Prioritizing Tanzania’s agricultural development policy to build smallholder climate resilience. Final report for the Bill & Melinda Gates Foundation Grand Challenges Explorations 22: Risk-explicit and Evidence-based Policy Prioritization (REAP)

Faced with myriad options, Sub-Saharan Africa policy makers struggle to prioritize actions. Commonly used modeling approaches perform poorly in data scare conditions or focus intently on tools at hand. Policies, by consequence, report ‘wish lists’, making them a challenge to implement given resource constraints. Here, we evaluate the potential of using an alternative approach, Bayesian Networks (BNs), to prioritize agricultural policy actions, specifically modeling seven ‘Investment Areas’ listed in Tanzania’s Agriculture Sector Development Programme II

A novel causal structure-based framework for comparing a basin-wide water–energy–food–ecology nexus applied to the data-limited Amu Darya and Syr Darya river basins

The previous comparative studies on watersheds were mostly based on the comparison of dispersive characteristics, which lacked systemicity and causality. We proposed a causal structure-based framework for basin comparison based on the Bayesian network (BN) and focus on the basin-scale water–energy–food–ecology (WEFE) nexus. We applied it to the Syr Darya River basin (SDB) and the Amu Darya River basin (ADB), of which poor water management caused the Aral Sea disaster. The causality of the nexus was effectively compared and universality of this framework was discussed. In terms of changes in the nexus, the sensitive factor for the water supplied to the Aral Sea changed from the agricultural development during the Soviet Union period to the disputes in the WEFE nexus after the disintegration. The water–energy contradiction of the SDB is more severe than that of the ADB, partly due to the higher upstream reservoir interception capacity. It further made management of the winter surplus water downstream of the SDB more controversial. Due to this, the water–food–ecology conflict between downstream countries may escalate and turn into a long-term chronic problem. Reducing water inflow to depressions and improving the planting structure prove beneficial to the Aral Sea ecology, and this effect of the SDB is more significant. The construction of reservoirs on the Panj River of the upstream ADB should be cautious to avoid an intense water–energy conflict such as the SDB's. It is also necessary to promote the water-saving drip irrigation and to strengthen the cooperation

Modelling approaches to food waste : discrete event simulation; machine learning; Bayesian networks; agent-based modelling; and mass balance estimation

The generation of food waste at both the supplier and the consumer levels stems from a complex set of interacting behaviours. Computational and mathematical models provide various methods to simulate, diagnose and predict different aspects within the complex system of food waste generation and prevention. This chapter outlines four different modelling approaches that have been used previously to investigate food waste: discrete event simulation, which has been used to examine how the shelf life of milk and many actions taken around shopping and use of milk within a household influence food waste; machine learning and Bayesian networks, which have been used to provide insight into the determinants of household food waste; agent-based modelling, which has been used to provide insight into how innovation can reduce retail food waste; and mass balance estimation, which has been used to model and estimate food waste from data related to human metabolism and calories consumed

Integrated and sustainable management of intensive broiler farming according to the environmental balance logic

