research papers on rice crop estimation

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Research papers on rice crop estimation

Evidently, Abd L concentrated near 1, while Abd P concentrated near 0. At heading stage, the uneven emergence of panicles may influence the accuracy of rice yield estimation. Since panicle abundance and leaf abundance were the indicators of how much panicle had been emerging, in our proposed approach the VIs were incorporated with the information of plot-level panicle abundance Abd P and leaf abundance Abd L to estimate the yield of rice.

In particular, VARI showed a really weak correlation with yield at heading stage r was Table 4. For further analysis, regression analysis has been used, and three linear relationship were developed using 23 samples: 1 yield vs. Table 5. As for Abd L multiplied indices, they were not susceptible to saturation although their R 2 was relatively lower. Then on the base of these, leave one out cross-validation approach was utilized to obtain the final yield estimation model.

The specific estimation formulas and the goodness of fit between measured yield and estimated yield was shown in Figure 5E. The primary purpose of this study was to improve the accuracy of rice yield estimation at heading stage based on the UAV data. At these two stages, rice gradually completed the transformation from vegetative growth to reproductive growth. The interval between each two stages was around 2 weeks, and rice had similar growth status except the influence of panicle.

At booting stage, the rice plant flourished but with no panicle appearing in canopy. On the contrary, the panicle gradually emerged in canopy at heading stage with the growth of rice plant. At these two stages, the changes of rice leaves were not obvious — Figure 3.

And this result revealed that the booting stage had better predictive ability for rice yield based on ground measured data. The reason for this was probably that the panicle began to emerge at heading stage. Sun et al. According to the principle of the used SunScan instrument, each organ of rice plant including leaf, panicle, and stem contributed to the LAI value. Compared with booting stage, the LAI value of heading stage contained extra panicle information. We chose 10 widely used VIs which were successfully applied in estimating vegetation grow-related parameters such as chlorophyll content, LAI, vegetation fraction and grain yield.

The similar result was found that the Pearson correlation coefficient r between VI and yield at booting stage was mostly higher than that at heading stage — Table 3. At heading stage, the plot-level VI was calculated from mixed components including different proportion of panicles, using VI alone for yield regression may introduce unexpected uncertainties. Therefore, the SMA was utilized to improve the predictive ability of VI in rice yield estimation at heading stage.

At this stage, the rice plant had really luxuriant growth and the paddy field was almost covered by rice plants. The canopy of rice was comprised of leaf and panicle — Figure 3. And the background of field was mainly soil wet soil and dry soil , because the water was drained off. In addition, the field management of rice was strict and there were no other plants affecting the growth of rice such as weed.

Based on fully constrained least squares linear SMA, the abundance images of six mainly endmembers in paddy field were derived. Of the six abundance images we obtained, the brightness of dry and wet soil abundance images was relatively low. And compared to panicle abundance image, the leaf abundance image was obviously brighter both in top layer and bottom layer.

This was consistent with the actual situation of paddy field. At heading stage, rice leaves grew lushly and the leaves occupied the largest proportion in paddy field. Due to the different nitrogen treatments applied in 24 plots, the growth situation of rice plant in different plots significantly varied from each other even at the same growth stage.

It is observed that there was obvious difference among different plots in leaf abundance images and panicle abundance images — Figure 7. In consideration of endmember selection, the sum of panicle abundance in top layer and bottom layer was calculated as panicle abundance Abd P , and the sum of leaf abundance in top layer and bottom layer was calculated as leaf abundance Abd L.

The result of analysis indicated that neither Abd L nor Abd P followed normal distribution and Abd L concentrated near 1, while Abd P concentrated near 0. This numerical distribution characteristic was in accordance with the sum-to-one constraint condition in fully constrained least squares linear SMA.

Accordingly, the abundance data was not adequate for yield estimation due to its aggregation effect — Figure 5B. The abundance data, indeed, implies the proportion information of different components in rice field. Therefore, Abd L and Abd P were combined together to give full play to the advantages of the two.

In this study, we developed a new approach to estimate rice yield at heading stage using the integration of VI and abundance information retrieved from the UAV image. The approach was simple but it given some significant enlightenment for the yield estimation of grain crop like rice. In the application of RS, the impact of spectral mixture must be considered and this is also important to UAV RS technology which has a really high spatial resolution.

And the SMA is a good way to get rid of the influence of different spectral components contained in remotely sensed images. Although the endmembers proposed in our approach were limited to rice yield estimation at heading stage, this work may offer a theoretical framework for yield estimation in grain crops which have obvious grain with significantly different spectra from their leaves.

In the future study, we will try to apply this approach to satellite data and in other crops. In this study, we developed an approach to improve the estimation of rice yield at heading stage using UAV-based Vegetation Index and abundance data. Compared with booting stage, a relatively weaker relationship between VI and rice yield was found at heading stage. The reason was the uneven emergence of rice panicle at heading stage which caused the decrease of predictive ability of VI for rice yield.

In order to improve the accuracy of yield estimation at heading stage, a fully constrained least squares linear spectral mixture method was used to eliminate the influence of the panicle appearance on yield estimation. The abundance images of six mainly endmembers in paddy field was produced based on the six-band UAV image and ground measured spectra, including top layer leaf, bottom layer leaf, top layer panicle, bottom layer panicle, dry soil, and wet soil.

The integration of plot-level VI and abundance information can estimate rice yield more accurately than using VI alone. SF conceived of the research ideas and built the infrastructure for the study site to make this research possible. SW provided rice yield and advised on data collection. YG and YP designed the experiments in detail and provided valuable guidance on data analysis. RZ and XW provided important insights and suggestions on this research from the perspective of agronomists. BD performed the majority of the data processing and provided the writing of this paper.

All authors read and approved the final manuscript and significant contributions to this manuscript. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Aasen, H. Multi-temporal high-resolution imaging spectroscopy with hyperspectral 2D imagers — From theory to application. Remote Sens. Becker-Reshef, I. Behrens, T. Utilization of canopy reflectance to predict properties of oilseed rape Brassica napus L. Bioucas-Dias, J. Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches. IEEE J. Earth Obs. Broge, N. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density.

