Using information and communication technologies to disseminate and exchange agriculture-related climate information in the Indo-Gangetic Plains

This report documents and analyses emerging trends in the delivery and exchange of climate information in institutionalized agricultural extension systems, as well as through information and communication technologies for development (ICT4D) efforts that have a rural–agricultural focus. Such an analysis aims to give a clearer indication of how to best direct potential future investments in sharing climate change information with noninstitutional stakeholders. The analysis covers four countries across the Indo-Gangetic Plains (IGP): Bangladesh, India (Punjab, Haryana, Uttarakhand, Uttar Pradesh, Bihar and West Bengal States), Nepal (Terai Region), and Pakistan (Punjab Province). The critical potential impacts of climate change across the IGP include drought, flooding, glacial lake outburst floods, and variability of river runoff and coastal salinity

Entry Points to Improve Livestock Water Productivity in Selected Forage Based Livestock Systems

Agricultural production is challenged by increasing water scarcity and simultaneously growing demands for food and feed. Globally livestock feed sourcing is seen as one of the major causes for water depletion, and therefore increasing livestock water productivity (LWP) is necessary. Feed sources in Forage Based Livestock Production Systems [FLPS (grazing, mixed-irrigated and mixed-rainfed)] largely consist of pasture, crop residue, or immature cereal crops, and also plants cut for fodder and carried to the animals. In drylands (arid and semi-arid) eco-regions, FLPS are generally extensive and thus the scale of water depletion for feed production is a major concern. This paper synthesizes LWP-knowledge generated across different FLPS over time and systematically identifies entry points to enhance productive uses of fresh water resources. It draws on examples of grazing systems in Uganda (Nile basin), mixed-rainfed systems in Ethiopia (Nile basin), mixed-irrigated systems in Sudan (Nile basin), and mixed-irrigated systems in India (Indio-Gangana basin). Although these systems vary by their degree of intensification, scale of water related problems, and therefore in their values of LWP, a number of common entry points to increase LWP can be identified. Based on empirical evidence from these systems, we systematically clustered these entry points as: (1) improving the water productivity of feed; (2) improving livestock feed sourcing and feeding; (3) enhancing livestock feed use efficiencies; and (4) enabling institutions and market linkages to facilitate adoption of relevant technologies. The paper concludes by discussing a comprehensive framework for entry points to improve water productivity in FLPS

Methodological approach for predicting and mapping the phenological adaptation of tropical maize (Zea mays L.) using multi‑environment trials

Open Access Journal; Published online: 7 Dec 2018Background The phenological development of the maize crop from emergence through flowering to maturity, usually expressed as a rate (i.e. 1/duration), is largely controlled by temperature in the tropics. Maize plant phenological responses vary between varieties and quantifying these responses can help in predicting the timing and duration of critical periods for crop growth that affect the quality and quantity of seed. We used routine multi-environment trials data of diverse tropical maize varieties to: (1) fit 82 temperature dependent phenology models and select the best model for an individual variety, (2) develop a spatial framework that uses the phenology model to predict at landscape level the length of the vegetative and reproductive phases of diverse varieties of maize in different agro-ecologies. Multi-environment trial data of 22 maize varieties from 16 trials in Kenya, Ethiopia, and Sudan was analyzed and the Levenberg–Marquardt algorithm combined with statistical criteria was applied to determine the best temperature-dependent model. Results The Briere model, which is not often used in plant phenology, provided the best fit, with observed and predicted days to flowering showing good agreement. Linking the model with temperature and scaling out through mapping gave the duration from emergence to maturity of different maize varieties in areas where maize could potentially be grown. Conclusion The methodology and framework used in the study provides an opportunity to develop tools that enhance farmers’ ability to predict stages of maize development for efficient crop management decisions and assessment of climate change impacts. This methodology could contribute to increase maize production if used to identify varieties with desired maturity for a specific agro-ecology in in the targeted regions

Options for calibrating ceres-maize genotype specific parameters under data-scarce environments

