A 20-year study of NDVI variability over the Northeast Region of Brazil

The natural ecosystems of the Northeast Region of Brazil (NEB) have experienced persistent drought episodes and environmental degradation during the past two decades. In this study, we examined the spatial heterogeneity and temporal dynamics of the NEB using a 20-year time series of Normalized Difference Vegetation Index (NDVI) observations, derived from the National Oceanographic and Atmospheric Administration (NOAA)-Advanced Very High Resolution Radiometer (AVHRR) instrument. A set of 12 000 spatially distributed NDVI values was analysed to investigate significant deviations from the mean-monthly values of the base period (1982-2001) in the study area. Various statistical analyses involving minimum, mean and maximum values, coefficient of variation (CV), standardized anomalies (Z-scores), and 36-month running mean were applied to monthly NDVI values to identify spatial and temporal variations in vegetation dynamics. We found strong seasonal oscillations in the vegetation-growing season (February-May) over the NEB study area, with maximum NDVI observed in April-May and seasonal variations, expressed by the CV, ranging from 14% to 32%. In addition, a consistent upward trend in vegetation greenness occurred over the period 1984-1990, and was strongly reversed in the subsequent period 1991-1998. These upward and downward trends in vegetation greenness followed an inter-annual oscillation of ∼7-8 years. We also found that dry season peak (September) latitudinal variations in NDVI were 20-25% greater in 1991-1999 than 1982-1990 across the study region. The results of this study suggest that patterns in NEB vegetation variability were a result of the impact of enhanced aridity occurring over the last decade of the 20th century. © 2006 Elsevier Ltd. All rights reserved

Contributions of individual variation in temperature, solar radiation and precipitation to crop yield in the North China Plain, 1961-2003

An understanding of the relative impacts of the changes in climate variables on crop yield can help develop effective adaptation strategies to cope with climate change. This study was conducted to investigate the effects of the interannual variability and trends in temperature, solar radiation and precipitation during 19612003 on wheat and maize yields in a double cropping system at Beijing and Zhengzhou in the North China Plain (NCP), and to examine the relative contributions of each climate variable in isolation. 129 climate scenarios consisting of all the combinations of these climate variables were constructed. Each scenario contained 43 years of observed values of one variable, combined with values of the other two variables from each individual year repeated 43 times. The Agricultural Production Systems Simulator (APSIM) was used to simulate crop yields using the ensemble of generated climate scenarios. The results showed that the warming trend during the study period did not have significant impact on wheat yield potential at both sites, and only had significant negative impact on maize yield potential at Beijing. This is in contrast with previous results on effect of warming. The decreasing trend in solar radiation had a much greater impact on simulated yields of both wheat and maize crops, causing a significant reduction in potential yield of wheat and maize at Beijing. Although decreasing trends in rainfed yield of both simulated wheat and maize were found, the substantial interannual variability of precipitation made the trends less prominen

Characterizing spatial and temporal variability of crop yield caused by climate and irrigation in the North China Plain

Grain yields of wheat and maize were obtained from national statistics and simulated with an agricultural system model to investigate the effects of historical climate variability and irrigation on crop yield in the North China Plain (NCP). Both observed and simulated yields showed large temporal and spatial variability due to variations in climate and irrigation supply. Wheat yield under full irrigation (FI) was 8 t ha-1 or higher in 80% of seasons in the north, it ranged from 7 to 10 t ha-1 in 90% of seasons in central NCP, and less than 9 t ha-1 in 85% of seasons in the south. Reduced irrigation resulted in increased crop yield variability. Wheat yield under supplemental irrigation, i.e., to meet only 50% of irrigation water requirement [supplemental irrigation (SI)] ranged from 2.7 to 8.8 t ha-1 with the maximum frequency of seasons having the range of 4-6 t ha-1 in the north, 4-7 t ha-1 in central NCP, and 5-8 t ha-1 in the south. Wheat yield under no irrigation (NI) was lower than 1 t ha-1 in about 50% of seasons. Considering the NCP as a whole, simulated maize yield under FI ranged from 3.9 to 11.8 t ha-1 with similar frequency distribution in the range of 6-11.8 t ha-1 with the interval of 2 t ha-1. It ranged from 0 to 11.8 t ha-1, uniformly distributed into the range of 4-10 t ha-1 under SI, and NI. The results give an insight into the levels of regional crop production affected by climate and water management strategies. © 2011 Springer-Verlag

