Application of machine learning to agricultural soil data

Agriculture is a major sector in the Indian economy. One key advantage of classification and prediction of soil parameters is to save time of specialized technicians developing expensive chemical analysis. In this context, this PhD thesis has been developed in three stages: 1. Classification for soil data: we used chemical soil measurements to classify many relevant soil parameters: village-wise fertility indices; soil pH and type; soil nutrients, in order to recommend suitable amounts of fertilizers; and preferable crop. 2. Regression for generic data: we developed an experimental comparison of many regressors to a large collection of generic datasets selected from the University of California at Irving (UCI) machine learning repository. 3. Regression for soil data: We applied the regressors used in stage 2 to the soil datasets, developing a direct prediction of their numeric values. The accuracy of the prediction was evaluated for the ten soil problems, as an alternative to the prediction of the quantified values (classification) developed in stage 1

Bioeconomic modeling of farm household decisions for ex-ante impact assessment of integrated watershed development programs in semi-arid India

The increasing population and urbanization have serious implications for sustainable development in less-favoured areas of developing countries. In an attempt to sustain the long-term productivity of natural resources and to meet the food and non-food demands of growing population in the semi-arid tropics, the Indian government invests and promotes integrated watershed development programs. A comprehensive tool to assess the impacts of watershed development programs on both social well-being and sustainability of natural resource is currently lacking. In this study, we develop a watershed level bioeconomic model to assess the ex-ante impacts of key technological and policy interventions on the socioeconomic well-being of rural households and the natural resource base. These interventions are simulated using data from a watershed community in the semi-arid tropics of India. The model captures the interaction between economic decisions and biophysical processes and using a constrained optimization of household decision model. The interventions assessed are productivity-enhancing technologies of dryland crops and increased in irrigable area through water conservation technologies. The results show that productivity-enhancing technologies of dryland crops increase household incomes and also provided incentives for conserving soil moisture and fertility. The increase in irrigable area enables cultivation of high-value crops which increase the household income but also lead to an increase in soil erosion and nutrient mining. The results clearly indicate the necessity for prioritizing and sequencing technologies based on potential effects and trade-offs on household income and conservation of natural resources

A Survey of Machine Learning Modelling for Agricultural Soil Properties Analysis and Fertility Status Predictions

This research article was published by Preprints (www.preprints.org) 2023The problem of low soil fertility and limited research in agricultural data driven tools, may lead to low crop productivity which makes it imperative to research in applications of high throughput computational algorithms such as of machine learning (ML) for effective soil analysis and fertility status prediction in order to assist in optimal soil fertility management decision-making activities. However, difficulties in the choice of the key soil properties parameters for use in reliable soil nutrients analysis and fertility prediction. Also, individual ML algorithms setbacks and modelling expert implementation procedures subjectivity, may lead to exploitation of worst fertility level targets and soil fertility status targets classification models performance reported variations. This paper surveys state-of-affair in ML for agricultural soil nutrients analysis and fertility status prediction. Prominent soil properties and widely used classical modelling algorithms and procedures are identified. Empirically exploited fertility status target classes are scrutinized, and reported soil fertility prediction model performances are depicted. The three pass method, with mixed method of qualitative content analysis and qualitative simple descriptive statistics were used in this survey. Observably, the frequently used soil nutrients and chemical properties were organic carbon, phosphorus, potassium, and potential Hydrogen, followed by iron, manganese, copper and zinc. Predominant algorithms included Random Forest, and Naïve Bayes, followed by Support Vector Machine. Model performances varied, with highest accuracy 98.93% and 98.15% achieved by ensemble methods, and the least being 60%. Interdisciplinary ML related researchers may consider using ensemble methods to develop high performance soil fertility status prediction models

Developing a high-performance soil fertility status prediction voting ensemble using brute exhaustive optimization in automated multiprecision weights of hybrid classifiers

