Journal Articles

Permanent URI for this collection


Recent Submissions

Now showing 1 - 5 of 222
  • Item
    Innovation and Research by Private Agribusiness in India
    (2012-05) Latha Nagarajan; Carl E. Pray
    Agricultural research and innovation has been a major source of agricultural growth in developing countries. Unlike most research on agricultural research and innovation which concentrated on the role of government research institutes and the international agricultural research centers of the Consultative Group for International Agricultural Research, this paper focuses on private sector research and innovation. It measures private research and innovation in India where agribusiness is making major investments in research and producing innovations that are extremely important to farmers. It also reviews Indian policies that influence research and innovation. This new data and policy analysis can provide India policy makers with a basis for policies that can strengthen the direction and impact of agricultural research and innovation in the future. Agricultural innovations in India have rapidly increased since the 1980s. Government data and surveys of seed firms show that from about 1990 to 2010 the number of new seed cultivars available to farmers in maize, wheat, and rice roughly doubled, while the number of cotton cultivars at least tripled. Biotechnology innovations went from zero in the 1990s to 5 genetically modified (GM) traits in hundreds of GM cotton cultivars by 2008. Pesticide registrations went from 104 in the period 1980–1989 to 228 during the period 2000–2010. Similar growth in innovations also occurred in the agricultural machinery, veterinary medicine, and agricultural processing industries. These innovations have come from foreign technology transferred into India as well as from incountry public and—increasingly—private research. Based on interviews with firms and data from annual reports, we find that private investment in agricultural research grew from US$54 million in 1994/95 to US$250 million in 2008/09 (in 2005 dollars). Growth in private research and development (R&D) expenditure was particularly rapid in the seed and plant biotechnology industry, which grew by more than 10 times between the mid-1990s and 2009. Private innovations have contributed to agricultural productivity and incomes. Research and innovation by private industry led to the boom in cotton exports and to rapid increases in exports of generic pesticides and agricultural machinery. Private hybrids of cotton, rice, maize, pearl millet, and sorghum increased yields over public hybrids, varieties, and landraces. Small farmers in some of the poorest regions of India—the semiarid tropics of central India and the rainfed rice regions of eastern India—get higher productivity with private hybrids. The increases in innovation and R&D were led by expanding demand for agricultural products, which increased demand for land-, labor-, and water-saving inputs. A second major factor was the economic liberalization that allowed large Indian corporations, business houses, and foreign firms to invest in agriculture and agribusiness. Firms’ decisions to conduct research in India were also encouraged by strong public-sector research, which provided firms with increased opportunities to develop new products with scientists, such as hybrid cultivars. Finally, research was stimulated by the availability of new tools of science, such as biotechnology, and by the recent strengthening of intellectual property rights.
  • Item
    Nutrient Management in Conservation Agriculture-based Production Systems
    (2021) Yadvinder Singh; Parihar C.M.; Yashpal Singh Saharawat; Jat H.S; Jat M.L
    Conservation agriculture (CA) based crop management practices enhance soil health that ultimately improves crop production with low environmental foot prints. Developments of the better nutrient management practices are important to successful implementation of CA On average, efficiency of fertilizer N in India is only 30-40% in rice and 50-60% in other cereals. Higher nutrient use efficiency (NUE) under CA can be achieved through fine-tuning of nutrient management practices based on local site-specific conditions developed for conventional till- based agriculture. In South Asia systematic research on nutrient dynamics in soils and crop nutrient management requirements in CA systems is limited. Opportunities exist to enhance the yield, profitability. and NUE through site-specific nutrient management (SSNM) in CA Various tools, techniques and decision support systems (nutrient expert, optical sensors and leaf colour chart) are available for soil- and plant- based precision nutrient management, which offer the potential to enhance NUE in cereals and to mitigate environmental quality risk by avoiding N losses via volatilization, leaching and denitrification. GreenSeeker (GS)-based SSNM saved-20-30 kg N ha without affecting the grain yield under CA-based cereal systems compared to general recommended dose of fertilizers Nutrient expert and GS-based nutrient management reduced GHG intensity of rice, wheat and maize production by 5-35 and 0-13%, respectively over farmers' fertilizer practice. There is a need to develop nutrient prescriptions and application strategies in line with the 4R-principles to increase the NUE under CA- based management practices Future studies should be focused on layering of CA with different novel nutrient management tools and subsurface fertigation for increasing both water and nutrient use efficiency In CA-based production systems, innovations in machinery are needed for precise band placement at seeding and during crop growth.
