 5,194,000 | 5,194,000 | 5,194,000 |
Total revenue* (IDR/ha) | 9,450,000 | 7,700,000 | 7,350,000 | 6,930,000 | 4,410,000 |
Total income (IDR/ha) | 4,256,000 | 2,506,000 | 2,156,000 | 1,736,000 | (784,000) |
R/C ratio | 1.8 | 1.5 | 1.4 | 1.3 | 0.7 |
B/C ratio | 0.8 | 0.5 | 0.4 | 0.3 |
Farming income of soybean farming under teak shade, Blora Regency, Central Java, 2018 [33].
Planting spacing was 40 cm × 15 cm (technology of Iletri).
Planting spacing was 20 cm × 20 cm (existing technology).
Revenue = the average of yield multiplied by the selling price of soybean seeds i.e. IDR 7,000/kg. Figure in the bracket showed total income was minus or soybean farming lost.
Note:
Soybean grown under the teak stands (left) and eucalyptus trees (right) in Blora, Central Java.
There are three primary challenges in terms of increasing the soybean production in Indonesia in order to achieve self-sufficiency, i.e. low fertility of the available land, less competition of existing soybean varieties in terms of the quality traits, and relatively low selling price of locally produced soybean.
Java Island is the most fertile and largest planted area of soybean in Indonesia. Shifting the soybean planting area to outside of Java has been started since the 1980s. The available land for crop cultivation in such areas, including soybean, is more than 40 million hectares, however, the major soil type is ultisol. This mostly exists in Sumatra, Bali, Kalimantan, Sulawesi, and Papua. Constraints, like acidity, low content of organic matter, and phosphorus (P) availability naturally occurred in ultisol soil, thus more inputs are needed to provide optimal conditions for producing soybean [34].
Quality traits of local or domestic soybean are also important to drive or push the production of soybean in Indonesia. However, there is a limited quality trait of local soybean to compete with imported soybean. Previously, the improved soybean varieties belonged to small and medium-seeded, which is not desired for tempeh ingredients. Large-seeded (> 14 g/100 seeds) is favored for tempeh preparation as it would give a good appearance and high volume development, while small to large seed sizes are suitable for tofu making [22]. Therefore, for the last two decades, a number of improved varieties with large seed sizes have been released (Table 7) to meet such preferences. However, the released varieties concerning health benefits, such as Devon 1 and Devon 2 with high isoflavone content (Table 7) that has antioxidant activity, have not been attractive for consumers and farmers based on this superiority or character as the market is not yet available. Therefore, lack of market quality traits is also an essential challenge for producing local soybean.
In the case of price, the imported soybean always has a lower price than the local soybean. It is calculated [35] that the profitable price for farmers is minimally IDR 9,000 per kg or US$ 0.6/kg (US$ 1 = IDR 14,000). With this selling price, farmers would be able to cover the expenses for soybean production activity and gain some profit. However, the price of local soybean at the farm level is frequently around IDR 6,500 per kg, causing less interest of farmers to grow soybean. Therefore, the current average soybean productivity at the farm level (1.5 t/ha) needs to be increased to at least 3.0 t/ha, thus soybean farming income can compete with those of other commodities, such as maize as presented in Table 10.
Parameter | Commodity farming | ||
---|---|---|---|
Maize | Soybean (Farmer technology) | Soybean (Improved technology) | |
Productivity (t/ha) | 5,648 | 1,873 | 3,060 |
Selling price (IDR/kg) | 4,000 | 6,500 | 6,500 |
Revenue (IDR/ha) | 22,592,000 | 12,174,500 | 19,890,000 |
Production cost (IDR/ha) | 9,737,000 | 6,800,200 | 7,542,000 |
Profit (IDR/ha) | 12,855,000 | 5,372,500 | 12,348,000 |
B/C | 1.32 | 0.79 | 1.64 |
Income of maize farming compared to soybean farming using existing farmer technology and improved technology [9].