A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Information and Communication Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyWith the advent of machine learning (ML) techniques, various algorithms have been applied in previous studies to develop models for predicting soil fertility status. However, these models are observed to use varying fertility target classes, and variations have been reported in these models' predictive performances. As a result, practical applications of these models for obtaining the most accurate predictions may become hindered. While the weighted voting ensemble (WVE) ML technique can be used to improve soil fertility status prediction by aggregating individual models prediction, guaranteeing finding of an optimal WVE assignment weights is challenging. Whereas a brute exhaustive search procedure can be applied for the mentioned task, there is a lack of exploration on the exploitation of automated classifiers' precise weights combinations as search spaces for successful optimization. This research aims to develop a high-performance soil fertility status prediction voting ensemble using brute exhaustive optimization in automated 1EXP(-)Z+ multi-precision weights of hybrid classifiers. Soil chemical properties and ML modeling algorithms for modeling soil fertility status were identified. Base hybrid ML classification models for predicting soil fertility status were evaluated using Tanzania as a case study. Finally, the base ML hybrids WVE models were optimized using brute exhaustive search procedure’s novel developed search spaces generation algorithm for guaranteed optimal solution finding. The research was designed using design science research methodology, with the application of unsupervised machine learning K-mean algorithm with a knee detection method to find the optimal number of soil fertility status target classes, and supervised learning algorithms were applied to model classifiers for those optimal classes. Three soil fertility target classes were identified by clustering technique. The model achieved on test data a predictive accuracy of 98.93%, with respective AUC of 82%, 83%, and 87% for low, medium, and high soil fertility targets classes. Whereas these performances are observed higher compared to models in previous studies, 92% correct classifications were obtained on validation against external unseen laboratory-based tested soil results. Therefore, soil testing laboratories and farmers should consider using the model to smartly manage soil fertility which may lead to improved crop growth and productivity. The government could set agricultural-related policies that require the use of the model by farmers with the provision of agricultural inputs subsidies. Future work could be to develop an integrated real-time web and mobile application for providing farmers with soil fertility status information

Bioeconomic modeling of farm household decisions for ex-ante impact assessment of integrated watershed development programs in semi-arid India

The increasing population and urbanization have serious implications for sustainable development in less-favoured areas of developing countries. In an attempt to sustain the long-term productivity of natural resources and to meet the food and non-food demands of growing population in the semi-arid tropics, the Indian government invests and promotes integrated watershed development programs. A comprehensive tool to assess the impacts of watershed development programs on both social well-being and sustainability of natural resource is currently lacking. In this study, we develop a watershed level bioeconomic model to assess the ex-ante impacts of key technological and policy interventions on the socioeconomic well-being of rural households and the natural resource base. These interventions are simulated using data from a watershed community in the semi-arid tropics of India. The model captures the interaction between economic decisions and biophysical processes and using a constrained optimization of household decision model. The interventions assessed are productivity-enhancing technologies of dryland crops and increased in irrigable area through water conservation technologies. The results show that productivity-enhancing technologies of dryland crops increase household incomes and also provided incentives for conserving soil moisture and fertility. The increase in irrigable area enables cultivation of high-value crops which increase the household income but also lead to an increase in soil erosion and nutrient mining. The results clearly indicate the necessity for prioritizing and sequencing technologies based on potential effects and trade-offs on household income and conservation of natural resources

Agroforestry Opportunities for Enhancing Resilience to Climate Change in Rainfed Areas,

Not AvailableAgroforestry provides a unique opportunity to achieve the objectives of enhancing the productivity and improving the soil quality. Tree systems can also play an important role towards adapting to the climate variability and important carbon sinks which helps to decrease the pressure on natural forests. Realizing the importance of the agroforestry in meeting the twin objectives of mitigation and adaptation to climate change as well as making rainfed agriculture more climate resilient, the ICAR-CRIDA has taken up the challenge in pursuance of National Agroforestry Policy 2014, in preparing a book on Agroforestry Opportunities for Enhancing Resilience to Climate Change in Rainfed Areas at ICAR-CRIDA to sharpen the skills of all stakeholders at national, state and district level in rainfed areas to increase agricultural productivity in response to climate changeNot Availabl

Agricultural land use and associated nutrient flows in peri-urban production systems

Urban and peri-urban agriculture could play a pivotal role as a recipient of organic waste. But recycling and reuse of solid and liquid organic waste in peri-urban agriculture requires planning tools flexible enough to capture the diversity of farming systems and to assess their nutrient status over spatial and temporal scales. This work aims at developing a methodology to determine nutrient flows and budgets at farm, village and communal level of peri-urban agricultural systems of Hanoi, Vietnam, by taking into account spatial and temporal variability of crop and nutrient manageme

Socio-Economic Evaluation of the Potential for Australian Tree Species in the Philippines

