Roberto Quiroz

Understanding precipitation patterns and land use interaction in Tibet using harmonic analysis of SPOT VGT‐S10 NDVI time series

Time series analysis of Normalized Difference Vegetation Index (NDVI) imagery is a powerful tool in studying land use and precipitation interaction in data‐scarce and inaccessible areas. The Fast Fourier Transform (FFT) was applied to the annual time series of 36 average dekadal NDVI images. The dekadal annual average pattern was calculated from 189 NDVI images from April 1998 to June 2003 acquired with the VEGETATION instruments of the SPOT‐4 and SPOT‐5 satellites in Tibet. It is shown that the first two harmonic terms of a Fourier series suffice to distinguish between land use classes. The results indicate that the highest biomass production occurs before the monsoon peak. Regression analysis with 15 meteorological stations has shown that the total amount of precipitation during the growing season shows the strongest relation with the sum of the amplitudes of the first two harmonic terms (R 2 = 0.72). Inter‐annual NDVI variation based on Fourier‐transformed time series was studied and it was shown that, early in the season, the expected NDVI behaviour of the up‐coming season could be forecast; if linked to food production this might provide a robust early warning system. The most important conclusion from this work is that harmonic time series analysis yields more reliable results than ordinary time series analysis.

Precipitation characteristics of the South American monsoon system derived from multiple datasets

TheSouthAmericanmonsoon system(SAMS) isthemostimportant climaticfeature in South America and is characterized by pronounced seasonality in precipitation during the austral summer. This study compares several statistical properties of daily gridded precipitation from different data (1998‐2008): 1) Physical Sciences Division (PSD), Earth System Research Laboratory [1.08 and 2.58 latitude (lat)/longitude (lon)]; 2) Global Precipitation Climatology Project (GPCP; 18 lat/lon); 3) Climate Prediction Center (CPC) unified gauge (CPC-uni) (0.58 lat/lon); 4) NCEP Climate Forecast System Reanalysis (CFSR) (0.58 lat/lon); 5) NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis (0.58 lat/0.38 lon); and 6) Tropical Rainfall Measuring Mission (TRMM) 3B42 V6 data (0.258 lat/lon). The same statistical analyses are applied to data in 1) a common 2.58 lat/lon grid and 2) in the original resolutions of the datasets. Alldatasetsconsistentlyrepresentthelarge-scalepatternsoftheSAMS.Theonset,demise,anddurationof SAMS are consistent among PSD, GPCP, CPC-uni, and TRMM datasets, whereas CFSR and MERRA seem to have problems in capturingthe correct timing of SAMS. Spectral analyses show that intraseasonal variance is somewhat similar in the six datasets. Moreover, differences in spatial patterns of mean precipitation are small among PSD, GPCP, CPC-uni, and TRMM data, while some discrepancies are found in CFSR and MERRA relative to the other datasets. Fitting of gamma frequency distributions to daily precipitation shows differences in the parameters that characterize the shape, scale, and tails of the frequency distributions. This suggests that significant uncertainties exist in the characterization of extreme precipitation, an issue that is highly important in the context of climate variability and change in South America.

TRMM rainfall correction over the Andean Plateau using wavelet multi-resolution analysis

Quantifying rainfall from remotely sensed data is crucial for regions where meteorological stations are scarce. This might be one of the only options for analysing rainfall patterns at different temporal and spatial scales in data-scarce environments, particularly in developing countries. The Tropical Rainfall Measuring Mission (TRMM) provides rainfall estimation over the tropics. Rainfall estimates from the TRMM satellite exhibit inaccuracies over topographically complex regions, thus warranting suitable corrections. Multi-resolution analysis (MRA) was applied to improve TRMM 3B42 daily rainfall estimation at 19 meteorological stations located over the Andean Plateau. The detailed signal from each meteorological station was added to the trend signal of each TRMM data cell. Comparing raw and corrected TRMM with gauged rainfall revealed that wavelet-based correction of TRMM 3B42 on average improved several metrics: entropy difference (15.45−1.32), determination coefficient (0.07−0.92), bias (0.68−1.01) and relative mean absolute error (RMAE, 0.86−0.59). The entropy difference of corrected TRMM and gauged rainfall was less than 5%, even when TRMM correction was performed with noise from a station located up to 565 km away from the TRMM cell. This entropy difference corresponded to an average bias of less than 10% in the rainfall estimation.

