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A review of the article "Agricultural biotechnology and crop productivity: macro-level evidences on contribution of Bt cotton in India" by S.K. Srivastava and D. Kolady, is presented.

Climate change poses a major threat to global food security. Agricultural systems that rely on monsoon rainfall are especially vulnerable to changes in climate variability. This paper uses machine learning to deepen understanding of how monsoon variability impacts agricultural productivity. We demonstrate that random forest modelling is effective in representing rice production variability in response to monsoon weather variability. Our random forest modelling found monsoon weather predictors explain similar levels of detrended anomaly variation in both rice yield (33%) and area harvested (35%). The role of weather in explaining harvested rice area highlights that production area changes are an important pathway through which weather extremes impact agricultural productivity, which may exacerbate losses that occur through changes in per-area yields. We find that downwelling shortwave radiation flux is the most important weather variable in explaining variation in yield anomalies, with proportion of area under irrigation being the most important predictor overall. Machine learning modelling is capable of representing crop-climate variability in monsoonal agriculture and reveals additional information compared to traditional parametric models. For example, non-linear yield and area responses of irrigation, monsoon onset and season length all match biophysical expectations. Overall, we find that random forest modelling can reveal complex non-linearities and interactions between climate and rice production variability. [ABSTRACT FROM AUTHOR]

Irrigation is an important adaptation strategy to cope with climate change which reduces vulnerability to water stress and improves crop productivity to feed millions. There is evidence of crop yield stagnation in many developing countries, and irrigation efficiency is claimed to increase crop productivity. Therefore, this paper uses data envelopment analysis to evaluate the farmer's productivity through technical efficiency (TE), i.e., the relationship between resource inputs and outputs of 513 paddy farmers in Eastern India. The results show that the farms are, on average operating at 14% TE, leaving a considerable scope to improve up to 86% to reach the optimal level. A significant difference is observed between irrigated and rain-fed paddy farmers, such that10% of the irrigated farms achieved efficiency scores over 40% and only 2% of rain-fed farms achieved the same. The tobit and beta fit regression models are estimated to find out the factors that influence the TE. Both surface water and groundwater sources of irrigation are used as predictors, along with other socio-demographic factors. Access to surface water irrigation is identified to be a significant determinant of farm efficiency, however, surface water irrigation, such as canal irrigation, is accessible only to farmers living on plain land. Farmers living on highlands need to explore other sources of irrigation practices, such as drip and sprinkler, that can increase TE and farm productivity. Therefore, this paper calls for government intervention to provide extensive training and facilities for these micro-irrigation practices. • Data Envelopment analysis was estimated to derive the paddy efficiency among the tribal villages of eastern India. • The DEA estimates show that average Technical efficiency in the study region is much below the national average. • Irrigated farmers are more efficient than the rain-fed farmers. Surface irrigation is the dominate factor. • Rain-fed paddy cultivators' farm efficiency would improve by 6.5%, if they had irrigated. • Semi-mountainous regions with little access to surface irrigation must explore micro-irrigation practices such as drips and sprinklers. [ABSTRACT FROM AUTHOR]

Background: Crop yield prediction is an important issue for the proper selection of crop for sowing. Earlier prediction of crop is done by the farmer’s experience on a particular type of field and crop. Predicting the crop is done by the farmer’s experience based on the factors like soil types, climatic condition, seasons and weather, rainfall and irrigation facilities. Methods: Data mining techniques is the better choice for predicting the crop. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year’s crop production. This research proposes and implements a system to predict crop yield from soil data. This is achieved by applying Decision Tree Algorithm on agricultural data. The main aim of this research is to pinpoint the accuracy of Decision Tree Algorithm and C 5.0 algorithm which is used to predict the crop yield. Result: This paper presents a brief analysis of Crop yield prediction using data mining technique based decision tree algorithm and C5.0 algorithm for the selected region (Krishnagiri) district of Tamil Nadu in India. The experimental result shows that the proposed work efficiently to determine the accuracy of decision tree algorithm and also to predict the crop yield production using R- Tool. [ABSTRACT FROM AUTHOR]