Crop Production/Industries,

Annual Report 2017-18

Not AvailableI am extremely happy and privileged to present the annual report of ICAR-CRIDA for the year 2017- 18. During the reporting year, ICAR-CRIDA has made eloquent progress in technology development and dissemination associated with climate change in rainfed agriculture and dealing contingencies in agriculture and allied sector. The institute has received copyright for “Unreaped yield potentials in major rainfed crops and scope for bridging yield gaps - A decision support system”. ICAR-CRIDA along with SAUs and KVKs prepared contingency plans at district level for all the 126 agro-climatic zones of the country (623 districts) to deal with weather related aberrations. An IFS module with cotton, vegetables, fodder and small ruminants with farm pond using portable raingun at Chenchu tribal farmer field implemented in Petrallachenu village of Nagarkurnool district showed positive impact on socio economic condition of the farmer with total net income of Rs. 96,605/- over the traditional system of growing only rainfed cotton, which gave negative returns of Rs. (-) 3600. A small scale solar powered micro-irrigation system was designed and installed for small farmers having one acre or less land under farm pond system for growing vegetables. The assessment based on daily rainfall dataset, annual average effective rainfall and runoff percentages helped in developing the expected runoff in various rainfall zones, which could be used to estimate the runoff in meso-scale watersheds. Seven inbreds of maize (DTL2, SNJ2011- 03, SNJ2011-37, SNJ2011-26, Z101-15, Z32-12 and HKI7660) were found to be promising for use in crop improvement programme under rainfed conditions. 4:4 strip intercropping system of sorghum and pigeonpea with relay horse gram performed better compared to traditional 2:1 intercropping system. In a study on resource conserving technologies, conventional tillage recorded 15% lower maize yields as compared to conservation agriculture practices. Intensive system of rearing livestock not only improved the profitability but also significantly reduced methane emissions as compared to semi-intensive and extensive systems. Heat Load Index (HLI) and Temperature Humidity Index (THI) was found to be better choice for comparing heat stress in extensively and intensively reared sheep, respectively. A rotary implement for weeding operation was developed to effectively utilize low horse power tractor for field applications. A raised bed planter cum herbicide applicator was developed and the design was transferred to Avanthi Bufa Industries Ltd., Jahirabad. Farmers’ first project, envisaged to transfer rainfed technologies with objective of doubling farmers income is being implemented in 4 villages of Pudur mandal of Vikarabad district. Among 12 pigeonpea genotypes AKT-8811, PUSA-33, GRG-276-1 and RVK-274 were the high yielders in both unstressed and rainfed conditions. An econometric analysis of impact of climate change on crop yields showed that the impacts would be more severe and widespread towards the end of the century. Under changing climatic scenarios, runoff is not expected to vary much in Vijayapura district under low or medium emission scenarios, but the high runoff potential available under the present scenario itself shows substantial scope for rainwater harvesting and its utilization for supplemental irrigation. Decreased grub duration with increased predation capacity of M. sexmaculatus on A. craccivora with elevated CO2 indicated increased predation in future climate change scenarios. For assessing the real time climate change impacts on crop water requirements, SCADA Preface based rainfall simulator and precision lysimeter was designed and developed by using state of art process automation instrumentation in climate change research complex at Hayathnagar. Rotavator, cultivator and disc plough + harrow recorded higher GHG emissions and global warming potential, whereas animal drawn implements recorded lower emissions. Evaluation of the performance of different crops under organic, inorganic and integrated production systems showed that yield of sunflower was 14 and 7% higher under integrated management (1374 kg/ha) than that of under inorganic and organic management, respectively. Supplementation of chromium propionate @ 200 ppb can help in mitigation of heat stress in grazing lambs. An experiment to evaluate 36 elite clones of short rotation and high biomass yielding multipurpose tree species (M. dhubia, Casuarina, B. balcoa, D. sisoo and Eucalyptus) was established at Hayathnagar Research Farm. The KVK under technology assessment and refinement has assessed 17 technologies through 115 trials on crop varieties, integrated crop management, horticulture and livestock management. 269 Frontline demonstrations on 19 technologies were conducted in different disciplines. It also organized 115 need based and skill oriented training programmes on various aspects of improved technologies to 3005 clientele farmers and filed level extension workers. Two special skill development programmes allotted by Department of Horticulture, Government of Telangana in the disciplines of “ farm pond construction and lining” were organized for 520 rural youths. Exemplary performance of its scientists were visible as two scientists attended trainings/exposure visit outside the country and 52 graduate and post graduate students carried out research work at ICAR-CRIDA. Sustained performance of its scientists were exhibited in terms of 20 scientists of Institute receiving several awards, fellowships, copyright and recognition from national academies, professional societies and other institutions. The scientists of the institute published a total of 116 research articles in international and national journals, 29 books/bulletins including 2 in Hindi and 112 book chapters. The contributions of scientists also appeared in the form of a number of policy papers, bulletins, popular articles, presentations in conferences, e-publications and radio and television programmes. The collaborations with several Ministries and Departments, SAUs, NGOs and Private Industries reflect its commitment to work hand-to-hand with grow together and finding the technological solutions to the problems of farmers in rainfed regions of India. I would like to place on record my sincere gratitude to Indian Council of Agricultural Research for its continued guidance and support. I appreciate all the committee members of annual report for their timely compilation and shaping this report in time.Not Availabl