Scenario development for estimating potential climate change impacts on crop production in the North China plain

It is important to investigate potential changes in temperature, precipitation and solar radiation for assessing the impacts of future climate change on agricultural production for specific regions. In this study, climate scenarios of precipitation, temperature and solar radiation for the North China Plain (NCP) were constructed in terms of stochastic daily weather sequences. A nonhomogeneous hidden Markov model (NHMM) was used to downscale daily precipitation projections at 32 stations during winter wheat and summer maize growing seasons for a baseline (1966-2005) and a 21st century (2080-2099) A1B scenario, using selected general circulation models (GCMs). A climatological seasonal cycle of regional-averaged daily reanalysis precipitation was used as input to the down-scaling for the baseline simulation; this input was then scaled by the precipitation changes from GCM projections to generate down-scaled stochastic simulations of precipitation in the 21st century. Temperature was generated using a weakly stationary generating process, conditional on precipitation occurrence, with 21st century additive changes taken from the GCMs at the regional scale. Three hypotheses about changes in solar radiation (-20%, 0% and 20%) were made considering the large uncertainty in its future change. The down-scaled simulations exhibit station increases in the mean daily rainfall of 13.9-69.7% in the scenarios driven by the GCM with the projected largest and multi-model mean precipitation increase for the wheat season, with changes of 0.4-29.9% for the maize season. In the scenario driven by the GCM with the largest projected precipitation decrease, the simulated rainfall decreases at all stations, with changes ranging from -24.6 to -0.1% for the wheat and maize seasons, respectively. Temperature increases by about 3.7°C for the wheat season and 3.6°C for the maize season. © 2013 Royal Meteorological Society

Increasing profits and reducing risks in crop production using participatory systems simulation approaches

The development of cropping systems simulation capabilities world-wide combined with easy access to powerful computing has resulted in a plethora of agricultural models and consequently, model applications. Nonetheless, the scientific credibility of such applications and their relevance to farming practice is still being questioned. Our objective in this paper is to highlight some of the model applications from which benefits for farmers were or could be obtained via changed agricultural practice or policy. Changed on-farm practice due to the direct contribution of modelling, while keenly sought after, may in some cases be less achievable than a contribution via agricultural policies. This paper is intended to give some guidance for future model applications. It is not a comprehensive review of model applications, nor is it intended to discuss modelling in the context of social science or extension policy. Rather, we take snapshots around the globe to 'take stock' and to demonstrate that well-defined financial and environmental benefits can be obtained on-farm from the use of models. We highlight the importance of 'relevance' and hence the importance of true partnerships between all stakeholders (farmer, scientists, advisers) for the successful development and adoption of simulation approaches. Specifically, we address some key points that are essential for successful model applications such as: (1) issues to be addressed must be neither trivial nor obvious; (2) a modelling approach must reduce complexity rather than proliferate choices in order to aid the decision-making process (3) the cropping systems must be sufficiently flexible to allow management interventions based on insights gained from models. The pro and cons of normative approaches (e.g. decision support software that can reach a wide audience quickly but are often poorly contextualized for any individual client) versus model applications within the context of an individual client's situation will also be discussed. We suggest that a tandem approach is necessary whereby the latter is used in the early stages of model application for confidence building amongst client groups. This paper focuses on five specific regions that differ fundamentally in terms of environment and socio-economic structure and hence in their requirements for successful model applications. Specifically, we will give examples from Australia and South America (high climatic variability, large areas, low input, technologically advanced); Africa (high climatic variability, small areas, low input, subsistence agriculture); India (high climatic variability, small areas, medium level inputs, technologically progressing; and Europe (relatively low climatic variability, small areas, high input, technologically advanced). The contrast between Australia and Europe will further demonstrate how successful model applications are strongly influenced by the policy framework within which producers operate. We suggest that this might eventually lead to better adoption of fully integrated systems approaches and result in the development of resilient farming systems that are in tune with current climatic conditions and are adaptable to biophysical and socioeconomic variability and change. (C) 2001 Elsevier Science Ltd. All rights reserved