Small Cardamom-Precious for People, Harmful for Mountain Forests: Possibilities for Sustainable Cultivation in the East Usambaras, Tanzania

Abstract Small cardamom (Elettaria cardamomum) is a valuable source of income for numerous resource-poor farmers inhabiting the East Usambara Mountains in Tanzania. Notwithstanding, the primary forests in these mountains also have the highest ratio of endemic flora and fauna per 100 km2 of all biodiversity hotspots in the world. Cardamom cultivation, under current growing practices, is radically changing the composition of the forest, thus threatening the endemic species within the forest. A comparative analysis of cardamom-growing practices, problems, and opportunities in the traditional cardamom-producing country, India, and in the worlds largest cardamom producer, Guatemala, showed that deterioration is a common denominator. On-farm research, conducted as a complement to the project, demonstrated that organic cardamom could be profitably grown in homegardens. By implementing agro-forestry systems, the harmful impact of cardamom cultivation on the forests might be dramatically reduced.

Managing potato biodiversity to cope with frost risk in the high Andes: a modeling perspective.

Austral summer frosts in the Andean highlands are ubiquitous throughout the crop cycle, causing yield losses. In spite of the existing warming trend, climate change models forecast high variability, including freezing temperatures. As the potato center of origin, the region has a rich biodiversity which includes a set of frost resistant genotypes. Four contrasting potato genotypes –representing genetic variability- were considered in the present study: two species of frost resistant native potatoes (the bitter Solanum juzepczukii, var. Luki, and the non-bitter Solanum ajanhuiri, var. Ajanhuiri) and two commercial frost susceptible genotypes (Solanum tuberosum ssp. tuberosum var. Alpha and Solanum tuberosum ssp. andigenum var. Gendarme). The objective of the study was to conduct a comparative growth analysis of four genotypes and modeling their agronomic response under frost events. It included assessing their performance under Andean contrasting agroecological conditions. Independent subsets of data from four field experiments were used to parameterize, calibrate and validate a potato growth model. The validated model was used to ascertain the importance of biodiversity, represented by the four genotypes tested, as constituents of germplasm mixtures in single plots used by local farmers, a coping strategy in the face of climate variability. Also scenarios with a frost routine incorporated in the model were constructed. Luki and Ajanhuiri were the most frost resistant varieties whereas Alpha was the most susceptible. Luki and Ajanhuiri, as monoculture, outperformed the yield obtained with the mixtures under severe frosts. These results highlight the role played by local frost tolerant varieties, and featured the management importance –e.g. clean seed, strategic watering- to attain the yields reported in our experiments. The mixtures of local and introduced potatoes can thus not only provide the products demanded by the markets but also reduce the impact of frosts and thus the vulnerability of the system to abiotic stressors.

Quantifying energy dissipation by grazing animals in harsh environments.

Grazing systems in harsh environments are common throughout the world, and animal production is the mainstay of the livelihoods of many resource-poor farmers. The energy cost of the various activities involved in the process of harvesting the pasture to transform it into animal product can be estimated through an energy balance. This cost would be the difference between the metabolizable energy intake (MEI) and the energy expenditures for maintenance (MEm), temperature regulation (MEtr), and the energy for production (MEp). Each of the ME has its own net energy (NE) and its associated efficiency (K). When MEI>MEm+MEtr+MEp, the difference is attributable to the energy dissipated during grazing. The efficiency of converting the energy consumed into animal products depends on the magnitude of the dissipation. The inefficiency is associated with the energy spent in locomotion and the stress produced when there is low availability of energy in the pasture. This paper presents a method to quantify the dissipation of energy by grazing animals by considering it as a function of available energy. Such an understanding is required in order to develop management strategies to increase conversion efficiency.