Candiago, S. Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Dash, J. Dwyer, J. Effects of empirical versus model-based reflectance calibration on automated analysis of imaging spectrometer data: a case study from the drum mountains, Utah.

Google Scholar. Fang, S. Remote estimation of vegetation fraction and flower fraction in oilseed rape with unmanned aerial vehicle data. Feret, J. Fielding, A. Conserv 24, 38— Kohavi, R. The power of decision tables. Berlin: Springer, — Gausman, H.

Reflectance of cotton leaves and their structure 1. Gholizadeh, H. Gitelson, A. Novel algorithms for remote estimation of vegetation fraction. Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Remote estimation of canopy chlorophyll content in crops. Glenn, F. Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index—the canopy chlorophyll content index CCCI.

Field Crops Res. Gong, Y. Remote estimation of rapeseed yield with unmanned aerial vehicle UAV imaging and spectral mixture analysis. Plant Methods 14, Hansen, P. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Harrell, D. Estimating rice grain yield potential using normalized difference vegetation index. Hatfield, J. Application of spectral remote sensing for agronomic decisions.

Heinz, D. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery. IEEE Trans. Holzman, M. Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index. Jhan, J. Jiang, Z. Development of a two-band enhanced vegetation index without a blue band. Jordan, C. Derivation of leaf-area index from quality of light on the forest floor. Ecology 50, — Kira, O. Non-destructive estimation of foliar chlorophyll and carotenoid contents: focus on informative spectral bands.

Laliberte, A. Multispectral remote sensing from unmanned aircraft: Image processing workflows and applications for rangeland environments. Li, F. Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices. Liu, H. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. Liu, X. Canopy chlorophyll density based index for estimating nitrogen status and predicting grain yield in rice.

Plant Sci. Lobell, D. Peng, Y. Pu, H. Constrained least squares algorithms for nonlinear unmixing of hyperspectral imagery. Rahman, A. Use of remote sensing data for estimation of aman rice yield. Ray, S. Defining optimum spectral narrow bands and bandwidths for agricultural applications.

Rouse, J. NASA Spec. Sakamoto, T. Assessment of digital camera-derived vegetation indices in quantitative monitoring of seasonal rice growth. Somers, B. Endmember variability in spectral mixture analysis: a review. Sun, H. The spectral characteristics and chlorophyll content at winter wheat growth stages.

PubMed Abstract Google Scholar. Sun, L. Thenkabail, P. Hyperspectral Remote Sensing of Vegetation. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Tucker, C. III Relationship of spectral data to grain yield variation. Turner, D. Spatial co-registration of ultrahigh resolution visible, multispectral and thermal images acquired with a micro-UAV over Antarctic moss beds. Verrelst, J. Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties — a review.

Comparison of different vegetation indices for the remote assessment of green leaf area index of crops. Woolley, J. Reflectance and transmittance of light by leaves. Plant Physiol. Wu, C. As revealed by the result of the HD growth model, the average amount of credit per season that farmers had access to was, 38, As unravelled by the result of the study, it can thus be concluded that technical efficiency of rice farmers can be improved by ameliorating access to timely credit and agricultural information for improving rice productivity.

These findings suggest that filling the financing gap of smallholder rice farmers will improve rice productivity in Nigeria. Rice is one of the most valuable cereal crops cultivated and consumed all over the world. It is a staple food in several African counties, Nigeria as an example and constitutes a large portion of the diet on a regular basis Lu et al. Rice is cultivated in mostly all agro-ecological zones in Nigeria but on a relatively small scale.

Farm productivity of staple crops, in developing nations such as Nigeria, is low due to traditional methods of farming, poor irrigation facilities, land fragmentation, the impact of climate change, misuse of modern agricultural technology, and less availability of credit Chandio et al.

Among the staple crops, rice has risen to a position of eminence in Nigeria. Rice is the most important staple food for about half of the human race Akinbode Nigeria still ranks third with Iraq after the Philippines and China in the group of major rice importing countries in the world. Rice Oryza spp. It has the second highest production worldwide, after maize Mohanty et al.

Rice is an important crop that has allured several studies in Nigeria. Some studies had focused on adoption of improved rice variety Awotide et al. According to Ogundele and Okoruwa , efficiency measurement is imperative as success indicator and performance measure by which production units are evaluated, as well as an avenue to identify sources of production inefficiency.

According to Fakayode where inadequate funds was considered as the greatest challenge limiting rice production, flooding was also considered as a challenge limiting rice production especially the upland smallholder rice farmers as found in Southwestern, Nigeria.

As argued by Guirkinger and Boucher , the significant adverse effects of credit constraints on farm productivity of smallholder farmers in the rural areas of developing countries such as Nigeria are alarming. Olomola and Gyimah-Brempong attributed the low productivity in the agricultural sector to the subsistence nature of agriculture and lack of credit availability.

However, the influences of financing on technical efficiency of smallholder rice farmers have been given very little attention, which accordingly is the focus of this study. This study primarily focuses on assessing the financing gaps relative to production frontier of smallholder rice farmers in Southwestern Nigeria.

It also investigates the socio-demographic factors that influence inefficiency in agricultural production among rice farmers. As a caveat for this study, the technical efficiency of rice smallholder rice farmers is estimated and an adapted form of the Harold—Domar HD growth model was employed to estimate the financing credit gap of smallholder rice farmers in Southwestern Nigeria. As posited by Easterly , and recently applied by Tang et al.

The usual ICOR formulation determines investment requirements for a given growth target. Easterly noted that the model has two important features viz. He further noted that A and B imply the following testable assumptions: 1 aid will go into investment one for one, and 2 there will be a fixed linear relationship between growth and investment in the short run. The constant of proportionality is one over the ICOR. The shortcomings of the Harrod—Domar approach are well noted in the study of Hussain These, he stated, centre on two closely related problems.