Indonesia has a good chance to increase soybean production and fulfills domestic needs. This opportunity can be seen from the market demand, land and improved varieties availability, and the Government’s strong will. Soybean demand as food and feed increases continuously and be expected to increase in the next years. The highest portion of demand comes from processed food mainly tempeh and tofu. Another high demand is coming from the cattle feed industry which is expected to increase continuously as part of increasing cattle production. Therefore, by increasing the national soybean production, the Government wants to fulfill these demands by using national production and reducing imports [36].
Other potential opportunities are the availability of source seeds, especially in the form of “Breeder Seeds” for the production of certified seed of “Foundation Seeds”, “Stock Seeds”, and “Extension Seeds” to fulfill the need for quality soybean seed for the area of production. The “Breeder Seeds” available are various soybean varieties with a various specific traits, including the variety tolerance to pod borer and pod sucking insect, shading, flooding, and drought. The readiness of soybean production technology for various agroecosystems can also be stated as an opportunity because those significantly contribute to the high productivity and also for the production of soybean in the country.
Soybean in Indonesia is the third important staple food after rice and maize. The need for this commodity continuously increases every year due to the increase in population. The trend of domestic soybean production tended to decline and do not meet the demand leading to the increase of soybean import every year. There are three challenges that require drastic changes so that local soybean production is able to meet domestic needs. First, the current productivity at the farm level, which is around 1.5 t/ha must be increased to at least 2.0–3.0 t/ha. It will also help soybean farming income compete with those of other commodities. Second, the soybean harvested area which only reaches 0.3 million hectares in 2019 must be increased at least become 1.7 million hectares. The potential soybean planting areas in Indonesia are the optimal land including irrigated lowland and rainfed after paddy (rice), as well as suboptimal lands such as dryland, acidic land, tidal land, and shaded land under young plantation crops. Soybean productivity in those kinds of agroecosystems can reach 1.8–3.0 t/ha, depending on the type of land, the improved varieties used, and the applied of cultivation technological package. Third, it is necessary to develop agricultural machinery that can reduce the farming cost, so that soybean farming is more efficient and able to provide higher profit.
Some efforts should be made to increase national soybean production to achieve self-sufficiency, including improving the attractiveness point of soybean farming, launching the program(s) to increase soybean production starting from the central government to the regions, accelerating technology transfer dan adoption of the high yielding improved varieties, reducing soybean import gradually, improving the cooperation among stakeholders, and providing a good market guarantee for soybean farming.
We would like to thank to the Indonesian Agency for Agricultural Research and Development (IAARD) through the Indonesian Legumes and Tuber Crops Research Institute (ILETRI) for the support of research results facilities to compile this manuscript.
We declare that we have no conflicts of interest on the entire manuscript.
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Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. 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Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. 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Fungal infectious illness prevalence and prognosis are determined by the exposure between fungi and host, host immunological state, fungal virulence, and early and accurate diagnosis and treatment. \r\nPatients with both congenital and acquired immunodeficiency are more likely to be infected with opportunistic mycosis. Fungal infectious disease outbreaks are common during the post- disaster rebuilding era, which is characterised by high population density, migration, and poor health and medical conditions.\r\nSystemic or local fungal infection is mainly associated with the fungi directly inhaled or inoculated in the environment during the disaster. The most common fungal infection pathways are human to human (anthropophilic), animal to human (zoophilic), and environment to human (soilophile). Diseases are common as a result of widespread exposure to pathogenic fungus dispersed into the environment. \r\nFungi that are both common and emerging are intertwined. 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At the same time, they are faced with the Covid-19 pandemic leading to what some authors have called potential syndemics that might worsen the outcome of such infections. Therefore, it is important to conduct studies that examine parasitic infections in the context of the coronavirus pandemic for the benefit of all communities to help foster more informed decisions for the betterment of human and animal health.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/5.jpg",keywords:"Blood Borne Parasites, Intestinal Parasites, Protozoa, Helminths, Arthropods, Water Born Parasites, Epidemiology, Molecular Biology, Systematics, Genomics, Proteomics, Ecology"},{id:"6",title:"Viral Infectious Diseases",scope:"The Viral Infectious Diseases Book Series aims to provide a comprehensive overview of recent research trends and discoveries in various viral infectious diseases emerging around the globe. The emergence of any viral disease is hard to anticipate, which often contributes to death. 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Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. 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Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. 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We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. 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Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. 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