Open Access JournalMost crop simulation models require the use of Genotype Specific Parameters (GSPs) which provide the Genotype component of G×E×M interactions. Estimation of GSPs is the most difficult aspect of most modelling exercises because it requires expensive and time-consuming field experiments. GSPs could also be estimated using multi-year and multi locational data from breeder evaluation experiments. This research was set up with the following objectives: i) to determine GSPs of 10 newly released maize varieties for the Nigerian Savannas using data from both calibration experiments and by using existing data from breeder varietal evaluation trials; ii) to compare the accuracy of the GSPs generated using experimental and breeder data; and iii) to evaluate CERES-Maize model to simulate grain and tissue nitrogen contents. For experimental evaluation, 8 different experiments were conducted during the rainy and dry seasons of 2016 across the Nigerian Savanna. Breeder evaluation data were also collected for 2 years and 7 locations. The calibrated GSPs were evaluated using data from a 4-year experiment conducted under varying nitrogen rates (0, 60 and 120kg N ha-1). For the model calibration using experimental data, calculated model efficiency (EF) values ranged between 0.88–0.94 and coefficient of determination (d-index) between 0.93–0.98. Calibration of time-series data produced nRMSE below 7% while all prediction deviations were below 10% of the mean. For breeder experiments, EF (0.58–0.88) and d-index (0.56–0.86) ranges were lower. Prediction deviations were below 17% of the means for all measured variables. Model evaluation using both experimental and breeder trials resulted in good agreement (low RMSE, high EF and d-index values) between observed and simulated grain yields, and tissue and grain nitrogen contents. It is concluded that higher calibration accuracy of CERES-Maize model is achieved from detailed experiments. If unavailable, data from breeder experimental trials collected from many locations and planting dates can be used with lower but acceptable accuracy

Can a combination of UAV-derived vegetation indices with biophysical variables improve yield variability assessment in smallholder farms?

Open Access Journal; Published online: 09 Dec 2020The rapid assessment of maize yields in a smallholder farming system is important for understanding its spatial and temporal variability and for timely agronomic decision-support. We assessed the predictability of maize grain yield using unmanned aerial/air vehicle (UAV)-derived vegetation indices (VI) with (out) biophysical variables on smallholder farms. High-resolution imageries were acquired with UAV-borne multispectral sensor at four and eight weeks after sowing (WAS) on 31 farmer managed fields (FMFs) and 12 nearby nutrient omission trials (NOTs) sown with two genotypes (hybrid and open-pollinated maize) across five locations within the core maize region of Nigeria. Acquired multispectral imageries were post-processed into three VIs, normalized difference VI (NDVI), normalized difference red-edge (NDRE), and green-normalized difference VI (GNDVI) while plant height (Ht) and percent canopy cover (CC) were measured within georeferenced plot locations. Result shows that the nutrient status had a significant effect on the grain yield (and variability) in NOTs, with a maximum grain yield of 9.3 t/ha, compared to 5.4 t/ha in FMFs. Generally, there was no relationship between UAV-derived VIs and grain yield at 4WAS (r 0.1), but significant correlations were observed at 8WAS (r ≤ 0.3; p < 0.001). Ht was positively correlated with grain yield at 4WAS (r = 0.5, R2 = 0.25, p < 0.001) and more strongly at 8WAS (r = 0.7, R2 = 0.55, p < 0.001), while the relationship between CC and yield was only significant at 8WAS. By accounting for within- and between-field variations in NOTs and FMFs (separately), predictability of grain yield from UAV-derived VIs was generally low (R2 ≤ 0.24); however, the inclusion of ground-measured biophysical variable (mainly Ht) improved the explained yield variability (R2 ≥ 0.62, Root Mean Square Error of Prediction, RMSEP ≤ 0.35) in NOTs but not in FMFs. We conclude that yield prediction with UAV-acquired imageries (before harvest) is more reliable under controlled experimental conditions (NOTs), compared to actual farmer managed fields where various confounding agronomic factors can amplify noise-signal ratio

Entry Points to Improve Livestock Water Productivity in Selected Forage Based Livestock Systems

Abstract Agricultural production is challenged by increasing water scarcity and simultaneously growing demands for food and feed. Globally livestock feed sourcing is seen as one of the major causes for water depletion, and therefore increasing livestock water productivity (LWP) is necessary. Feed sources in Forage Based Livestock Production Systems [FLPS (grazing, mixed-irrigated and mixed-rain-fed)] largely consist of pasture, crop residue, or immature cereal crops, and also plants cut for fodder and carried to the animals. In drylands (arid and semi-arid) eco-regions, FLPS are generally extensive and thus the scale of water depletion for feed production is a major concern. This paper synthesizes LWP-knowledge generated across different FLPS over time and systematically identifies entry points to enhance productive uses of fresh water resources. It draws on examples of grazing systems in Uganda (Nile basin), mixed-rainfed systems in Ethiopia (Nile basin), mixed-irrigated systems in Sudan (Nile basin), and mixed-irrigated systems in India (Indio-Gangetic basin). Although these systems vary by their degree of intensification, scale of water related problems, and therefore in their values of LWP, a number of common entry points to increase LWP can be identified. Based on empirical evidence from these systems, we systematically clustered these entry points as: i) improving the water productivity of feed; ii) improving livestock feed sourcing and feeding; iii) enhancing livestock feed use efficiencies; iv) enabling institutions and market linkages to facilitate adoption of relevant technologies. The paper concludes by discussing a comprehensive framework for entry points to improve water productivity in FLPS