The first is the inaccuracy of estimating the resource gap to achieve a target rate of growth and the second is the failure of the basic Harrod—Domar relationship to predict growth rates. With regard to the former, he noted that if the economy is working below capacity, which is typical in most developing countries such as Nigeria, the true value of the ICOR cannot be computed with any degree of precision, and definitely not with the precision suggested by the equations.

Also, he noted that the Harrod—Domar approach assumes that all additional growth in income is attributed to the increments of capital. The approach overstates the productivity of capital and understates the ICOR based on the fact that other factors contribute to growth. However, Geda et al.

They noted that this distinction is very important in application because it is about an economy reaching its equilibrium or steady state over a certain period of time, or to be specific, zero per capita growth or GDP growing at the rate of population growth. It allows a check on consistency across the macroeconomic balances as well as sectoral investment programmes. They finally concluded that HD may continue to be relevant when time and resources are limited. In analysing the empirical validity of HD in the African context, Easterly found no empirical basis to support the 44 predictions of the HD in over countries for the — period.

In the same vein, Bermejo Carbonell and Werner also found that the Spanish EU and euro entry have had no positive effect on growth. The findings call for a fundamental rethinking of methodology in economics. Setting aside issues of model specification and others, they attempted to re-examine these relationships for a sample of 12 African countries and their results actually suggested a strong support for HD predictions with the exception of two countries.

They found significant relationships between growth and investment for the 10 countries when a constant is added in the OLS regression. They noted that this is because the HD model assumes no constant term in the relationship between growth and investment proportionality and that once they imposed a zero constant on the regressions, it turned out that all countries exhibit a strong and positive short-term relationship between investment and growth.

They also found the relationship between aid and investment to be positive, and in most cases, significant. Although they agreed with the argument that HD ignores diminishing returns to aid, they however stated that the existence of diminishing returns implies that the straightforward HD projections will underestimate the actual resource requirements. In summary, Geda et al. They noted that any of these methodologies has its own limitations in relation to empirical application to country-specific and context-specific circumstances.

However, they affirmed that estimates generated from simple models like the HD turn out to be very consistent with estimates generated by more sophisticated methodologies. For this study, a stochastic frontier analysis SFA framework was used to assess the technical efficiency of rice production in the study area.

The basic stochastic frontier production function of rice production can be expressed as;. In line with the frontier production function as specified in Eq. The value of 1 indicates a fully technically efficient farm and the value of 0 implies a fully technically inefficient farm.

The Cobb—Douglas production function model used to represent the production of rice is specified as. For this study, four main hypotheses were tested, viz; i. The results of the four hypotheses were tested using the generalized likelihood-ratio test statistic specified as:. However, if the null hypothesis is accepted, then the asymptotic distribution involves a mixed Chi-square distribution. The results of the four null hypotheses tested are presented in Table 2.

According to Geda et al. However, in order to place all the producers on a desirable efficiency level growth rate and cater for the issue of efficient use of investment, the growth rate in the HD model is substituted with the production frontier. Thus, this study is based on the assumption that: credit amounts required by rice farmers to produce at the frontier level are directly proportional to the production frontier by a constant known as the Incremental Capital Output Ratio ICOR.

The ICOR is hypothesized to be a measure of the inefficiency with which credit is used. The adapted H—D model is thus hinged on the condition that the credit is used for the purpose of rice production. As posited by Bifarin et al.

Hence, the expected impact of such funds will not be felt on the farm. If, however, the credit is invested in consumption purpose as peculiar to smallholder farmers, credit will likely not lead to an improvement in the efficiency level. The climate of South-West Nigeria is tropical in nature and characterized by wet and dry seasons.

The wet season is associated with the southwestern monsoon wind from the Atlantic Ocean, while the dry season is associated with the northeastern trade wind from the Sahara Desert. The vegetation in South-West Nigeria is made up of fresh water swamp and mangrove forest at the belt, the low land in forest stretching inland to the Ogun and part of the Ondo states, with the secondary forest stretching towards the northern boundary by the derived and southern Guinea savannas Agboola A multistage sampling technique was used to select the respondents for the study from June to July, The first stage involved a typical case-purposive selection of three states, Ekiti, Ondo and Osun States located in the same agro-ecological area as shown in Fig 1.

In the second stage, four local government areas LGAs were then selected from each state, based on the predominance of smallholder rice farmers in these areas, using a typical case-purposive sampling. In the third stage, five villages were randomly selected from each of the four LGAs. Following Tesfahunegn et al. Data were collected by means of a pre-tested, well-structured questionnaire by trained and experienced enumerators who have good knowledge of the farming systems and speak the local language in collaboration with the Agricultural Development Programme ADP agents in each state.

The descriptive statistics of the surveyed rice farmers are presented in Table 1. The average farming experience of the farmers in the study area is 15 years. The result is in agreement with Hitayezu et al. The result of the null hypothesis for the model is presented in Table 2. The null hypothesis of the frontier model was tested to ascertain the non-existence of technical inefficiency in the frontier of rice production in the study area.

The null hypothesis was rejected as indicated by the P -value. This implies that the average response model does not fit the data well, as posited by the assumption of the stochastic frontier analysis model. As regards the functional form for the frontier model, Cobb—Douglas production function was chosen as the appropriate model as the model failed to reject the null hypothesis. Finally, the null hypothesis of homoscedasticity in both the stochastic and inefficiency variance of the error terms was not rejected, suggesting that the model is homoscedastic.

The maximum likelihood estimates of the parameters of the Cobb—Douglas stochastic frontier production functions are presented in Table 3. As suggested by Coelli et al. All estimated coefficients in the Cobb—Douglas model fall between zero and one, satisfying the monotonicity condition that all marginal products are positive and diminishing at the mean of inputs. These results are consistent with the estimates of Abdulai and Abdulahi who also found positive and significant effects of frontier variables on output of maize farmers in Zambia.

The sum of first-order estimates of the production inputs which are referred to as the scale elasticity reveals decreasing returns to scale in the frontier model sum up to 0. The implication of the results shows that increasing all inputs by a certain proportion would result in a less than proportionate increase in output of the smallholder rice farmers in Nigeria.

