No abstract

Introduction: Rainfed agriculture is one of the most vulnerable forms of crop to the effects of climate change producing either drought or excess moisture and thus damaging crops and causing substantial economic damage. In developing countries with scarce resources for technological investment, rainfed agriculture is extremely important for the food production. The sowing of seasonal maize in the Bajo Balsas is for self-consumption, thence the importance of identifying climatic hazard indicators that threaten agricultural production of this culture. The purpose of this study was to build and validate climatic hazard indicators considering the different phenological stages of the plant.                       Method: First, we identify the climatic variables and characterize the phenological cycle of maize variety that is cultivated in the region; then we elaborate a database with the following daily and monthly climatic parameters: pluvial precipitation, maximum temperature and minimum temperature. We applied data mining with the WEKA software to validate and classify the maize production of 18 years, then, the years were classified by production defining three classes: 1) low hazard: a sinister area from 0 to 10%, without drought reports; 2) moderate hazard: a sinister area from 11 to 50%, with moderate drought reports; 3) high hazard: a sinister area from 51 to 100%, with reports of severe drought.                       Results: We determined ten climatic hazard indicators of which four correspond to the number of days with precipitation lower than zero in the months of June, July, August and September (DDE0jun, DDE0jul, DDE0aug, DDE0sep); two indicators with rainfall equal to 5 mm for June and August (DDL5jun, DDL5aug); two indicators associated with the maximum temperature of July and August (TX38jul, TX38aug), and two indicators with precipitation equal to or greater than 10 mm in October and November (WDH10oct, WDH10nov). The result of the Kappa statistic was 0.91, indicating a high reliability in the classification of three hazardness categories: high, moderate and low.                       Conclusion: The proposed method on the validation of climatic hazard with the WEKA software allowed to identify the most important indicators that affect the maize phenological cycle and therefore its yield; the mathematical validation of the method shows three primary indicators, which indicates that there are primary and secondary variables that determine its climatic hazard. The indicators of climatic hazard were automated and adjusted using ICC® (Indicators of Climatic Change) which offers a useful tool for future research on climate hazard analysis of other rainfed crops.

Climatic anomalies affect agricultural production, so the identification of climate change at the local level is a pressing task. The Intergovernmental Panel on Climate Change (IPCC) has generated indices that allow the identification of extreme climate events. Recent studies of weather and climate variation have increasingly used the climate extreme indices defined by the IPCC. This article dwells upon the ICC (climate change indicators) software written in the Java language which is a convenient tool for storing and processing large sets of daily weather data. The software allows the calculation of 27 climate change indices and four indices for growing vines. Built-in analytical tools help identify trends and climate anomalies on different time intervals. The application is additionally supplied with important functionality for statistical data processing and visualization. Tools that help analyzing climate change can foster wiser strategic decision-making in the management of agriculture and ecosystems.

Since water is an increasingly limited resource, having methods for evaluating its quality is necessary to ensure an adequate and sustainableuse for human consumption, agriculture and industry. The aim of this work was to describe agriwater software for evaluating the quality of irrigation water. The software consisted of the following stages: a) development of a database capable of hosting all the information stored in the software; b) conversion of chemical units; c) indexes calculation; and d) evaluation of performance and efficiency in data processing. The Agriwater software converts measuring units of ions to mg L-1, meq L-1 and mmol L-1, calculating the following water quality indexes: sodium absorption ratio, effective salinity, potential salinity and chloride toxicity. The result is Agriwater software, a professional tool that can handle, analyze and evaluate thousands of data in seconds in an intuitive and simple way.

The analysis of climate data is tedious and time-consuming, delaying the analysis of millions of meteorological data worldwide. Local level decision making about climate change requires converting data into information. The aim was to develop software to analyze thousands of meteorological data in seconds. We first designed the database, selected the statistical tests used in time series analyses, and chose agroclimatic indexes, before designing the algorithms. Finally, we developed the software having in mind its easiness of use, and its efficiency for processing data and interpreting results. The software was tested several times by potential users, which allowed improvements in the development and design of the software. Clic-MD includes a set of routines for calculating derived variables most used in land evaluation such as agroclimatic indices, probability of rainfall by month, and others. Clic-MD allows for identifying climate change trends (climate anomalies and extreme events).