  • Item
    Long-term Soil Organic Carbon and Crop Yield Feedbacks Differ between 16 soil-Crop Models in Sub-Saharan Africa
    (2024) Antoine Couëdel; Falconnier Gatien; Myriam Adam; Rémi Cardinael; Kenneth Boote; Eric Justes; Ward N Smith; Anthony M Whitbread; François Affholder; Juraj Balkovič; Bruno Basso; Arti Bhatia; Bidisha Chakrabarti; Regis Chikowo; Mathias Christina; Babacar Faye; Fabien Ferchaud; Christian Folberth; Folorunso Mathew Akinseye; Thomas Gaiser; Marcelo V Galdos; Sebastian Gayler; Aram Gorooei; Brian B Grant; Hervé Guibert; Gerrit Hoogenboom; Bahareh Kamali; Moritz Laub; Fidel Maureira; Fasil Mequanint; Cheryl H Porter; Dominique Ripoche; Alex C. Ruane; Leonard Rusinamhodzi; Shikha Sharma; Upendra Singh; J. Six; Amit Kumar Srivastava; Bernard Vanlauwe; Antoine Versini; Murilo Vianna; Heidi Webber; Tobias K.D. Weber; Congmu Zhang; Marc Corbeels; Cheryl H. Porter
    Food insecurity in sub-Saharan Africa is partly due to low staple crop yields, resulting from poor soil fertility and low nutrient inputs. Integrated soil fertility management (ISFM), which includes the combined use of mineral and organic fertilizers, can contribute to increasing yields and sustaining soil organic carbon (SOC) in the long term. Soil-crop simulation models can help assess the performance and trade-offs of a range of crop management practices including ISFM, under current and future climate. Yet, uncertainty in model simulations can be high, resulting from poor model calibration and/or inadequate model structure. Multi-model simulations have been shown to be more robust than those with single models and help understand and reduce modelling uncertainty. In this study, we aim to perform the first multi-model comparison for long-term simulations of crop yield and SOC and their feedbacks in SSA. We evaluated the performance of 16 soil-crop models using data from four long-term maize experiments at sites in SSA with contrasting climates and soils. Each experiment had four treatments: i) no exogenous inputs, ii) addition of mineral nitrogen (N) fertilizer, iii) use of organic amendments, and iv) combined use of mineral and organic inputs. We assessed model performance in two steps: through blind calibration involving a minimum level of experimental data provided to the modeling teams, and subsequently through full calibration, which included a more extensive set of observational data. Model ensemble accuracy was greater with full calibration than blind calibration. Improvement in model accuracy was larger for maize yields (nRMSE 48 vs 18%) than for topsoil SOC (nRMSE 22 vs 14%). Model ensemble uncertainty (defined as the coefficient of variation across the 16 models) increased over the duration of the long-term experiments. Uncertainty of SOC simulations increased when organic amendments were used, whilst uncertainty of yield predictions was largest when no inputs were applied. Our study revealed large discrepancies among the models in simulating i) crop-to-soil feedbacks due to uncertainties in simulated carbon coming from roots, and ii) soil-to-crop feedbacks due to large uncertainties in simulated crop N supply from soil organic matter decomposition. These discrepancies were largest when organic amendments were applied. The results highlight the need for long-term experiments in which root and soil N dynamics are monitored. This will provide the corresponding data to improve and calibrate soil-crop models, which will lead to more robust and reliable simulations of SOC and crop productivity, and their interactions.
  • Item
    Impact of Agro-Geotextiles on Soil Aggregation and Organic Carbon Sequestration under a Conservation-Tilled Maize-Based Cropping System in the Indian Himalayas
    (2023-11-16) Plabani Roy; Ranjan Bhattacharyya; Raman Jeet Singh; N. K. Sharma; Gopal Kumar; M. Madhu; D. R. Biswas; Avijit Ghosh; Shrila Das; Ann Maria Joseph; T. K. Das; Soora Naresh Kumar; S. L. Jat; Yashpal Singh Saharawat; Pramod Jha
    Although agro-geotextile (AGT) emplacement shows potential to mitigate soil loss and, thus, increase carbon sequestration, comprehensive information is scanty on the impact of using agro-geotextiles on soil organic carbon (SOC) sequestration, aggregate-associated C, and soil loss in the foothills of the Indian Himalayan Region. We evaluated the impacts of Arundo donax AGT in different configurations on SOC sequestration, aggregate stability, and carbon management index (CMI) since 2017 under maize-based cropping systems on a 4% land slope, where eight treatment procedures were adopted. The results revealed that A. donax placement at 0.5-m vertical-interval pea–wheat (M + AD10G0.5-P-W) treatment had ~23% increase in SOC stock (27.87 Mg·ha−1 ) compared to the maize–wheat (M-W) system in the 0–30-cm soil layer. M + AD10G0.5-P-W and maize–pea–wheat treatments under bench terracing (M-P-W)BT had similar impacts on SOC stocks in that layer after 5 years of cropping. The total SOC values in bulk soils, macroaggregates, and microaggregates were ~24, 20, and 31% higher, respectively, in plots under M + AD10G0.5-P-W treatment than M-W in the topsoil (0–5 cm). The inclusion of post-rainy season vegetable pea in the maize–wheat cropping system, along with AGT application and crop residue management, generated additional biomass and enhanced CMI by ~60% in the plots under M + AD10G0.5-P-W treatment over M-W, although M + AD10G0.5-P-W and (M-P-W)BT had similar effects in the topsoil. In the 5–15-cm layer, there was no significant effect of soil conservation practices on CMI values. Under the M + AD10G0.5-P-W treatment, the annual mean soil loss decreased by ~92% over M-W treatment. We observed that CMI, proportion of macroaggregates, aggregate-associated C, labile C, total SOC concentration (thus, SOC accumulation rate), and mean annual C input were strongly correlated with the mean annual soil loss from 2017 to 2021. The study revealed that the emplacement of an A. donax mat and incorporation of a legume in a cropping system (M-W), conservation tillage, and crop residue retention not only prevented soil loss but also enhanced C sequestration compared to farmers’ practice (M-W) in the Indian Himalayas. The significance of this study is soil conservation, recycling of residues and weeds, and climate change adaptation and mitigation, as well as increasing farmers’ income.