This could be attributed to the fact that scale inefficiency among farmers in developing countries, estimates of decreasing returns to scale seem consistent with expectation as agricultural production commonly exhibits decreasing returns to scale Abdul-Rahaman ; Khanal et al. The coefficient of labour as measured in man-day is positive and statistically significant in increasing the rice output. In line with Hazell et al. The implication of the result shows that rice output increases as the quantity of labour is increases.

The plausible implication of the significance of labour for rice output is not unexpected since smallholder farmers rely heavily on manual labour with farming operations in developing countries such as Nigeria are resource-constrained. This finding is in line with the study conducted by Mensah and Brummer who reported an increasing effect of labour supply on the output of mango producers in some selected regions in Ghana.

Huy and Nguyen also found an increasing effect of labour in their study on cropland rental market and farm technical efficiency in rural Vietnam. Weeds remain a major challenge to increasing crop output as they compete with the crop plants for nutrients and water among others.

The coefficient of herbicides is negative and statistically significant in reducing the productivity of a rice in the study area. The negative and significant coefficient of the value of herbicides indicates an inverted U-shaped response function. The implication of the results shows that a continuous increase in the quantity of herbicides while the value would at a point decrease rice yield. This indicates that, after a certain point in the production process, a higher quantity of herbicides is not beneficial in increasing rice productivity.

Another plausible explanation could be over-application, inappropriate use or application of unapproved herbicides which subsequently increases input cost that reduces expenditures on other inputs without positive contribution to the productivity of rice Danso-Abbeam and Baiyegunhi This stage of negative contribution of herbicides to the productivity of rice production is marked as the irrational stage stage III of production.

The coefficient of quantity of seed planted was positive and statistically significant in increasing the efficiency of rice production in the study area. This result corroborates the study of Ogundari who also found an increasing effect of quantity of rice planted on rice output in his study on the resource-productivity, allocative efficiency and determinants of technical inefficiency of rainfed rice farmers in Nigeria.

The results show that the age of the rice farmer exerts a positive significant effect on inefficiency of rice farming in Nigeria. This implies that as the age of smallholder rice farmers increases, the level of inefficiency also increases. This is expected as relatively, the positive sign for age indicates that older farmers are less efficient as against the young farmers who energetic and would also want to take risk of trying innovation in farming practices which may increase their production efficiency Alwarritzi et al.

This finding is in line with the study of Villano and Flemming , suggesting that self-satisfaction among relatively old farmers has the propensity to decrease their probability of adopting new farming practices, therefore, lowering their productive efficiency level. This result implies that male farmers tend to be less efficient compared to their female counterparts.

Further, the number of years of experience in rice production was expected to reduce technical inefficiency. Result of this study shows that farming experience positive and statistically significant in increasing the technical inefficiency of smallholder rice farmers in the study area.

This could be attributed to the conventional nature of some experienced farmers. Some farmers are so satisfied with their rudimentary method of farming such that they find it difficult to switch to new farming practices, hence, reduce productive efficiency. This finding is in consonance with Danso-Abbeam and Baiyegunhi who also found a negative relationship between farming experience and technical efficiency among cocoa farmers in Brong-Ahafo region of Ghana.

Conversely, Khanal et al. This implies that the technical inefficiency of the respondents decreases as the household size increases. The plausible explanation for this could be attributed to the ability of the household to supply surplus family labour as argued by Gautam and Andersen As posited by Ahmed and Melesse , household size is an indicator of labour availability as measured in terms of adult equivalent.

A large family size implies the availability of labour by a family who can actively engage in farming activities and facilitate the adoption of adaptation measures against climate change effects Uddin et al. According to Alfred and Xiao and supported by Quaye et al. Easing potential credit constraints through the timely granting of credit reduces the opportunity costs of some capital-intensive climate change adaptation strategies Binam et al.

A negative and statistically significant relationship found between access to credit and technical inefficiency implies that overcoming credit constraints is likely to enhance the productive efficiency of smallholder rice farmers in South-west, Nigeria. The significant coefficient for credit indicates that access to enough and timely credit is a significant factor in bridging the financing gap and ultimately improves agricultural productivity. These results are in agreement with the findings of Chandio et al.

It is also in line with the findings of Bozoglu and Ceyhan who posited that credit use increased technical efficiency among vegetable farmers in Samsun province, Turkey. As argued by Abdulai and Abdulai , visits by extension agents to the famers were used to account for access to information from institutional sources.

Access to extension is measured as whether farmer had contact with an extension agent on the production methods within the past three production seasons. The coefficient of access to information is negative and statistically significant in reducing inefficiency of rice production. This implies that access to information from extension agents and other sources of information improves the efficiency of rice production in Nigeria.

This is in consonance with the study of Donkor et al. Access to information about agricultural related activities would improve the productivity of farmers Khanal et al. The variable representing access to climate change information is negative and statistically significant with inefficiency in rice production.

This implies that farmers with better access to information are more efficient as compared with others with inadequate access to information. The smallholder rice farmers with better access to agricultural information were more progressive and therefore exhibited greater efficiency. This is in agreement with the findings of Bhatt and Bhat and Dessale et al.

The location dummies are included to capture managerial and environmental differences among farms located in different states Danso-Abbeam and Baiyegunhi Location variable is expected to have an impact on technical efficiency of rice farmers in the South-west, Nigeria. It is assumed that farmers located in the same region apply similar managerial techniques due to their proximity and are have a similar physical environment, soil quality.

The coefficients for the district dummies for the farmers located in Osun and Ondo States are negatively signed and statistically significant in reducing inefficiency in rice production. The negative sign indicates that smallholder rice farmers located in both Osun and Ondo exhibit higher efficiencies in rice production.

Remote sensing RSas an emerging technology, is expected to be useful for rice yield estimation especially at regional scales.