With respect to meat production in Italy, poultry meat production is among the main ones with a production of 1.25 million tonnes, 68% of which is broiler meat (Avec, 2015). Most of the broiler meat come from standard indoor system farms and they are located in the North-East regions (Unaitalia, 2014), often concentrated in specific areas, that frequently leads to criticism due to emissions, in particular ammonia (NH3), nitrous oxide (N2O) and methane (CH4) produced and the difficulty to obtain a proper disposal of poultry manure. This is because the broiler farms in these areas are a lot and all are characterized by the absence of field where the poultry manure could be spread. The broiler standard indoor system is characterized by a standard production chain, which starts with the companies that produce the feed and closes with the companies that slaughter and prepare the finished product. However, the poultry chain has never given much importance to the co-product that inevitably forms, that is, the poultry manure. The poultry manure is a co-product, it has an excellent amounts of nitrogen and phosphorus (Chamblee and Todd, 2002). This situation leads to problems of the emissions of broiler farm and the correct management of the poultry manure and the consequent environmental impacts. For these reasons, the research follows three research lines: i) use mix of microorganisms (LW) in the broiler breeder phase (PM = poultry manure treatment, DW = drinking water treatment and CL = control or no treatments); ii) three utilization scenarios of poultry manure (direct field spread = DFS, production of organic fertilizers = POF and combustion plant = CP). The last two scenarios produce organic fertilizer, also (IFA, 2012); iii) application of a field simulation model and compare cultures with high (Hi) and low (Li) input, in particular respect nitrogen (N). The third line of research has been developed because, although not strictly related to the use of poultry manure, it concerns nitrogen (N) and its application to a crop. Since the poultry manure has a lot of nitrogen (N), it has been considered interesting to evaluate this element, considering the problems connected to it also and especially bound by the Nitrates Directive (91/676/CEE and DM 5046 of 25 February 2016). The first line, was evaluated using the methodology Life Cycle Assessment (LCA). The second with LCA and DeNitrification- DeComposition (DNDC) model approaches. Finally, the last with DNDC model. From the first line of research (i), it can be deduced that, except the greater environmental impact of feed that are 81% of CL, 79% of PM and DW, microorganism treatments have reduced emissions from broiler breeding farm and hence, environmental impacts. The environmental impacts of the two types of treatment (PM and DW) are compared to the CL both. The Terrestrial Acidification (TA) expressed as kg SO2 eq., in PM is less than 11.057% and in DW is 4.876%. In the Particular Matter Formation (PMF) expressed as kg PM10 eq., in PM is less than 9.076 and in DW is less than 2.727. In the Eutrophication Potential (EP) expressed as kg PO4 eq., in the PM is less than 5.212 and in DW is less than 0.101. On the other hand, there have not been significant results with a lower environmental impact as regards the Climate Change (CC) expressed as kg CO2 eq. Finally, with regard to housing emissions, especially with respect to NH3, Monte Carlo analysis showed a significant reduction in emissions between the different scenarios. In PM there were less emissions of 69% and 77% in DW, respectively compared to CL. Instead, from the second line of the research (ii), the environmental impacts of utilization scenarios of poultry manure (POF and CP) are both compared to the DFS. In Eutrophication (EP) expressed as kg PO4- eq., there is a lower environmental impact of 33% in the CP. Instead, it is higher of 16.2% in the POF, in agreement with other studies, also (González-García et al., 2014). Another important impact category to consider is the Acidification (AP) expressed as kg SO2 eq., that is higher in POF scenario of 2.5%, insteed it is less of 9.7% in CP. This becouse the N leach (nitrate), is 22.11, 20.17 and 16.43 kg N/ha/y in a time horizon of 100 years in production of POF, DFS and CP, respectivelly. The Photochemical Oxidation expressed as kg C2H4 eq., it is less of 5.2% in the POF and it is less of 28% in the CP. The Particular Matter Formation (PMF) expressed as PM10 eq., it is less of 18% in the CP. The Abiotic Depletion of Fossil Fuel (FD) expressed as MJ, it is less of 9.5% in the CP and insteed, it is higher of 5,4% in the POF. The Cumulative Energy Demand (CED) expressed as MJ, it is less of 8.1% in the POF and it is less of 4.9% in the CP. Regarding FD, and especially for the CED, values of higher environmental impact for POF, it is due to the high energy request. Finally, from the thrid line of the research (iii), despite of its positive applications, the use of active light crop canopy remote sensors for in-season site-specific nitrogen (N) management, has some drawbacks. The development of algorithms to estimate in-season N rates is based on data that relates canopy spectral data to potential yield and N uptake over multiple years and locations. Furthermore, canopy sensing-based N rate algorithms use in-season estimation of canopy N status to prescribe N rate need to reach yield potential, but is does not account for crop streses between sensing and harvest. The goal of this third study was to develop and test a methodology for combining normalized difference vegetation index data (NDVI) and simulating the assess spatial variability of corn N stress and in-season N rate. Using two season data (2008-2009) of five corn fields located in the Venice lagoon watershed, spatial model calibration and simulation were conducted using the CERES – Maize model in DSSAT in conjunction with the GeoSpatial Simulaton (GeoSim) tool in the Quantum GIS software. The model was first optimized to properly predict the yield, and subsequently to match the simulated and the NDVI-derived leaf area index (LAI). Model accuracy in yield estimation was reached by soil parameters optimization and was not negatively influenced by model optimization for LAI. In order to evaluate the advantages of coupling modelling and spectral data, N stress was simulated and optimum rates able to minimize it were evaluated. The incorporation of proximal sensed-derived data into the model guaranteed to increase the accuracy of Nitrogen stress simulation, due to the relationship between NDVI, LAI and N stress. Manage an inseason site-specific fertilization aiming to minimize N stress could N efficiency not guarantee to satisfy other criteria, such as the maximum achievable yield, the economic convenience or the environmental impact of the fertilization