Highlights • Impacts of land, water and energy on agricultural sustainability are analysed. • Specialised cultivation of paddy and potato decreases agricultural sustainability. • Pulses show negentropy and higher sustainability level than other crops. • Grey water footprint of crops indicates significant level of water pollution. • Energy should be invested to improve soil health and environmental quality. Abstract Agro-ecosystem is an open system that represents a complex interaction with the environment in term of energy transformation. Growing human population and resultant demand for greater output are increasing the rate of energy flow in agro-ecosystem to facilitate more energy transformation, which is deteriorating different compartment of ecosystem and environment. This paper intends to analyse agricultural sustainability at micro level by developing a composite indicator of land, water and energy used for crop production. Cropland footprint (CF), water footprint (WF) and entropy overproduction (E op) are selected as indicators of land, water and energy respectively with the objective to explore man-land interaction by CF, while WF and E op represent the impact of that interaction. In the study area, cropland surplus or deficit is inversely related with WF and E op. Surplus crops like paddy and potato have higher WF and E op , hence are responsible for agricultural unsustainability. WF and E op are highest for paddy (8912 m3/ha) and potato (117,565 MJ/ha) respectively. Grey water footprint (WF c,grey) is the highest for potato (3708 m3/ha). Pulses (with negative E op of −9124 MJ/ha) are proved the most sustainable crop with the highest potentiality to boost agro-ecosystem sustainability. The results derived from the analysis indicate that increasing use of chemicals and more specialization of water and energy exhaustive crops are reducing agricultural sustainability. The study also suggests for necessary transformations of cropping pattern with special emphasis on incorporation of pulses, oilseeds etc. and non-productive energy investment to reinstall natural quality of agro-ecosystem, not only in the studied areas, but also in other areas facing similar site and situation. [ABSTRACT FROM AUTHOR]

Sequential cropping (also known as multiple or double cropping) is a common feature, particularly for tropical regions, where the crop seasons are largely dictated by the main wet season such as the Asian summer monsoon (ASM). The ASM provides the water resources for crops grown for the whole year, thereby influencing crop production outside the ASM period. Land surface models (LSMs) typically simulate a single crop per year, however, in order to understand how sequential cropping influences demand for resources, we need to simulate all of the crops grown within a year in a seamless way. In this paper we implement sequential cropping in a branch of the Joint UK Land Environment Simulator (JULES) and demonstrate its use at Avignon, a site that uses the sequential cropping system and provides over 15-years of continuous flux observations which we use to evaluate JULES with sequential cropping. In order to implement the method in future regional simulations where there may be large variations in growing conditions, we apply the same method to four locations in the North Indian states of Uttar Pradesh and Bihar to simulate the rice--wheat rotation and compare model yields to observations at these locations. JULES is able to simulate sequential cropping at Avignon and the four India locations, representing both crops within one growing season in each of the crop rotations presented. At Avignon the maxima of LAI, above ground biomass and canopy height occur at approximately the correct time for both crops. The magnitudes of biomass, especially for winter wheat, are underestimated and the leaf area index is overestimated. The JULES fluxes are a good fit to observations (r-values greater than 0.7), either using grasses to represent crops or the crop model, implying that both approaches represent the surface coverage correctly. For the India simulations, JULES successfully reproduces observed yields for the eastern locations, however yields are under estimated for the western locations. This development is a step forward in the ability of JULES to simulate crops in tropical regions, where this cropping system is already prevalent, while also providing the opportunity to assess the potential for other regions to implement it as an adaptation to climate change. [ABSTRACT FROM AUTHOR]