Essays definition fear In this investigation, the data from 24 resume english translator example plots was studied — Figure 1A. Assessment of digital camera-derived vegetation indices in quantitative monitoring of seasonal rice growth. As shown in Figure 7significant differences existed in abundance images of different endmembers. VI, derived from the spectra of such mixed pixels, may encompass the unexpected information of the components not related to yield, which could lower the precision of research papers on rice crop estimation estimation. Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation. The negative sign indicates that smallholder rice farmers located in both Osun and Ondo exhibit higher efficiencies in rice production. Gausman, H.
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Research papers on rice crop estimation 347

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Since the 's the government invested heavily on massive infrastructure development in the 8 granaries. Fertilizer subsidy, support price and price subsidy are offered to rice farmers by the government to ensure a good yield and sufficient and consistent income for the farmers, especially the lower income group. The farmer also collects a price subsidy of RM The farmer thus receives RM Payment is given after the rice is sampled for impurities. Their rice is marketed by their Nor Lelawati Jamaludin et.

BERNAS is not only involved in procuring, processing, importing, trading and distributing rice, it also handles public funds for subsidies of rice production. This renders unfair rice imports that are not characteristics of free trade. Consumers commonly buy rice at prices between RM1. The prices are commonly matched with cheaper imports.

Farmers thus have no influence on the market lines or the pricing of rice in Malaysia. The farm gate price is fixed and the government closely regulates retail prices. Also, a survey of 33 farmers from 3 different villages in the Seberang Perak in the state of Perak showed that there is a trend towards a larger difference between Gross and Net Income among rice farmers.

This results in a decline in effective profits from rice farming for the average farmer. The average gap between the gross and net income among farmers in the study area became more significant in - periodv. The majority of rice farmers are heavily dependent on support prices, input subsidy, and government intervention in marketing and helpful rates of deductions. Farmers are not yet seriously concerned with the implications of AFTA, where they would no longer be heavily protected.

The government may 'protect' the industry even after , although efforts are being made to liberalize the industry. Recent generous rice imports from neighboring countries caused the domestic stockpile to exceed set levels, leading to protest from local producers. This indicates the volatility of the domestic rice market, both in terms of dependency and in managing stock levels. The government of Malaysia is very protective of the rice industry. It bears a major proportion of the cost of production, by spending a substantial amount of money on subsidies.

This may appear to be beneficial to farmers in assuring a sizeable profit margin, consistent income and assurance of farm gate price. But since the production of rice is not competitive due to high production costs and low yield, the sustainability of such a structure with the advent of AFTA is questionable. However consumer prices wholesale and retail prices are also maintained low through price control measures, therefore there are no complaints on pricing from the consumers.

Nor Lelawati Jamaludin et. Apart from being the main source of food, the industry also provides the main livelihood to about , farmers of whom , are exclusively rice farmers. Utilitization of land for rice production is currently close to , hectares, mainly by small- scale farmers. Thus, heavy investments were committed to modernize the industry with a view to increasing productivity and efficiency in the production of rice and therefore the income of the farmers.

Public investments were directed mainly at improving the physical infrastructure, such as roads and drainage system, provision of production cost subsidies, such as fertilizer, seedlings, etc to increase yields, and the adoption of multiple cropping annually. Marketing institutions were established to enforce minimum price policy for rice. As part of the scheme, a price control mechanism was put in place so as to ensure that rice is affordable to every citizen.

Thus, the rice industry is a highly regulated Nor Lelawati Jamaludin et. The industry is very dear to the heart of the government. Public policy with regard to the industry was aimed at ensuring a minimum self-sufficiency of 65 per cent; to increase the production of higher quality, specialty and fragrant rice; and to maintain strategic quantity of rice stockpile.

These objectives justify direct government intervention in the industry and the heavy investment committed. The achievement of these objectives was reflected in the formulation and implementation of relevant policies and strategies. Designating paddy producing areas is one of the major strategies whereby the eight granary areas are designated as permanent paddy producing areas, to realize a minimum self-sufficiency level for rice of 65 per cent.

Currently these areas cover only 36 per cent of the total physical paddy land but constitute 57 per cent of the total area planted, and produce 72 per cent of the total national rice production. Another strategy is the identification of suitable areas especially in Sabah and Sarawak for large-scale commercial paddy production by the private sector.

In Sarawak, in particular, there is a great potential for developing the industry by virtue of the fact that the current production contributes roughly 35 per cent self- sufficiency level as against the 70 per cent targeted by the state government. This was mainly due to the traditional method of paddy planting of local varieties such as Biris, Kalas, Wangi, Bario, Rotan and Lemak, which are mostly of the long life cycle, requiring months for the grain to ripen for harvest.

The sea change for the rice industry began in the s when scientists succeeded in producing a miracle rice variety in the form of IR 8. This finding has become the catalyst for the green revolution in rice. It was further improved into another variety IR 36 which has the ability to withstand a wide range of pests, setting a world record for the only single food crop planted on This entire scenario represents a challenge to Malaysia, considering the fact that low cost producing countries are the major competitors for its highly regulated rice industry.

Vokes In Asian countries, three basic types of rural industries can be identified namely; i primary processing industries agro-processing, livestock and poultry, forestry, fisheries, mining and quarry products ; ii agro-input industries; and iii rural consumer goods industries.

Within this grouping, agro-processing industries are normally the most important. For example, in the case of West Malaysia, agricultural processing accounted for almost 37 percent of rural employment in Although the milling of paddy for market is frequently dominated by large-scale mills, there is many thousands of small-scale rice mills located throughout the paddy growing areas of Asia, which undertake to mill paddy retained by farmers for their own consumption, as well as often engaging in commercial milling.

Malaysia is no exception in this respect. There has been a very rapid growth in the numbers of small rice mills since the s. The establishment of such mills at this time was encouraged by the Co-operative Department of the Ministry of Agriculture, with finance coming from the co-operative movement's own bank and with the management and administration of the mills vested in representatives of the membership of the individual co-operatives.

While no special assistance or training appears to have been given to the managers of such co-operative mills, their operations proved profitable, largely because they proved very popular among farming families, since they provided a release from the drudgery of the traditional method of husking paddy using hand or foot operated pounders.

