Smallholder Adaptive Farming and Biodiversity Network (SAFBIN)

SAFBIN is an action research programme from Caritas Organisations to address the issues of climate change and food security of smallholder farmers in South Asia. The programme aiming to achieve SDG 2, is inspired by the achievements and mutual learning process of the Caritas Partners in a successful previous phase of regional programme under the European Union Global Programme on Agriculture Research for Development (ARD).

SAFBIN is a multi-dimensional and multi-sector programme aimed to address the agricultural development challenges of developing and emerging countries. The innovative models piloted by the smallholder farmers from five rainfed Agro-Ecosystems (AES) in South Asia will be scalable and replicable in all similar Agro-Ecosystems. This programme will primarily contribute in achieving Sustainable Development Goal 2 of United Nations: “End hunger, achieve food security and improved nutrition and promote sustainable agriculture in South Asia”.

The overall programme will benefit about 40000 people living in 165 villages of 21 districts in Bangladesh, India, Nepal and Pakistan. The first phase of the programme will be implemented from April, 2018 in 95 villages of 11 districts, benefiting about 22000 people.

SAFBIN programme follows farmer led collective on-farm adaptive research, farming system and partnership approaches to empower the smallholder farmers in:

Caritas organisations part of this initiative are the official national organisations of the Catholic Bishops' Conference for social development in their respective countries. They are also members of Caritas Internationalis in Rome, which is a global confederation of 165 Catholic organisations working in humanitarian emergencies and international development. Implementing partners in South Asia are the members of Caritas Asia, which is also a strategic partner of this programme.

Caritas India, Caritas Bangladesh, Caritas Nepal and Caritas Pakistan will be implementing this programme in South Asia with the support of Caritas Austria and Caritas Switzerland. They will collaborate and partner with global and national research institutions, national agricultural research system and universities to implement this programme.


  • Simulation on various agronomic management and climate change scenarios for increasing productivity of spring maize genotypes with staggered planting under rain-fed upland situation in Central Terai using DSSAT ver 4.5 crop.

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  • 01/04/2016

This MSc thesis study was conducted by Umesh Shrestha, student at the Tribhuwan University, as part of the SAF-BIN project in Nepal in 2014.


Spring maize is a dominant crop in the upland terrain of Nepal. However, changing weather conditions experienced in the growing season negatively influence the productivity. Crop simulation models help to investigate the impact of varying weather conditions on crop production. The aim of the study was to assess the productivity and best agronomic management options for different maize genotypes, under changing climate scenarios using Cropping System Model CSM-CERES in DSSAT (ver. 4.5)[1]

A field experiment and a simulation modeling study were undertaken in Shivamandir-2, Nawalparasi District, during late spring season (April-August) 2013. Twelve combined treatments, consisting of four maize cultivar [2], planted at three different sowing dates[3] were carried out in a factorial randomized complete block design. Analysis of variance (ANOVA) was tested via MSTAT-C. Measured parameters of the field experiment included biometric observation, yield factors and records to estimate agro-climatic indices. To evaluate the CSM-CERES model, field data on soil, weather and cultivars, for simulating the climate change impact were compiled, followed by model calibration, validation and sensitivity analysis.

Cultivar and sowing date significantly influenced the productivity of the crop in the field experiment. The hybrid RML- 4/17 achieved highest values for most yield parameters, including kernel rows ear-1 (13.77), 1000 grain weight (245 g) and grain yield (6.03 t ha-1).  Early planting date (7th April) resulted in highest yield factors, which were decreasing with later dates. The CSM-CERES model was found well validated e.g. with days to physiological maturity (RMSE [4] = 0.674 day, D-index: 0.999) and grain yield (RMSE = 54.94 kg ha-1, D-index: 1.00). The model was found sensitive to sowing dates, no water stress condition, cultivars, weather years and climate change parameter. Simulating no water stress condition, resulted in higher yields (up to 18%). On the contrary, simulating rising temperature (+2°C) and decreasing solar radiation and carbon-dioxide concentration decreased spring maize yield by 15% and this effect was highest in hybrid cultivars. The results of the simulation modeling are promising for farmers and decision markers, as they could have access to accurate yield forecast. Quality data sets, as well as repetitive field work should be generated to improve the accuracy of the model.

[1] Cropping System Model (CSM)-CERES (Crop-Environment Resource Synthesis)

[2] Cultivars: Short durational: Arun-2, local (Tharu makai), long durational:  Poshilo makai-1 (QPM), RML-4/17

[3] Sowing dates: 7th April, 22nd April , 7th May

[4] RMESE= root mean square error