Hasil untuk "Agriculture"

M. S. Khan, A. Zaidi, P. A. Wani

R. Mittler, E. Blumwald

D. Reay, E. Davidson, Keith A. Smith et al.

Gerald C. Nelson, M. Rosegrant, J. Koo et al.

G. P. Robertson, P. Vitousek

M. Zeder

B. Davies, D. Baulcombe, I. Crute et al.

G. Nelson, H. Valin, R. Sands et al.

C. Badgley, Jeremy Moghtader, E. Quintero et al.

A. Lithourgidis, C. Dordas, C. Damalas et al.

L. Christiaensen, L. Demery, Jesper J. Kuhl

T. Gomiero, D. Pimentel, M. Paoletti

B. Smit, M. Skinner

Harihar Bhawan

T h i s p a p e r r e v i e w s t h e d i s c u s s i o n cont a i n e d i n t h e 11 p r e s e n t a t i o n s g i v e n a t a sympos ium on r e c r e a t i o n c h o i c e b e h a v i o r . It a d d r e s s e s ma jo r p o i n t s o f commonality i n t h e p a p e r s , a s w e l l a s a r e a s where d i f f e r e n c e s e x i s t . I t a l s o sugg e s t s a number of a r e a s i n which a d d i t i o n a l r e s e a r c h on r e c r e a t i o n c h o i c e b e h a v i o r i s needed.

R. Prasad, Vivek Kumar, K. Prasad

Nanotechnology is a promising field of interdisciplinary research. It opens up a wide array of opportunities in various fields like medicine, pharmaceuticals, electronics and agriculture. The potential uses and benefits of nanotechnology are enormous. The current global population is nearly 7 */billion with 50% living in Asia. A large proportion of those living in developing countries face daily food shortages as a result of environmental impacts or political instability, while in the developed world there is surplus of food. For developing countries, the drive is to develop drought and pest resistant crops, which also maximize yield. The potential of nanotechnology to revolutionise the health care, textile, materials, information and communication technology, and energy sectors has been well publicized. The application of nanotechnology to agriculture and food industries is also getting attention nowadays. Investments in agriculture and food nanotechnologies carry increasing weight because their potential benefits range from improved food quality and safety to reduced agricultural inputs and improved processing and nutrition. While most investment is made primarily in developed countries, research advancements provide glimpses of potential applications in agricultural, food, and water safety that could have significant impacts on rural populations in developing countries. This review is concentrated on modern strategies used for the management of water, pesticides, limitations in the use of chemical pesticides and potential of nano-materials in sustainable agriculture management as modern approaches of nanotechnology.   Key words: Agriculture, nanotechnology, nanofertilizer, nanoencapsulation, nanoherbicides.

N. McClintock

M. Srbinovska, C. Gavrovski, V. Dimcev et al.

Sarah J. Velten, J. Leventon, Nicolas W. Jager et al.

The idea of a sustainable agriculture has gained prominence since the publication of the Brundtland Report in 1987. Yet, the concept of sustainable agriculture is very vague and ambiguous in its meaning, which renders its use and implementation extremely difficult. In this systematic review paper, we aim to advance understandings of sustainable agriculture from a social science and governance perspective by identifying areas of complementarity and concern between emerging definitions of sustainable agriculture. For this purpose, we conducted a structured literature review in combination with a cluster analysis in order to (1) identify the overall ideas and aspects associated with sustainable agriculture; (2) detect patterns and differences in how these ideas and aspects are adopted or applied; (3) evaluate how the different ideas and aspects of sustainable agriculture are combined in the scientific debate, and assess whether these different conceptions match with those that have been claimed to exist in the debate. There are two valuable outcomes from this research. The first is a framework for understanding the components of sustainable agriculture. The second outcome is in highlighting ways for actors involved with sustainable agriculture to deal with the complexity and multiplicity of this concept in a constructive manner.

P. Brown, S. Saa

Biostimulants, which may be derived from a wide range of natural or synthetic processes, are now widely used in agriculture and yet the mode of action of these materials is not well understood. On the basis of available literature, and based upon the diversity of biostimulant responses highlighted in this focus issue, we hypothesize that biostimulants function by directly interacting with plant signaling cascades or act through stimulation of endophytic and non-endophytic bacteria, yeast and fungi to produce molecules of benefit to the plant. The benefit of the biostimulant is derived from the reduction in assimilates that are diverted to non-productive stress response metabolism.

Zhixing Zhang, Jesen Zhang, Hao Liu et al.

Foundation models for agriculture are increasingly trained on massive spatiotemporal data (e.g., multi-spectral remote sensing, soil grids, and field-level management logs) and achieve strong performance on forecasting and monitoring. However, these models lack language-based reasoning and interactive capabilities, limiting their usefulness in real-world agronomic workflows. Meanwhile, large language models (LLMs) excel at interpreting and generating text, but cannot directly reason over high-dimensional, heterogeneous agricultural datasets. We bridge this gap with an agentic framework for agricultural science. It provides a Python execution environment, AgriWorld, exposing unified tools for geospatial queries over field parcels, remote-sensing time-series analytics, crop growth simulation, and task-specific predictors (e.g., yield, stress, and disease risk). On top of this environment, we design a multi-turn LLM agent, Agro-Reflective, that iteratively writes code, observes execution results, and refines its analysis via an execute-observe-refine loop. We introduce AgroBench, with scalable data generation for diverse agricultural QA spanning lookups, forecasting, anomaly detection, and counterfactual "what-if" analysis. Experiments outperform text-only and direct tool-use baselines, validating execution-driven reflection for reliable agricultural reasoning.