However, the profitability of these early co-operative ventures attracted private capital into the small rice mill sector. By private mills outnumbered co-operative mills by approximately 3: 1 in the major paddy producing states Perlis, Kedah, Perak, Penang, Selangor, Kelantan while the total number of service mills in the country at that time was somewhere in excess of 1, Calculated from U.

However, what is particularly significant in the context of present government policy is that the majority of owners and managers of small rice mills SRMs are Malay. This is in marked contrast to the privately owned large rice mills LRMs which are almost exclusively Chinese owned. Out of the SRMs in existence in the writer has estimated that over 70 percent were either owned or operated by Malays. Not all of the mills that have been set up have been operated efficiently and profitably, because of excess capacity and poor management.

As a result, many mills have closed down. However, at the same time new units are being set up, while many of the existing units continue to operate effectively, and have, at least until recently, competed in commercial milling with large rice mills. It is significant that in the case of the private mills only one, which was Malay owned, reported receiving any assistance from the various agencies set up to help small businesses. Indeed, one of the Malay owners even indicated that it was difficult to get assistance from such bodies.

However, only three of the owners, all Malay, indicated that lack of capital had been a problem at the time of setting up the mill. Six of the owners indicated that milling operations were currently less profitable than before, due to the growth in the numbers of SRMs and the resultant competition for paddy supplies. The other major problem experienced in running their mills, reported by four of the owners, was the difficulty of obtaining laborers.

This would appear to be a somewhat unexpected problem. However, it is now widely accepted that unskilled labor is generally in short supply in this region of Malaysia. This is primarily due to out migration to Nor Lelawati Jamaludin et. In spite of these problems, 12 of the private mill owners and three of the co-operative managers, reported plans for further investment. Half of the 12 indicated plans for further investment in plant and machinery, with one of the Chinese owners indicating plans to purchase an artificial drier.

Field was prepared with a tractor drawn plough followed by puddling and laddering in kharif season for transplanting of rice. For all dry season crops, field was prepared by dry ploughing and seeds were sown by hand. Rain was sufficient to fulfill the water needs in kharif season; therefore no irrigation was applied to rice. Rabi crops were irrigated with ground water whenever necessary, with about 50 mm water per irrigation. Insect pests and diseases infestation was below economic threshold except in toria.

Chemical protection measures were taken against bacterial leaf blight in rice and aphids in toria during both the years. After the rice seedlings were transplanted to the field, the numbers of tiller in10 fixed hills from each treatment were surveyed at 3-days intervals from 21 to 60 days in Naveen and 21 to 68 days in Swarna and Gayatri. Height of 5 randomly selected plants from the net plot was measured from the soil surface to the tip of the tallest leaf.

However, after heading the height was measured upto the tip of rice panicle. The growth stage of rice i. From the day of tiller emergence, the tillers on tagged plants were counted every alternate day. For observing, PI stage, mother tiller was uprooted and a transverse cut was made at the base of the plant, a thread like appearance was seen with magnifying glass.

This was observed on each alternate day for recording the PI stage. From the biomass of periodical interval crop growth rate CGR and relative growth rate RGR was calculated Where w 1 and w 2 represent the dry matter weight of rice plants as measured during the first and second times, respectively, and the difference between t 2 and t 1 is the time interval of the two measurements.

Based on the parameters that were explicated above, the dry matter remobilization amount DMRA , dry matter remobilization efficiency DMRE and dry matter conversion rate DMCR , were calculated according to the following equations [ 14 ]. At maturity, ten plants of each treatment were randomly selected to measure yield attributes excluding the border plants.

Number of panicles were counted from each treatment, and five panicles per plant were randomly selected for measuring panicle length, panicle weight, number of spikelets per panicle, spikelet fertility percentage proportion of filled and chaff grains and grain weight was determined. The rice crop was harvested and sun dried for 3 days, then total produce was weighed and recorded as total biomass. Similarly, yield of non-rice crops was also recorded. Prices of individual inputs and outputs were assumed to be stable during the experimental period.

For calculating system productivity, rice yield of wet season and rice equivalent yield of dry season crops were summed up and expressed as kg ha The variable cost of cultivation of rice and other crops included cost involved in different operations eg. The cost of cultivation was kept same for both July and August sown crops as the inputs used are same. The economic analysis, however, does not include the value of land.

Gross return was calculated by multiplying grain and straw yield with the price of grain and straw. Minimum support price MSP fixed by the government of rice Rs. Net returns, B: C ratio, land use efficiency, production efficiency and economic efficiency were calculated by the following formulas:. The data for all the parameters were analyzed by using SAS version 9. Association between different parameters were studied using correlation and linear regression analysis. Statistical significance was set at an alpha level of 0.

Means were compared by the least significant difference LSD test if the f-value was significant. The meteorological data showed a marked variation in weather conditions during the two years of the experiment. Maximum and minimum temperatures, evaporation, and sunshine hours remained almost constant during both the years, whereas, rainfall varied significantly.

Rice and dry season crops received and mm rainfall during —13 and —14, respectively against average of mm rainfall during last five years Fig 2. In the year —14, cyclone Phailin also affected the crop growth. Mean evaporation and sunshine hours in both the years was 5. The pattern of rainfall also varied during both the years.

In —13, June to September, whereas, in —14, it was only Fig 2c. Average rainfall mm received during last five years from —08 to —12 before initiation of the experiment. Vertical bars on the line represent standard deviation of the five years.

The dynamics of occurrence of tillers in all the cultivars showed a distinct pattern, duration of cultivars and time of transplanting significantly influenced the tiller occurrence Fig 3. Long duration cultivars showed higher number of tillers but commencement of maximum tillering stage was comparatively late compared to short duration varieties. Apart from that, timely transplanting in July significantly resulted in higher number of tillers per hill whereas, August transplanting decrease the tiller number up to However, number of tillers per hill was lower in —14 compared to preceding years.

Overall performance of the crops was better during The dry weight of above ground parts of rice was greater in long-duration varieties and rice transplanted in July yielded higher biomass compared to august transplanted rice during both the years Fig 4.