  • Item
    Modeling the Growth, Yield and N Dynamics of Wheat for Decoding the Tillage and Nitrogen Nexus in 8-Years Long-Term Conservation Agriculture based Maize-Wheat System
    (2024-01-31) Kamlesh Kumar; C. M. Parihar; D. R. Sena; Samarth Godara; Kiranmoy Patra; Ayan Sarkar; K. Srikanth Reddy; P. C. Ghasal; Sneha Bharadwaj; A. L. Meena; T. K. Das; S. L. Jat; D. K. Sharma; Yashpal Singh Saharawat; Mahesh K. Gathala8; Upendra Singh; Hari Sankar Nayak
    Context: Agricultural field experiments are costly and time-consuming, and their site-specific nature limits their ability to capture spatial and temporal variability. This hinders the transfer of crop management information across different locations, impeding effective agricultural decision-making. Further, accurate estimates of the benefits and risks of alternative crop and nutrient management options are crucial for effective decision-making in agriculture. Objective: The objective of this study was to utilize the Crop Environment Resource Synthesis CERES-Wheat model to simulate crop growth, yield, and nitrogen dynamics in a long-term conservation agriculture (CA) based wheat system. The study aimed to calibrate the model using data from a field experiment conducted during the 2019-20-2020-21 growing seasons and evaluation it with independent data from the year 2021–22. Method: Crop simulation models, such as the Crop Environment Resource Synthesis CERES-Wheat (DSSAT v 4.8), may provide valuable insights into crop growth and nitrogen dynamics, enabling decision makers to understand and manage production risk more effectively. Therefore, the present study employed the CERES-Wheat (DSSAT v 4.8) model and calibrated it using field data, including plant phenological phases, leaf area index, aboveground biomass, and grain yield from the 2019-20-2020-21 growing seasons. An independent dataset from the year 2021–22 was used for model evaluation. The model was used to investigate the relationship between growing degree days (GDD), temperature, nitrate and ammonical concentration in soil, and nitrogen uptake by the crop. Additionally, the study explored the impact of contrasting tillage practices and fertilizer nitrogen management options on wheat yields. The experimental site is situated at ICAR-Indian Agricultural Research Institute (IARI), New Delhi, representing Indian Trans-Gangetic Plains Zone (28o 40’N latitude, 77o 11’E longitude and an altitude of 228 m above sea level). The treatments consist of four nitrogen management options, viz., N0 (zero nitrogen), N150 (150 kg N ha−1 through urea), GS (Green seeker based urea application) and USG (urea super granules @150 kg N ha−1 ) in two contrasting tillage systems, i.e., CA-based zero tillage (ZT) and conventional tillage (CT). Result: The outcomes exhibited favorable agreement between the model’s simulations and the observed data for crop phenology (With less than 2 days variation in 50% onset of flowering), grain and biomass yield (Root mean square error; RMSE 336 kg ha−1 and 649 kg ha−1 , respectively), and leaf area index (LAI) (RMSE 0.28 & normalized RMSE; nRMSE 6.69%). The model effectively captured the nitrate-N (NO3 −-N) dynamics in the soil profile, exhibiting a remarkable concordance with observed data, as evident from its low RMSE = 12.39 kg ha−1 and nRMSE = 13.69%. Moreover, as it successfully simulated the N balance in the production system, the nitrate leaching and ammonia volatilization pattern as described by the model are highly useful to understand these critical phenomena under both conventional tillage (CT) and CA-based Zero Tillage (ZT) treatments Conclusion: The study concludes that the DSSAT-CERES-Wheat model has significant potential to assess the impacts of tillage and nitrogen management practices on crop growth, yield, and soil nitrogen dynamics in the western IndoGangetic Plains (IGP) region. By providing reliable forecasts within the growing season, this modeling approach can facilitate better planning and more efficient resource management. Future implications: The successful implementation of the DSSAT-CERES-Wheat model in this study highlights its applicability in assessing crop performance and soil dynamics. Future research should focus on expanding the model’s capabilities by reducing its sensitivity to initial soil nitrogen levels to refine its predictions further. Moreover, the model’s integration with decision support systems and real-time data can enhance its usefulness in aiding agricultural decision-making and supporting sustainable crop management practices.