IoT Applications Computing

The evolution of emerging and innovative technologies based on Industry 4.0 concepts are transforming society and industry into a fully digitized and networked globe. Sensing, communications, and computing embedded with ambient intelligence are at the heart of the Internet of Things (IoT), the Industrial Internet of Things (IIoT), and Industry 4.0 technologies with expanding applications in manufacturing, transportation, health, building automation, agriculture, and the environment. It is expected that the emerging technology clusters of ambient intelligence computing will not only transform modern industry but also advance societal health and wellness, as well as and make the environment more sustainable. This book uses an interdisciplinary approach to explain the complex issue of scientific and technological innovations largely based on intelligent computing

Uncertainty Analysis of the Performance of a System of Best Management Practices for Achieving Phosphorus Load Reduction to Surface Waters

The repeated occurrence of Lake Erie’s harmful algal blooms suggests an inadequate phosphorus management system that results in excessive loads to the lake. In response, Canadian and United States’ governments have issued a new management objective, a 40% reduction in total and dissolved reactive phosphorus loads relative to 2008. To provide scientific evidence to guide managers toward achieving their management objective, we used the International Organization of Standardization (ISO) 31010 Bowtie Risk Analysis Tool to analyze the performance of the phosphorus management system. The effectiveness of agricultural best management practices (BMPs) and their adoption were combined into a Bayesian belief network model to predict watershed performance of each BMP. Then, the BMPs were analyzed for their probability of high risk phosphorus load reduction and achieving the management objective. Trade-offs were observed among the BMPs that will require decision makers to decide whether the management priority is to achieve the 40% load reduction objectives, or prevent further increase in the proportion of dissolved reactive phosphorus in the load, the identified culprit causing the repeated algal blooms

Using organic phosphorus to sustain pasture productivity: A perspective

Organic phosphorus (P) in grazed pastures/grasslands could sustain production systems that historically relied on inorganic P fertiliser. Interactions between inorganic P, plants and soils have been studied extensively. However, less is known about the transformation of organic P to inorganic orthophosphate. This paper investigates what is known about organic P in pasture/grassland soils used for agriculture, as well as the research needed to utilise organic P for sustainable plant production. Organic P comprises > 50% of total soil P in agricultural systems depending on location, soil type and land use. Organic P hydrolysis and release of orthophosphate by phosphatase enzymatic activity is affected by a range of factors including: (a) the chemical nature of the organic P and its ability to interact with the soil matrix; (b) microorganisms that facilitate mineralisation; (c) soil mineralogy; (d) soil water electrolytes; and (e) soil physicochemical properties. Current biogeochemical knowledge of organic P processing in soil limits our ability to develop management strategies that promote the use of organic P in plant production. Information is particularly needed on the types and sources of organic P in grassland systems and the factors affecting the activity of enzymes that mineralise organic P. Integrated approaches analysing the soil matrix, soil water and soil biology are suggested to address this knowledge gap