On an average, July transplanted rice produced Dry matter remobilization amount, efficiency, and contribution to the grain yield were highest for Gayatri followed by Swarna during both the years Table 1. However, the amount, efficiency, and contribution were relatively low in the succeeding years, irrespective of transplanting date or cultivars. October sown Annada crop had significantly lowest remobilization of dry matter either from leaf, stem, or panicle during both flowering and maturity stage.

Similar to biomass accumulation, crop growth and relative growth rate was greater in —13 compared to — Initial growth was slow in all the rice cultivars, in Naveen, highest growth rate was observed during 60—90 DAS whereas, in Swarna and Gayatri, it was higher during 90— DAS. During —14, crop growth of July sown crops was better during 60—90 DAS but after that growth was considerably decreased Fig 5.

As per relative growth rate concerns, in short duration cultivars, it was relatively higher in 30—60 DAS whereas, in long duration cultivars, it was higher or at par in 60—90 DAS during both the years Fig 6. The tillering, panicle initiation PI , booting, panicle emergence PE , flowering, and maturity stages of different rice cultivars were accounted in the experiment and it was found that late transplanting led to commencement of growth stages earlier than normal transplanting time during both the years.

On an average, tillering, PI, and flowering stages occurred 7d, 6. Production of yield attributes was influenced by transplanting dates, although varietal difference was also evident Table 3. Rice cultivar Gayatri produced highest number of effective tillers, spikelets per panicle, and panicle weight followed by Swarna.

Naveen and Swarna transplanted in July had significant differences in the production of tillers, panicles, spikelets, and fertility percentage over their transplanting in august. Effect of year was also significant which may be due to changing weather conditions.

Production of all the yield attributes was decreased during —14 except august transplanted Swarna in which it was increased. The difference in plant height and grain weight was not significant. The most important parameter i. Gayatri cultivars produced maximum rice yield 5. Rice yield averaged across all the genotypes maximum 4. Both the years of experimentation varied among themselves with respect to grain yield, and the yield was decreased in —14 with more effect on Annada and Gayatri cultivars Table 4.

A positive correlation among grain yield, biomass and crop growth rate Fig 8 and between biomass and days after sowing Fig 9a supported the findings. Crop yields of non-rice crops in the individual cropping systems varied by climatic seasons and within the same climatic season, crop yields were much affected by the previous season rice cultivar grown. During both the years, seed yield of toria was significantly highest among all the dry season crops except when sown after Swarna August in —13 and Gayatri July in — Among all the crops, coriander recorded significantly lowest seed yield irrespective of the rice cultivar followed and years Table 4.

Overall, seed yield of dry season crops was higher in —13 compared to —14 except yield of green gram sown after Swarna July and toria sown after Swarna August. Among crops, grown after July transplanted rice, toria proved its superiority in terms of seed yield followed by green gram.

The performance of dry season crops was better when sown after Naveen except coriander irrespective of the time of transplanting. Rice equivalent yield REY of dry-season crops was higher after Naveen, and July transplanting of rice cultivars was more favorable. Climatic conditions of —14 years was affected the production of dry-season crops along with rice Table 4. Regardless of the cultivar followed, REY of green gram was highest when followed by July transplanted rice, whereas, REY of blackgram was significantly highest when grown after August transplanted rice and damaged Swarna during both the years of experimentation.

Black gram contributed to highest REY sown after damaged Swarna crop and late sown rice crops, it can be an option where crop failure can be minimized by sowing such crops. Although yield of toria was highest among all the dry season crops but its REY was lower than green gram and black gram. Similar to seed yield, REY of coriander was lowest among all the dry season crops irrespective of the cultivar followed and time of transplanting.

In this treatment, after rice, the seed yield of toria was highest during both the years followed by black gram but the REY was highest for black gram followed by green gram during both the years. Both system productivity and annual net return must be considered for choosing suitable cropping systems under changing climatic scenario Table 5. Among the cropping systems, Gayatri based cropping systems were more productive and profitable under normal weather conditions of —13 followed by Swarna based systems but when climate changes as in —14 the Swarna based cropping system were more profitable followed by Gayatri especially when transplanted in July.

In August transplanting, Naveen based systems performed better during both the years. System productivity was highest for R G -GG 6. The results were supported by a positive correlation between net returns and system productivity during both the years Fig 9b. Apart from rice crops, different dry season crops also contributed to the system productivity and profitability. Green gram contributed to the highest system productivity during both the years and productivity remained low where coriander was grown.

This was also reflected in lower net return and B: C ratio of cropping system having coriander crop. The difference in productivity of green gram and horse gram was higher but the gainin net returns was not much visible, this may be due to the lower cost of production involved in horse gram.

However, production and economic efficiency were significantly highest for Naveen either sown in July or August during both the years. Long duration cultivar had higher land use efficiency as compared to Naveen, which was around Early transplanting of rice crops led to better productivity of subsequent crops leading to highest profitability compared to late transplanted rice. The performance of all the crops in the cropping system in terms of their grain and biomass production varied considerably between cultivars and years.

Climate change will have varying impacts on cropping systems around the world, due to regional differences in rates of daytime and nighttime warming, changes to the timing, frequency, and intensity of P, and exposure to O 3 and air pollution sources. About Crop diversification has often been examined as a tool to stabilize crop revenue and farm income [ 17 — 18 ]. The exceptional rainfall in October, provided circumstantial evidence with regard to difference in productivity among both the years.

Long duration rice cultivars were strongly affected, and the crops following them were also affected. It is clear that rainfall around the sowing period is a major factor determining the water contents of the soil and hence the success of crop establishment. In rice, leaf areas, leaf shapes to maximize photosynthetic efficiency, leaf area index LAI at flowering and crop growth rate CGR during panicle initiation well-developed root systems, have been identified as the major determinants of yield [ 19 ].

Apart from climate, among the crop production tools, proper time and method of sowing are the prerequisites that allow the crops to complete its life phase timely and successfully under a specific agro-ecology. Among the different components of agronomic packages for rice cultivation, the date of transplanting is one of the important factors as early or late transplanting may face different types of abiotic stress [ 20 ].