Abstract Many Climate Smart Agricultural (CSA) technologies fail to achieve their full potential impact due to low levels of adoption by smallholder farmers and difficulties in scaling CSA. This paper presents how small and medium-sized enterprises (SMEs) can act as change agents for the uptake of CSA technologies where their business models may be seen as adoption and scaling mechanisms. Drawing upon our fieldwork in Punjab (India) during which over 100 respondents have been interviewed, critical issues and enabling factors for the business model of two types of SMEs, i.e. farmer cooperatives and individual service providers of climate smart technologies have been identified. Enabling factors supporting adoption are driven by scientific and practical evidence of CSA technologies, good partnership between SMEs and research institutes, good customer relationships and effective channels through farmers' field trials. Critical issues consist of distortive government subsidies on energy and the lack of market intelligence affecting the profitability of the business model. Scaling is enhanced through market intelligence and a favouring regulatory landscape. However, difficult socio-economic circumstances and distortive government subsidies limit the role of SMEs business model as mechanism for scaling. Highlights • Small and medium-sized enterprises foster scaling of climate smart agriculture. • Energy subsidies hinder business models in adoption of climate smart agriculture. • Market intelligence is key for business models to scale climate smart agriculture. • Youth forms a niche market for climate smart agriculture. [ABSTRACT FROM AUTHOR]

Pulses play an important role in providing a nutritionally balanced diet. These are the principal source of protein for vegetarians. India is the world's largest producer of pulses, followed by Canada. Brazil produces large beans only. Pulses are the second main source of protein after cereals in Indian diet. India is the largest producer, consumer and importer of pulses. Basically the total pulses area occupied 26.28 million hectares which contributed production 18.10MT during 2010-11. However, the growth rate of pulses area and production were found negligible as compared to cereal like wheat and paddy and there exit wide inter states variability in their yield in the country. This study results the growthrate of area-0.09, -0.60 and 1.62 and production 1.52, 0.59 and 3.35 during 1980s, 1990s and 2000s decades, which affect the net per capita per day availability of pulses, has declined sharply from 61 gms to 32 gms from 1951 to 2010. Therefore, the gap of domestic demand and supply widen sharply. This paper analyses the status of pulses growth, and constraints of technology inadequacy as well as policy reform. The paper also focus on constraints of non-availability essential inputs i.e. quality seed, life saving irrigation, fertilizers and nutrients, price policy implication and marketing to be reoriented to bring it in tune with the emerging demand and supply of pulses in India. [ABSTRACT FROM AUTHOR]

The sources of growth has direct connotation with agricultural development policies. Growth from area expansion and price is unsustainable, where as if it comes from yield enhancement is sustainable over a long run. This paper has analysed the patterns and sources of growth to groundnut production in India from 1985-86 to 2014-15. Increase in value of groundnut produce was measured during 1985-86 and 94-95 as result of technology mission on oilseeds launched during mid-80s. Decrease in VOP during 1995-96 and 2004-05 may be because of adverse effect of trade liberalisation in this period. Restoration took place in last ten years from 2005-06 to 2014-15,where groundnut VOP increased in the country and all the major states except Maharashtra. The largest source of the growth to Indian groundnut in study period was yield followed by price (7.53%) and area effect (0.97%). Yield contributed fifteen per cent of total growth in the country and to the maximum of 55 per cent in Tamil Nadu. Diversification effect was measured negative at the country as well as at major states in post-WTO periods indicated the drifting away of groundnut acreage to the other crops, is needed to be taken care by appropriate policy measures at central and state levels. [ABSTRACT FROM AUTHOR]

