Applications of systems simulation for understanding and increasing yield potential of wheat and rice

Understanding and increasing yield potential of cereals is essential to meet the growing food demand in Asia. A crop growth simulation model -WTGROWS- was developed to quantify the climatically determined potential grain yields and yield gaps in wheat in tropics and sub-tropics. The model written in PCSMP simulates daily dry matter production as a function of solar radiation, maximum and minimum temperatures, and water and nitrogen stresses. Comparison of simulated and measured quantities indicated satisfactory performance of the model in reference to water and nitrogen uptake, dry matter growth and grain yield in potential as well as water- and N-limited environments. The wheat yield potential in India varied between 2.6 and 8.3 t ha -1depending upon the location. Economically optimal yields in irrigated environments were estimated for all locations based on current price ratios of N fertilizer and grain, native soil fertility, simulated crop response to N fertilizer and other costs related to transport, harvesting and market forces. Yield gaps were found to be small in irrigated regions of northwestern India but significantly large in eastern regions. Almost 35 - 50% of the gap could be ascribed to delayed sowing. Crop simulation with different amounts of nitrogen and irrigation showed significant interaction between water and N availability and climatic variability, particularly with low inputs. The effect of climate change was more pronounced in central India where yield potential is already low.The model was also used to explore the opportunities for growing wheat in irrigated and rainfed tropical southeastern Asia. The results indicated that potential yields exceed 3 t ha -1at all places and increased further with latitude and elevation. At sea level, between equator and 8°N latitude, potential grain yield was 3 t ha -1. It increased to 5 t ha -1at 21°N and 4.5 t ha -1at 10°S latitudes. Realization of the yield potential of the presently available varieties may be limited because of several agronomic constraints.A simulation framework has been developed to determine the relative importance of different plant traits in isolation or in combination for increasing yield potential. In this approach, hypothetical genotypes are 'created' by changing the specific crop parameters of a crop simulation model. The impact of simultaneous change in many traits is assessed by randomly combining different traits in the hypothetical genotype. The approach is illustrated with examples for rice in tropics. No trait individually or in combination provides more than 5% advantage in yield at the level of management typically practiced by breeders. In such environments, even though genotypes may possess traits for higher yield potential, they will not be able to express them. Another framework is presented for using crop simulation models and statistical analysis together to increase the efficiency of multi-environment genotype testing.</p

Managing Climatic Risks to Combat Land Degradation and Enhance Food security: Key Information Needs

This paper discusses the key information needs to reduce the negative impacts of weather variability and climate change on land degradation and food security, and identifies the opportunities and barriers between the information and services needed. It suggests that vulnerability assessments based on a livelihood concept that includes climate information and key socio-economic variables can overcome the narrow focus of common one-dimensional vulnerability studies. Both current and future climatic risks can be managed better if there is appropriate policy and institutional support together with technological interventions to address the complexities of multiple risks that agriculture has to face. This would require effective partnerships among agencies dealing with meteorological and hydrological services, agricultural research, land degradation and food security issues. In addition a state-of-the-art infrastructure to measure, record, store and disseminate data on weather variables, and access to weather and seasonal climate forecasts at desired spatial and temporal scales would be needed

インドタミルナドゥ州の最脆弱農業気候ゾーンにおける降雨量不足保険支払い

Weather based insurance is a resilience strategy adopted by farmers. It is intended to provide protection to the cultivator against declined rainfall, which is deemed to adversely affect the crop during its cultivation period. It is becoming popular nowadays in India due to high fluctuation in rainfall and other climate related parameters. The present paper provides a method to compute the initial premium for each crop based on the premium structure given by Agricultural Insurance Company of India Limited, New Delhi. For this, the duration in each stage of selected crop identified by Crop Production Guide(2005) jointly published by Tamil Nadu Agricultural University and Department of Agriculture, Government of Tamil Nadu and 30 years of rainfall data from Indian Meteorological Department (IMD) were used. The payout structure was derived for each stage of the selected crop in the respective district. The strike or upper threshold of the rainfall corresponds to the 30 year average accumulated rainfall of the district reference weather station while the exit or lower threshold is intended to equal the water requirement of the respective crop necessary to avoid complete crop failure. This way, the weather based crop insurance acts as a resilience mechanism for rainfall uncertainties.和文のものは、英文の報告の内容を要約したものとなってい