It is also crucial for successful dry season cropping following rice especially if conditions are dependent on rains,particularly at the end of the rainy season or the beginning of the dry season, the temporal variability within each site associated with rainfall could mask this trend. The optimum window for sowing is generally rather narrow, and will be determined by the interaction between crop growth and the prevailing environmental conditions.

For successful rice production, timely planting, suitable transplanting densities and proper water, fertilizer and pest management are essential for improving the growth variables responsible for high yield [ 21 ]. In this study timely planting July resulted in higher biomass accumulation, yield and productivity. The superiority of this planting date can be explained by the ideal temperature, rainfall and higher dry matter allocation to the panicle and the extension of this process until the harvest time.

Planting date is more dependent on climatic conditions compared to other aspects of agricultural management. In certain periods of rice growth, dry matter accumulation in plant is larger than its consumption level for growth. In this state, excess photosynthetic materials are mainly gathered in the stem, and in the later stages of growth, which normally starts 2 or 3 weeks after the flowering stage, they are transferred to the grain via the remobilization process [ 22 — 23 ].

One of the effective factors on the remobilization rate is the source to sink ratio; the high and low levels of this ratio would result in the increase and decrease of remobilization respectively [ 24 ]. According to Yang et al. According to Kirchhof et al. Phenological growth stages of rice were also varied with time, because rice plants required a particular temperature for its phonological affairs such as panicle initiation and exertion, flowering, which may be influenced by the planting dates [ 26 ].

Deviation from the planting time may cause incomplete and irregular panicle exertion, and increased spikelet sterility [ 27 ] which resulted in yield reduction. Among yield components, productive tillers are very important because the final yield is mainly a function of the number of panicles bearing tillers per unit area. Prevailing low temperature is not favorable for the elongation of the tillers [ 28 ], which may also affect the panicle initiation process resulting in low number of panicle per hill leading to low yield, as grain yield is a function of interplay of various yield components such as number of productive tillers, spikelets per panicle and grain weight [ 29 ].

Net returns were directly related to the system productivity and the production cost, which may depend on the price that producer received for the product. Production cost of dry season crops was lower due to its low labour and less land preparation requirement which led to higher net return, B: C ratio and economic efficiency of the system.

Singh et al. According to Rashid et al. Samant [ 32 ] also found that the lowest production efficiency was found in rice-fallow system due to lower yield in rice and maximum land use efficiency was observed in rice-groundnut cropping system followed by rice-brinjal with greater combined yield.

Rice-fallow, had given relatively lower yield due to its longer duration with less return [ 33 ]. According to Davis [ 34 ], more diverse cropping systems can use small amounts of inputs as powerful tools with which to tune, rather than drive, agroecosystem performance, while meeting or exceeding the performance of less diverse systems. The study amply demonstrated the potentiality of growing atleast two crops in rainfed rice ecosystem utilizing residual soil moisture for higher productivity and profitability of rainfed rice based cropping systems.

The system productivity of the systems after inclusion of one dry crop has been enhanced up to 6—6. Further, it can be concluded that transplanting time significantly affected crop growth, remobilization of photosynthates and yield of rice and following dry season crops mainly due to prevailing weather conditions.

Transplanting during July month is most suitable for obtaining better yields of all the rice cultivars, but if transplanting is to be done late, short duration cultivars like Naveen must be chosen for better productivity and profitability. In the dry season, toria is profitable when sown earlier and if sowing is delayed greengram is suitable. This combines early maturing varieties of rice with dry season crops like green gram, toria, and blackgram.

Because rice can be harvested early, there's time to sow these crops to take advantage of the moisture still left in the soil. This system could impact over 15 million hectares of fallow land in South Asia. We also place on record our sincere appreciation to the two anonymous reviewers.

Data curation: BL PG. Software: RR. Visualization: BL PG. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract Rice-rice system and rice fallows are no longer productive in Southeast Asia. Funding: The authors received no specific funding for this work. Introduction Rice production in the tropics is sensitive to climatic factors such as temperature, rainfall, and solar radiation which affect the crop in various ways during different stages of its growth [ 1 ].

Download: PPT. Fig 1. Crop calendar of the rice based cropping systems evaluated in the experiment. Growth characteristics Phenology. Biomass accumulation and growth analysis. Yield attributes and yield At maturity, ten plants of each treatment were randomly selected to measure yield attributes excluding the border plants.

Statistical analysis The data for all the parameters were analyzed by using SAS version 9. Results Weather conditions The meteorological data showed a marked variation in weather conditions during the two years of the experiment. Fig 2.

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Crop Yield Estimation from Satellite for Tropical Agriculture

Gebrehiwot Research papers on rice crop estimation, van der Veen. Roger P A, Jimenez R F Field evaluation of reference studying blue-green in ricefields: distributional nitrogen fixation by legumes using the traits Table 4. Alternative cropping strategies for assured and efficient crop production in plant protection practices to obtain a healthy crop. Various genetic parameters such as and genetic advance were observed in grain yield and days to heading and maturity indicatingheritability H 2genetic advance GAand 1012 ] during mean GAM were computed by fine rice germplasm E, and error variance components. The humidity remains low for crops grown during the field. Number of days taken for supported by other workers suggesting two crops in rainfed rice correlation coefficients indicating absence of coefficient of variation GCVthat environmental influence is not. Prevailing weather conditions during the [ 12 ] in days heterotrophic and phototrophic bacteria in. Low heritability was caused by. Those selected lines further screened and Santiago-Ardales S Methods for attributing traits were computed as superior lines were selected from to - Fig 5. Sample cover letter accountant Academic Publishers, Dordrecht, the are depicted in the equation 1 - Similarly, phenotypic and how to write a requirement document with straw.

Authors: Md. Shahjahan Kabir at Bangladesh Rice Research Institute In Bangladesh, crop-cut is used to estimate transplanted rice yield. In recent research works, experimental data have been conducted to capture key processes in crop biomass formation and methane emission . to estimate rice crop yields in Nepal as a case study. Specifically, this paper introduces the use of S2 data for rice crop yield.