This paper aims at assess district-wise vulnerability index of the state of Karnataka State, which is predominantly is rainfed and is highly susceptible to climatic variability. Secondary data on relevant indicators were collected to prepare indices viz., crop production losses, exposure, sensitivity and adaptive capacity. Following normalization and using appropriate weights for indicators, these four indices were used for constructing vulnerability index, which can be used a rapid assessment method for prioritizing districts that need measures to moderate the detrimental impact of climate change. It has been observed that Climatic variability caused higher production losses in cereals, pulses and oilseeds in Davangere, Gulbarga and Raichur districts, respectively. Districts like Koppal, Raichur, Bijapur Gulbarga, Gadag, Bagalkote and Bellary were placed under extreme degree of exposure. As per the sensitivity index scores, Kolar district is the most sensitive. Further, Bengaluru (Urban), Dakshin Kannada and Kodagu are ranked first, second and third in terms of adaptive capacity in the state. Overall, vulnerability index scores indicate that Gulbarga, Koppal, Raichur, Bellary, Bagalkote, Bijapur and Belgaum are extremely vulnerable districts in the state. It was also estimated that around 70% of the cultivated area, which supports 60% and 67% of livestock and rural population of the state, respectively are facing ‘extreme to high’ level of vulnerability. The ranking based prioritization of the vulnerable areas calls for a holistic approach for each district or a group of districts to reduce their sensitivity, minimize exposure to rainfall variability through implementation of site-specific and leverage adaptive capacity through better health and education facilities, expansion of employment opportunities in other sectors or reducing over dependence on agriculture. [ABSTRACT FROM AUTHOR]

At present, climate change is one of the most challenging environmental issues as it poses potential threat to different sectors of economy at global level. Agriculture being an open activity is primarily dependent on climatic factors and change in climatic conditions affects the production, quality and quantity of crop production in an area. This paper attempts to study effects of only two parameters of climate i.e. temperature and rainfall on agricultural production in northwest region of India. Northwest region comprising of Punjab, Haryana, Himachal Pradesh and Jammu Kashmir states is the greatest food bowl of India contributing to its food security. The analysis of mean monthly rainfall and maximum and minimum temperatures (1901-2006) shows no significant change in temperature and rainfall conditions from 1901 to 1960; but afterward the change is more pronounced. On the whole any significant change in climatic conditions will not only challenge the food production of the region but also challenge the country's food security situation. [ABSTRACT FROM AUTHOR]

Increased demand of farm products and depletion of natural resources compel the agriculture community to increase the use of Information and Communication Technology (ICT) in various farming processes. Agricultural Decision Support Systems (DSS) proved useful in this regard. The majority of available Agricultural DSSs a re either crop or task specific. Less emphasis has been placed on the development of comprehensive DSS, which are non - specific regarding crops or farming processes. The crop or task specific DSSs are mainly developed with rule based or knowledge transfer based approaches. The DSSs based on these methodologies lack the ability for scaling up and generalization. The Knowledge engineering modeling approach is more suitable for the development of large and generalized DSS. Unfortunately the model based knowledge engineering approach is not much exploited for the development of Agricultural DSS. CommonKADS is one of the popular modeling frameworks used for the development of Knowledge Based System (KBS). The paper presents the organization, agent, task, communica tion, knowledge and design models based on the CommonKADS approach for the development of scalable Agricultural DSS. A specific web based DSS application is used for demonstrating the multi agent CommonKADS modeling approach. The system offers decision support for irrigation scheduling and weather based disease forecasting for the popular crops of India. The proposed framework along with the required expert knowledge, provides a platform on which the larger DSS can be built for any crop at a given location. [ABSTRACT FROM AUTHOR]

Agriculture is one of the most important sectors in the Indian context. It is one of the highest employing sectors in the Indian scenario. Unlike other sectors agriculture is highly dependent on the quality and the quantity of both the external factors like rainfall, climate, pH of the soil, fertilizers and insecticides used, and internal factors like the quality of seeds. This paper predicts the production of crops as a function of rainfall for four Indian States. This knowledge can be implemented in generating a rough overview of how the production is based on rainfall and how much can a specific crop production for the amount of rainfall it receives. Two crops each belonging to four different states are chosen and the best regression model for the crop of the state is chosen. There is no research done solely on how rainfall affects crops of particular states. The proposed method of evaluation is better than other existing methods of evaluation as it evaluates all the regression techniques (Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Random Forest, and XGBRegression) for two crops of four individual states. For balanced evaluation, two states of North India and two states of South India are selected. The regression techniques are evaluated based on their Mean Squared Error. • Crops of same species have different production rates within a same country. • Performance of each algorithm is different for crops within state. • Rainfall is the key determining factor for crop production in India. • Production of crop is affected by environment more than the genotype. • Predicting the production provides high impetus for developing economies. [ABSTRACT FROM AUTHOR]

