Abstract An extraordinary moment of agricultural modernization is currently underway due to innovations in Information and Communication Technologies (ICTs). In this process, management precepts are renewed, fostering significant gains in efficiency, productivity, and sustainable use of natural resources and the environment. The growing supply of ICTs and the extension of connectivity in rural areas, with their transforming roles in productive practices and economic relations, bring about questions regarding their impacts. These technologies include precision positioning systems and large volume databases, electronic sensors of site-specific production and environmental conditions, repositories of relational data, statistical and crop forecasting software, methodologies and processes; web-based information services, among others. The assessment of impacts focused on ICTs for agriculture needs innovative approaches, due to the peculiarities of their applications, the different scales of their socioenvironmental scopes and, at the same time, the verification of effectiveness of institutional investments on research, development, and innovation (RD&I). Based on these premises, the objectives of this work are to present a ‘module of impact indicators for Information and Communication Technologies (Ambitec-TICs)’, and assess its application to six typical technology adoption cases resulting from agricultural RD&I projects. The results detail critical analyses of the contributions of the proposed module for the registration, interpretation, and communication of impacts, with recommendations for technology transfer and accountability in institutional Social Balance documentation.

A restauração da Mata Atlântica vem recebendo aumento na atenção por causa do papel na conservação da biodiversidade e sequestro de carbono para a mitigação da mudança global do clima. Este estudo foi conduzido em um projeto de restauração da Mata Atlântica na região centro-sul do Estado de São Paulo - Brasil e buscou desenvolver equações alométricas para estimar a biomassa arbórea de espécies nativas em plantios mistos. Em 60 árvores de 19 espécies foram medidas a biomassa acima e abaixo do solo, o diâmetro do tronco (DAP: diâmetro a 1,3 m de altura), altura (H) e densidade específica da madeira (Ds). Diferentes equações de biomassa (linear e não linear) foram ajustadas para estimar a biomassa acima e abaixo do solo, utilizando DAP, H e Ds como variáveis preditoras. As equações de biomassa lineares foram as menos acuradas para a estimativa da biomassa acima e abaixo do solo. As equações de biomassa não lineares que usaram log DAP2, log H e log Ds como variáveis preditoras foram mais acuradas para a estimativa da biomassa acima do solo e as não lineares que usaram log DBH2*WD como variáveis preditoras foram as mais acuradas para a estimativa da biomassa abaixo do solo. Conclui-se que estas equações ajustadas podem ser usadas na estimativa da biomassa acima e abaixo do solo nas áreas do projeto estudado. Também pode ser considerado o uso destas equações ajustadas em outras áreas restauradas da Mata Atlântica com idade do povoamento, classes de tamanho das árvores, composição de espécies e características das áreas similares ao deste estudo, desde que árvores sejam avaliadas para verificar a eficácia da equação a ser usada.

Restoration of Atlantic Forests is receiving increasing attention because of its role in both biodiversity conservation and carbon sequestration for global climate change mitigation. This study was carried out in an Atlantic Forest restoration project in the south-central region of São Paulo State - Brazil to develop allometric equations to estimate tree biomass of indigenous tree species in mixed plantations. Above and below-ground biomass (AGB and BGB, respectively), stem diameter (DBH: diameter at 1.3 m height), tree height (H: total height) and specific wood density (WD) were measured for 60 trees of 19 species. Different biomass equations (linear and nonlinear-transformed) were adjusted to estimate AGB and BGB as a function of DBH, H and WD. For estimating AGB and BGB, the linear biomass equation models were the least accurate. The transformed nonlinear biomass equation that used log DBH2, log H and log WD as predictor variables were the most accurate for AGB and the transformed nonlinear biomass equations that used log DBH2*WD as predictor variables were the most accurate for BGB. It is concluded that these adjusted equations can be used to estimate the AGB and BGB in areas of the studied project. The adjusted equations can be recommended for use elsewhere in the region for forest stands of similar age, tree size ranges, species composition and site characteristics.