Multifractal characterization of the spatial distribution of ulexite in a Bolivian salt flat

Understanding spatial patterns is a critical and under‐explored aspect of remote sensing. This paper describes how multifractal theory can be applied to characterize these heterogeneous patterns in remotely sensed data as well as to determine the operational scale. An example based on the characterization of ulexite distribution on the worlds largest salt flat (10 000 km2), located in Bolivia, using a binarized Landsat Thematic Mapper (TM) 4/7 ratio image, is used to describe the step‐by‐step procedure. Distribution was well characterized by the multifractal parameters and expressed through the f–α, τ–q and D–q relationships. Moments from q = −2 to 5 showed a linear trend in scales from approximately 0.007 to 10 000 km2. This implies that the attribute analysed could be measured at different scales, within defined boundaries, and up‐ and down‐scaled using the multifractal parameters. In addition, the asymmetry shown by the f–α spectrum indicates the presence of clusters with high probability of finding ulexite, and large areas where the mineral might be found in small patches. The areas with a high probability of finding ulexite were mapped to guide any future field survey. Using the maximum entropy concept, the operational scale to determine the mineral was obtained at 1062 m.

Improving potato drought tolerance through the induction of long-term water stress memory

Knowledge of drought tolerance in potato is limited and very little is known about stress memory in this crop. In the present study, long-term stress memory was tested on tuber yield and drought tolerance related traits in three potato varieties (Unica, Désirée and Sarnav) with contrasted yields under water restriction. Seed tubers produced by plants grown under non-restricted (non-primed tubers) and restricted (primed tubers) water conditions were sown and exposed to similar watering treatments. Tuber yield and leaf greenness of plants from primed and non-primed seeds as well as tuber carbon isotope discrimination (Δ(13)C) and antioxidant activity (AA) responses to watering treatments were compared. Higher tuber yield, both under non-restricted and restricted water regimes, was produced by primed Sarnav plants. The decrease of tuber yield and Δ(13)C with water restriction was lower in primed Unica plants. Long-term stress memory consequently appears to be highly genotype-dependent in potato. Its expression in plants originated from primed tubers and facing water restriction seems to be positively associated to the degree of inherent capability of the cultivar to yield under water restriction. However, other effects of priming appear to be genotype-independent as priming enhanced the tuber AA in response to water restriction in the three varieties.

Poverty and the Deterioration of Natural Soil Capital in the Peruvian Altiplano

The Peruvian altiplano is a marginal agricultural region with limited infrastructure, climate constraints, and high levels of poverty. Data were collected from 265 farms in four different agricultural regions, and data from three of those regions are analyzed here. Regressions were run with soil nutrient loss, soil depth loss, and yield loss over the past 20 years, as perceived by farmers. Location, topographical and management factors were considered. Additional regressions were run to examine the determinants of two agricultural management practices which were found to affect soil quality, namely fallowing and ploughing vertical furrows. Use of traditional fallowing (aynoca) was associated with helping to preserve soil quality, and was practiced by households with more education, with higher non-farm income, and in villages which had benefitted from natural resource development projects. Vertical furrowing similarly was associated with helping preserve soil quality, and was practiced by households with fewer unmet basic needs. These results have implications for the agricultural and development strategies to be followed in the region.

Detection of bacterial wilt infection caused by Ralstonia solanacearum in potato (Solanum tuberosum L.) through multifractal analysis applied to remotely sensed data

Potato bacterial wilt, caused by the bacterium Ralstonia solanacearum race 3 biovar 2 (R3bv2), affects potato production in several regions in the world. The disease becomes visually detectable when extensive damage to the crop has already occurred. Two greenhouse experiments were conducted to test the capability of a remote sensing diagnostic method supported by multispectral and multifractal analyses of the light reflectance signal, to detect physiological and morphological changes in plants caused by the infection. The analysis was carried out using the Wavelet Transform Modulus Maxima (WTMM) combined with the Multifractal (MF) analysis to assess the variability of high-resolution temporal and spatial signals and the conservative properties of the processes across temporal and spatial scales. The multispectral signal, enhanced by multifractal analysis, detected both symptomatic and latently infected plants, matching the results of ELISA laboratory assessment in 100 and 82%, respectively. Although the multispectral method provided no earlier detection than the visual assessment on symptomatic plants, the former was able to detect asymptomatic latent infection, showing a great potential as a monitoring tool for the control of bacterial wilt in potato crops. Applied to precision agriculture, this capability of the remote sensing diagnostic methodology would provide a more efficient control of the disease through an early and full spatial assessment of the health status of the crop and the prevention of spreading the disease.