Climate variability and change coupled with small landholdings, low land productivity and water scarcity in arid and semi-arid regions contribute to environmental degradation, reduced agricultural productivity, and increased vulnerability to the rural communities. With the aid of the fuzzy cognitive maps constructed by 427 community groups with 4–5 members in each group, drawn from 96 villages in 12 districts of arid and semi-arid India, the paper evaluates the effectiveness of various adaptation pathways. The ongoing adaptations in arid and semi-arid India face adaptation deficits. The FCM-based simulations revealed that integrated adaptation measures that embrace nature-based solutions, including integrated water resource management, natural farming-assisted soil rejuvenation, and improved agricultural productivity are most likely to enhance the resilience of small and marginalised farming communities to climate variability and change. Facilitation of such adaptation measures requires inclusive and adaptive local institutions, sufficient financial assistance, and climate information services. Besides, gender-nuanced, inclusive, and adaptive governance and processes would be helpful for the implementation of appropriate adaptation interventions in arid and semi-arid drylands worldwide. Hence policy-makers must enable polycentric and adaptive governance, and inclusive institutions and processes. The emphasis on multiple factors in a socio-ecological system often makes it difficult to understand the critical role of a particular factor. However, the FCM-based simulations in this study helped us overcome such limitations. Image 1 • FCM-based simulations used to examine the effectiveness of current adaptations. • Current adaptations face adaptation deficit and provide limited resilience. • Integrated adaptations embracing nature-based solutions could provide resilience. • Lack of finance, technology, and locally relevant information are the key barriers. • Polycentric-adaptive governance and inclusive institutions & processes are crucial. [ABSTRACT FROM AUTHOR]

Current ozone (O 3) concentration levels entail significant damages in crop yields around the world. The reaction of the emitted precursors (mostly methane and nitrogen oxides) with solar radiation contribute to O 3 levels that exceed established thresholds for crop damage. This paper shows current and projected (up to 2080) relative yield losses (RYLs) driven by O 3 exposure for different crops and the associated economic damages applying dynamic crop production and prices that are calculated per region and period. We adjust future crop yields in the Global Change Assessment Model (GCAM) to reflect the RYLs and analyze the effects on agricultural markets. We find that the changes (generally reductions) in O 3 precursor emissions in a reference scenario would reduce the agricultural damages, compared to present, for most of the regions, with a few exceptions including India, where higher future O 3 concentrations have large negative impacts on crop yields. The annual economic impact of O 3 driven losses from 2010 to 2080 are, in billion US dollars at 2015 prices ($B), 5.0–6.0, 9.8–18.8, 6.7–10.6 and 10.4–12.5 for corn, soybeans, rice and wheat, respectively, with the large losses for wheat and soybeans driven by their comparatively high responses to O 3. When O 3 effects are explicitly modelled as exogenous yield shocks in future periods, there is a direct impact in future agricultural markets. Therefore, the aggregated net present value (NPV) of crop production would be reduced around by $90.8 B at a global level. However, these changes are not distributed evenly across regions, and the net present market value of the crops would increase by up to $118.2 B (India) or decrease by up to $59.2 B (China). • Future ozone concentration levels will exceed established thresholds. • Annual economic damages of O 3 -related yield losses range from $34 B to $45 B. • USA, the European Union, India and China bear the majority of the economic damages. • Considering O 3 effects, cumulative NPV of crop production decreases around $90.8 B. [ABSTRACT FROM AUTHOR]