Potential and cost of carbon sequestration in Indian agriculture: Estimates from long-term field experiments

Carbon sequestration in tropical soils has potential for mitigating global warming and increasing agricultural productivity. We analyzed 26 long-term experiments (LTEs) in different agro-climatic zones (ACZs) of India to assess the potential and cost of C sequestration. Data on initial and final soil organic C (SOC) concentration in the recommended N, P and K (NPK); recommended N, P and K plus farmyard manure (NPK + FYM) and unfertilized (control) treatments were used to calculate carbon sequestration potential (CSP) i.e., capacity to sequester atmospheric carbon dioxide (CO2) by increasing SOC stock, under different nutrient management scenarios. In most of the LTEs wheat equivalent yields were higher in the NPK + FYM treatment than the NPK treatment. However, partial factor productivity (PFP) was more with the NPK treatment. Average SOC concentration of the control treatment was 0.54%, which increased to 0.65% in the NPK treatment and 0.82% in the NPK + FYM treatment. Compared to the control treatment the NPK + FYM treatment sequestered 0.33 Mg C ha−1 yr−1 whereas the NPK treatment sequestered 0.16 Mg C ha−1 yr−1. The CSP in different nutrient management scenarios ranged from 2.1 to 4.8 Mg C ha−1 during the study period (average 16.9 yr) of the LTEs. In 17 out of 26 LTEs, the NPK + FYM treatment had higher SOC and also higher net return than that of the NPK treatment. In the remaining 9 LTEs SOC sequestration in the NPK + FYM treatment was accomplished with decreased net return suggesting that these are economically not attractive and farmers have to incur into additional cost to achieve C sequestration. The feasibility of SOC sequestration in terms of availability of FYM and other organic sources has been discussed in the paper

Mining Contrast Subspaces

In this paper, we tackle a novel problem of mining contrast subspaces. Given a set of multidimensional objects in two classes C+  and C− and a query object o, we want to find top-k subspaces S that maximize the ratio of likelihood of o in C+  against that in C−. We demonstrate that this problem has important applications, and at the same time, is very challenging. It even does not allow polynomial time approximation. We present CSMiner, a mining method with various pruning techniques. CSMiner is substantially faster than the baseline method. Our experimental results on real data sets verify the effectiveness and efficiency of our method

Egocentric activity monitoring and recovery

This paper presents a novel approach for real-time egocentric activity recognition in which component atomic events are characterised in terms of binary relationships between parts of the body and manipulated objects. The key contribution is to summarise, within a histogram, the relationships that hold over a fixed time interval. This histogram is then classified into one of a number of atomic events. The relationships encode both the types of body parts and objects involved (e.g. wrist, hammer) together with a quantised representation of their distance apart and the normalised rate of change in this distance. The quantisation and classifier are both configured in a prior learning phase from training data. An activity is represented by a Markov model over atomic events. We show the application of the method in the prediction of the next atomic event within a manual procedure (e.g. assembling a simple device) and the detection of deviations from an expected procedure. This could be used for example in training operators in the use or servicing of a piece of equipment, or the assembly of a device from components. We evaluate our approach (’Bag-of-Relations’) on two datasets: ‘labelling and packaging bottles’ and ‘hammering nails and driving screws’, and show superior performance to existing Bag-of-Features methods that work with histograms derived from image features [1]. Finally, we show that the combination of data from vision and inertial (IMU) sensors outperforms either modality alone